Core-log integration

Core-Log Integration

Geological Society Special Publications Series Editors." A. J. FLEET A. C. MORTON A. M. ROBERTS

GEOLOGICAL SOCIETY SPECIAL PUBLICATION NO. 136

Core-Log Integration EDITED BY

P. K. H A R V E Y & M. A. L O V E L L University of Leicester, UK

1998 Published by The Geological Society London

THE GEOLOGICAL SOCIETY The Society was founded in 1807 as The Geological Society of London and is the oldest geological society in the world. It received its Royal Charter in 1825 for the purpose of 'investigating the mineral structure of the Earth'. The Society is Britain's national society for geology with a membership of around 8500. It has countrywide coverage and approximately 1500 members reside overseas. The Society is responsible for all aspects of the geological sciences including professional matters. The Society has its own publishing house, which produces the Society's international journals, books and maps, and which acts as the European distributor for publications of the American Association of Petroleum Geologists, SEPM and the Geological Society of America. Fellowship is open to those holding a recognized honours degree in geology or cognate subject and who have at least two years' relevant postgraduate experience, or who have not less than six years' relevant experience in geology or a cognate subject. A Fellow who has not less than five years' relevant postgraduate experience in the practice of geology may apply for validation and, subject to approval, may be able to use the designatory letters C Geol (Chartered Geologist). Further information about the Society is available from the Membership Manager, The Geological Society, Burlington House, Piccadilly, London WIV 0JU, UK. The Society is a Registered Charity, No. 210161.

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Contents Preface

vii

Measurement, sealing and calibration BRISTOW, C. S. & WILLIAMSON,B. J. Spectral gamma ray logs: core to log calibration, facies analysis and correlation problems in the Southern North Sea

CORBETT,P. W. M., JENSEN,J. L. & SORBIE, K. S. A review of up-scaling and cross-scaling issues in core and log data interpretation and prediction DUNCAN,A. R., DEAN,G. & COLLIE,D. A. L. Quantitative density measurements from X-ray radiometry 17

HARVEY,P. K., BREWER,T. S., LOVELL,M. A. & KERR,S. A. The estimation of modal mineralogy: a problem of accuracy in core-log calibration

25

LOVELL,M. A., HARVEY,P. K., JACKSON,P. D., BREWER,T. S. WILLIAMSON,G. & WILLIAMS,C. G. Interpretation of core and log data-integration or calibration?

39

RAMSEY,M. H., WATKINS,P. J. & SAMS,M. S. Estimation of measurement uncertainty for in situ borehole determinations using a geochemical logging tool

53

Physical and chemical properties

AHMADI,Z. M. & COE, A. L. Methods for simulating natural gamma ray and density wireline logs from measurements on outcrop exposures and samples: examples from the Upper Jurassic, England

65

HERRON,M. M. & HERRON,S. L. Quantitative lithology: open and cased hole application derived from integrated core chemistry and mineralogy database

81

KINGDON, A., ROGERS, S. F., EVANS, C. J. (~¢ BRERETON, N. R. The comparison of core and geophysical log measurements obtained in the Nirex investigation of the Sellafield region

97

LAUER-LEREDDE,C., PEZARD,P. A., TOURON,F. & DEKEYSER,I. Forward modelling of the physical properties of oceanic sediments: constraints from core and logs, with palaeoclimatic implications

115

WADGE,G., BENAOUDA,D., FERRIER,G., WHITMARSH,R. B., ROTHWELL, R. G. & MACLEOD, C. Lithological classification within ODP holes using neural networks trained from integrated core-log data

129

Petrophysical relationships BASTOS, A. C., DILLON, L. D., VASQUEZ,G. F. & SOARES,J. A. Core-derived acoustic, porosity & permeability correlations for computation pseudo-logs

14I

DENICOL, P. S. & JING, X. D. Effects of water salinity, saturation and clay content on the complex resistivity of sandstone samples

147

SAMWORTH, J. R. Complementary functions reveal data hidden in your logs

159

SHAKEEL, A. & KING, M. S. Acoustic wave anisotropy in sandstones with systems of aligned cracks

173

vi

CONTENTS

WIDARSONO,B., MARSDEN,J. R. & KING, M. S. In situ stress prediction using differential strain analysis and ultrasonic shear-wave splitting

185

WORDEN, R. H. Dolomite cement distribution in a sandstone from core and wireline data: the Triassic fluvial Chaunoy Formation, Paris Basin

197

WORTHINGTON,P. F. Conjunctive interpretation of core and log data through association of the effective and total porosity models

213

Xu, S. & WHITE, R. Permeability prediction in anisotropic shaly formations

225

Integration of core and borehole images GOODALL,T. M., Me~LLER,N. K. & RONNINGSLAND,T. M. The integration of electrical image logs with core data for improved sedimentologicaI interpretation

237

HALLER,D. & PORTURAS,F. How to characterize fractures in reservoirs using borehole and core images: case studies

249

JACKSON,P. D., HARVEY,P. K., LOVELL,M. A., GUNN, D. A., WILLIAMS,C. G. & FLINT, R. C. Measurement scale and formation heterogeneity: effects on the integration of resistivity data

261

LOFTS, J. C. & BRISTOW,J. F. Aspects of core-log integration: an approach using high resolution images

273

MAJOR, C. O., PIRMEZ, C., GOLDBERG, D. & LEG 166 SCIENTIFICPARTY High-resolution core-log integration techniques: examples from the Ocean Drilling Program

285

Applications and case studies AYADI M., PEZARD, P. A., LAVERNE, C. & BRONNER, G. Multi-scalar structure at DSDP/ODP Site 504, Costa Rica Rift, I: stratigraphy of eruptive products and accretion processes

297

AYADI, M., PEZARD, P. A., BRONNER, G., TARTAROTTI, P. & LAVERNE, C. Multi-scalar structure at DSDP/ODP Site 504, Costa Rica Rift, III: faulting and fluid circulation. Constraints from integration of FMS images, geophysical logs and core data

311

BARCLAY,S. A. & WORDEN, R. H. Quartz cement volumes across oil-water contacts in oil fields from petrography and wireline logs: preliminary results from the Magnus Field, Northern North Sea

327

BREWER,T. S., HARVEY,P. K., LOVELL,M. A., HAGGAS,S. WILLIAMSON,G. & PEZARD, P. A. Ocean floor volcanism: constraints from the integration of core and downhole logging measurements

341

BOCKER, C. J., DELIUS, H., WOHLENBERG,J. • LEG 163 SHIPBOARDSCIENTIFICPARTY. Physical signature of basaltic volcanics drilled on the northeast Atlantic volcanic rifted margins

363

GONq:ALVES,C. A. & EWERT, L. Development of the Cote d'Ivoire-Ghana transform margin: evidence from the integration of core and wireline log data

375

TARTAROTTI, P., AYADI, M., PEZARD, P. A., LAVERNE, C. & DE LAROUZII~RE,F. D. Multi-scalar structure at DSDP/ODP Site 504, Costa Rica Rift, II: fracturing and alteration. An integrated study from core, downhole measurements and borehole wall images

391

Index

413

Preface Core and log measurements provide crucial information about subsurface formations. Their usage, either for integration or calibration, is complicated by the different measurement methods employed, different volumes of formation analysed, and in turn, the heterogeneity of the formations. While the problems of comparing core and log data are only too well known, the way in which these data can be most efficiently combined is not at all clear in most cases. In recent years there has been increased interest in this problem both in industry and academia, due in part to developments in technology which offer access to new types of information, and in the case of industry, pressure for improved reservoir models and hydrocarbon recovery. The application of new numerical methods for analysing and modelling core and log data, the availability of core scanning facilities, and novel core measurements in both two and three dimensions, currently provide a framework for the development of new and exciting approaches to core-log integration. This Special Publication addresses some of the problems of core-log integration encountered by scientists and engineers from both industry and academia. The diverse nature of the contributions in this volume are an expression of the value and need to understand core and log measurements, and the way in which they can be combined to maximum effect. Contributions range geologically from hydrocarbon-bearing sediments in the North Sea to the volcanic rocks that form the upper part of the oceanic crust. In order to constrain this diversity for presentation the volume has been divided into five sections and starts with 'Measurement, scaling and calibration', 6 papers concerned purely with aspects of core and,or log measurements themselves including cross-correlation, upscaling, measurement uncertainty and accuracy. Subsequent sections include (2) 'Physical and chemical p r o p e r t i e s ' - 5 papers, (3) 'Petrophysical relationships'-8 papers, (4) 'Integration of core and borehole i m a g e s ' - 5 papers and (5) 'Applications and case s t u d i e s ' - 7 papers. All papers were submitted in response to an open call for contributions so, within the constraints of work loads and other factors, may be considered to represent a fair snapshot of recent developments in Core-Log Integration. The volume arises from a meeting of the Borehole Research Group of the Geological Society and the London Petrophysical Society (London Chapter of the Society of Professional Well Log Analysts) held in London in September 1996. The editors are particularly grateful to Gail Williamson both for the organization of the meeting and for persistence in coaxing authors, reviewers, and editors; also to Jo Cooke at the Geological Society Publishing House for her continuous support in the production of this volume. We also wish to thank all those who undertook the often arduous job of reviewing the manuscripts, and without whose help this volume would have been that much poorer. Peter K. Harvey & Michael A. Lovell Leicester University

Spectral gamma ray logs: core to log calibration, facies analysis and correlation problems in the Southern North Sea C. S. B R I S T O W 1 & B. J. W I L L I A M S O N 2

1Research School of Geological and Geophysical Sciences, Birbeck College and UCL, Gower Street, London WC1E 6BT 2 Present address." Department of Mineralogy, The Natural History Museum, Cromwell Road, London S W7 5BD Abstract: The aim of this study is to test the usefulness of spectral gamma ray logs in subsurface correlation, lithofacies description and the interpretation of depositional environments of Namurian and Dinantian sandstones in the southern North Sea. Lithofacies and depositional environments were identified from core descriptions and compared with spectral gamma ray logs from thirteen boreholes. The results show that lithofacies and sedimentary environments can be discriminated within single wells. However, there is too much variation between wells to make an unequivocal assessment of depositional environment on the basis of spectral gamma ray logs alone. Comparison of stratigraphically correlated sandstones shows that variations between wells are often greater than variations between lithofacies. The differences between correlated sandstones using spectral gamma ray logs are largely attributed to changes in the logging environment, mainly mud characteristics, borehole quality and contractor. In addition, the occurrence of negative numbers for uranium and potassium in some wells indicates that the algorithm used to calculate elemental concentrations may be in error. For sandstones with a low total gamma ray response, small errors associated with tool calibration and data processing make a comparatively large difference to results, which has made detailed correlation of sandstones untenable. The most significant problem is the correction factor for potassium in KC1 drilling mud.

G a m m a ray logs are an essential tool for subsurface correlation and gamma ray log curve shapes or signatures are often used as the basis for interpreting ancient sedimentary environments (Selley 1978; Cant 1992). The spectral gamma ray tool measures radiation produced by the radioactive decay of naturally occurring radioactive elements. The most common naturally occurring radioactive elements in sedimentary rocks are potassium, thorium and uranium. As each of these elements decay they give off gamma radiation of a particular energy measured in MeV (millions of electron volts). The principle energies for each element are 1.46 MeV for potassium, 0.68MeV for thorium, and 1.12 and 0.98 MeV for uranium (Desbrandes 1985). The radiation from potassium (K 40) is a single energy while uranium and thorium have a series of isotopes producing radiation with a range of energies which overlap (Rider 1986). In addition, Compton scattering leads to a reduction in energy and the total gamma radiation is a complex spectrum. The spectral gamma ray tool samples the spectrum around specific energy levels, 1.46MeV for potassium, 1.76MeV for uranium and 2.62 MeV for thorium (Rider 1986;

Dresser Atlas 1992). These measured values are then recalculated to estimate the proportions of potassium, thorium and uranium, expressed as percentages or API units. Spectral gamma ray data recorded from outcrop have been used for correlation and to define sediment facies in Upper Carboniferous deltaic sediments (Myers & Bristow 1989; Davies & Elliot 1995). Spectral gamma ray data have also been used to characterize marine bands in the Upper Carboniferous (Archard & Trice 1990; Leeder et al. 1990). In this study we have attempted to apply the methodology of Myers & Bristow (1989) to Carboniferous rocks in the Southern North Sea. We have examined spectral gamma ray logs from thirteen wells in the Southern North Sea (Wells 1-13). Sedimentary logs of core were available for seven of the boreholes and stratigraphic information showed that two sandstone units 'A' and 'B' could be correlated between three and six of the wells, respectively. Unfortunately due to confidentiality agreements we are unable to identify the wells in question or the names of the correlated units. G a m m a ray logs are affected by hole conditions, in particular an oversized hole can lead to

BRISTOW,C. S. & WILLIAMSON,B. J. 1998. Spectral gamma ray logs: core to log calibration, facies analysis and correlation problems in the Southern North Sea In. HARVEY,P. K. • LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 1-7

2

C.S. BRISTOW & B. J. WILLIAMSON

a decrease in gamma ray response. To provide some control on data quality, gamma ray measurements were plotted against caliper data. Another common borehole effect is the use of KC1 in drilling mud. The potassium in the drilling mud produces an increase in absolute values of potassium on the spectral gamma ray log. This is supposed to be corrected in the processing and we have assumed that the contractors have made the right corrections to the data. However, there appears to have been no correction for variations in mud chemistry down hole. Of the thirteen wells that we have examined, ten were logged by one contractor and the remaining three were logged by a second contractor (Table 1). Seven of the wells were drilled using an oil based mud, five were drilled using a water based KCI mud and one was drilled with a salt saturated polymer. Table 1. Summary of well characteristics

Well number

Logging contractor

Drilling mud

Correlated sandstone A

1

1

KC1

2 3 4 5 6 7 8 9

1 1 2 2 1 ! 1 1

Oil KC1 Oil Oil Oil KC1 Oil Oil

A

A B

10

1

KC1

B

11 12 13

2 1 1

polymer KC1 Oil

B B B

Methods

A simple seven category lithofacies scheme was adopted for the cored wells with classification on the basis of lithology and sedimentary structures: cross-stratified sandstones, silty sandstones, s a n d y siltstones, claystones, coal, limestones and rooted beds. After depth matching of core and log and corrections for core to log slip, depth intervals for each lithofacies were defined. Lithological boundaries were picked at the shoulder of gamma ray curves to take account of readings which 'smear' across bed boundaries. The gamma ray log data were then assigned a lithofacies classification on the basis of core descriptions. Where core log depths were metric and the wireline log data in feet, the data were recalculated to metric units. For several of the wells, potassium values were given in deci. % rather than as percentages. As deci. % units give

good separation of curves on log plots, all potassium values were converted to deci. %. Having reduced the log data to cored depth intervals, element data and element ratios were plotted for each well on cross plots and logs, and between wells on cross plots and box plots. Observations

Potassium-thorium cross plots discriminate lithofacies Cross plots of potassium against thorium, thorium against uranium and potassium against uranium were produced for each cored well. The cross plots provide an easy to read display of the range of measurements from each well by facies and for comparing each facies between wells. The cross plot of potassium against thorium from Well 1 (Fig. 1) is a typical example; It shows limestones with very little gamma radiation clustered in the lower left-hand corner of the plot with cross-stratified sandstones in a field around 0.01 deci % potassium and 10ppm thorium. Finer grained lithologies, silty sandstones, sandy siltstones and claystones are less well discriminated but all show relatively high potassium and thorium. One intriguing feature of this plot (Fig. 1) is the negative values for potassium in the limestones. Negative values for potassium are very small, less that 0.005 deci %, and were only found in Well 1; however, negative values for uranium were found in wells 2, 6 and 7. The negative values indicate a problem with the algorithm used by the contractor to calculate elemental concentrations. Other wells such as Well 5 (Fig. 2) show clear discrimination between lithofacies although the absolute values are different from those in Well 1. Cross-stratified sandstones generally contain slightly less potassium and less thorium, most claystones have relatively high values of potassium but a few have almost no potassium. Changes within lithofacies for a particular well could be due to differences in the detrital composition and diagenetic history of the claystones. However, low values may also be encountered where the gamma ray response is averaged across a bed boundary. The resolution of a gamma ray tool is typically about 3 0 4 0 cm depending on the speed that the tool was run (Rider 1986) and where the sampling interval coincides with a bed boundary the measurement will not represent either lithology, but a mixture of two different lithologies. Another possible source of error is in the core to log calibration where reconstruction of a core may lead to small offsets in the core to log slip.

SPECTRAL GAMMA RAY LOG CORRELATION PROBLEMS

3

Fig. 1. Potassium and thorium cross plot for Well 1 showing good discrimination of lithofacies with limestones in the lower left corner and fine-grained claystone and siltstones in the top right.

Fig. 2. Potassium and thorium cross plot for Well 5 showing good discrimination of lithofacies. The lithofacies have similar relative values to those in Welt 1 (Fig. 1) although absolute values for each lithofacies are slightly different.

Comparison of correlated sandstones On Fig. 3, which shows cross plots of crossstratified sandstones from all seven cored wells, the data for individual wells form distinct clusters. The differences within wells is less than the differences between wells, which suggests some systematic changes between wells. Geologic factors such as a change in provenance or

diagenesis are unlikely to produce such a clear systematic difference. Other possible explanations are that the sandstones were deposited in different deltaic environments: mouth bar, distributary channel or shoreface; or that the sandstones are stratigraphically different and have different detrital sources or different diagenetic histories. One way of testing these hypotheses is to examine the character of correlated sandstones.

4

C.S. BRISTOW & B. J. WILLIAMSON

Fig. 3. Cross plots of cross-stratified sandstones between wells showing a loose grouping of all the data in the lower left hand corner of the cross plot. Measurements from individual wells tend to be tightly grouped and the difference between wells appears to be greater than the differences within a well.

Fig. 4. Cross plot of potassium against thorium for the correlated sandstone Unit A shows the same sandstone in three different wells plotting in slightly different areas, note the lack of overlap between wells with lower potassium values in Well 1 which was drilled with a KC1 mud. Unit A. This has been correlated stratigraphically between three wells. The cross plot of potassium against t h o r i u m (Fig. 4) shows the same sandstone in three different wells plotting in slightly different areas. There is almost no overlap between the three data sets and although the trends appear to be similar in each well, there is a clear difference in the absolute values. Some

variation between wells could be due to lateral facies changes, but these are unlikely to have produced the observed shift in absolute values. The similar shape of the trends combined with their differences in absolute values indicates a systematic change between wells which we attribute to changes in the borehole environment. The factors most likely to affect the logs are caving, the use of different drilling fluids, and

SPECTRAL GAMMA RAY LOG CORRELATION PROBLEMS

5

Fig. 5. Cross plot of potassium against thorium for Unit B showing a consistent trend in the data for Wells 9, 10, 12 and 13. Well 11 appears to lie off trend with significantly higher potassium and thorium content which can be attributed to an error in the correction factor for KCI in the drilling mud..

Unit B.

Fig. 6. Box plot of total gamma for sandstones and claystones. Claystones usually have higher total gamma than sandstones although there is some overlap in Wells 3 and 6. The lower than usual values in these claystones may be due to deposition in an interdistributary bay rather than a prodelta environment. variations in the procedures of different logging contractors. There is very little difference in caliper data between wells and no evidence for significant caving, which leaves two possible e x p l a n a t i o n s for the differences observed. Firstly, Well 1 was drilled with water based mud, while Wells 4 and 8 were drilled with an oil based mud. Secondly, Wells 1 and 8 were logged by a different contractor to Well 4. Reduced values for potassium in Well 1 are most likely to be due to an over-correction for potassium in the KC1 drilling mud.

The cross plot of potassium against thorium for Unit B (Fig. 5) shows a consistent trend in the data for Wells 9, 10, 12 and 13, although there is an offset between the wells largely due to differences in the amount of thorium. Well 11 has a flatter trend with significantly higher potassium and a wider range in thorium. Assuming that the original correlation is correct, is there any simple explanation for the difference? Wells 9 and 13 were drilled with an oil based mud, Wells 10 and 12 were drilled with a water based mud and Well 11 was drilled with a salt saturated polymer (221 ppmK). It would appear most likely that the correction factor for potassium in the mud has left a residual of enhanced potassium values. One might wonder why the other Wells (10 and 12), with water based mud and relatively high KC1 contents, lie on a trend with Wells 9 and 13? The answer may be that Wells 9, 10, 12 and 13 were all logged by a different contractor to Well 11. It would appear therefore that the choice of logging contractor can have a significant effect on results.

Box plots show differences between wells Box plots have been used for a comparison of total gamma ray values for cross-stratified sandstones and claystones between wells, using lithofacies defined from core. Each plot (including boxes and whiskers) shows the spread of observations about the median. The box repre-

6

C.S. BRISTOW & B. J. WILLIAMSON

Fig. 7. Cross plot of K/Th against K/U for three correlated sandstones (Unit A) shows lower potassium values and an exceptionally good correlation of thorium and uranium in Well I which are attributed to correction factors which have over-compensated for KC1 in the drilling mud.

sents 50% of measurements about the median, the whiskers extend to the minimum and maximum data values. Median values for cross-stratified sandstones are generally 50 API units or less, although they do vary between wells (Fig. 6). Total gamma ray response for sandstones is almost always less than the total gamma ray response for claystones, where the median value is close to 100 API units, although there is some overlap in Wells 3 and 6 where the claystones have lower total gamma ray response than the other claystones. There is no obvious reason for the lower total gamma ray response in these two wells. Well 3 was drilled with a water based mud, but so were Wells 1 and 7, while Well 6 was drilled with an oil based mud as were Wells 2, 4 and 5. Wells 3 and 6 are from broadly similar stratigraphic units but Wells 5 and 7 are from the same Group. One possible explanation is that the claystones in Wells 3 and 6 were deposited in slightly different environments. The core logs indicate a prodelta environment for claystones in Wells 1, 2, 4, and 7 and an interdistributary bay environment for claystones in Wells 3, 6 and 5. Re-examination of the core logs indicates that the claystones in Well 5, originally attributed to an interdistributary bay, are significantly thicker than other interdistributary bay deposits and could be re-interpreted as prodelta deposits. If this is the case, then the total gamma ray response is discriminating between sedimentary environments, not just between lithofacies.

Eliminating inter well differences using ratio plots Element ratio vs element ratio plots were generated to eliminate the systematic variations in gamma ray tool response between wells (usually due to varying well conditions) which may have been inadequately compensated for in logging company calibration procedures. The plot of K/Th ratio against K/U ratio for Unit A (Fig. 7) shows that measurements from Wells 4 and 8 overlap while measurements from Well 1 are clearly lying on a different trend. Wells 4 and 8 were both drilled with an oil based mud while Well 1 was drilled with a water based mud containing KC1. The K/Th cross plot (Fig. 4) shows low potassium values for Well 1, and the ratio plot (Fig. 7) shows an offset due to low potassium values. In addition, Fig. 7 shows an exceptionally good correlation between thorium and uranium. We suspect that the correction factor applied to compensate for KC1 mud in Well 1, has over-compensated for potassium and also affected the measurements of thorium and uranium.

Conclusions Lithofacies for Carboniferous deltaic sequences from the Southern N o r t h Sea have been identified from core descriptions and compared with spectral gamma ray logs. The results show

SPECTRAL GAMMA RAY LOG CORRELATION PROBLEMS that lithofacies can be discriminated within single wells. However, comparison of correlated sandstones shows that variations between wells are greater than variations within wells. There is too much variation between wells to make an unequivocal assessment of lithofacies and depositional environment on the basis of spectral gamma ray logs alone. The differences between wells are attributed to changes in logging environment, mainly mud characteristics, borehole quality and different logging companies which have made detailed correlations impossible. For sandstones showing low total gamma ray response, small errors associated with calibrations and correction factors will make a comparatively large difference to results. In three wells, negative values for uranium were noted and in one well negative values for potassium were found which suggests a problem with the algorithm used to calculate elemental concentrations. Cross plots of correlated sandstones indicate that correction factors for KC1 in drilling muds are not always successful, and there appears to be a difference between the results achieved by different contractors in this respect. Corrections for KC1 appear to be based on a single value for each well although mud chemistry will almost certainly change down hole. More detailed tool calibration is required before subsurface correlations and facies analysis can be reliably made using spectral gamma ray response alone. The influence of downhole environment could be further tested by comparing the geochemical composition of core with gamma ray response. In the meantime avoid trying to read too much from spectral gamma ray response where KC1 mud is involved. The authors thank Mobil North Sea for funding this work and for permission to publish the results. The manuscript has been improved by the comments of J. S. Schweitzer and P. Corbett.

7

References ARCHARD, G. & TRICE, R. 1990. A preliminary investigation into the spectral radiation of the Upper Carboniferous marine bands and its stratigraphic application. Newsletters on Stratigraphy, 21, 167-173. CANT, D. J. 1992. Subsurface facies analysis. In: WALKER R. G. • JAMES, N. P. (eds)Facies Models, Geological Association of Canada, pp. 27-45. DAVIES, S. J. 8~ ELLIOT, T. 1995. Spectral gamma ray characterisation of high resolution sequence stratigraphy: examples from upper Carboniferous fluvio~leltaic systems, County Clare, Ireland. In: HOWELL, J. A. 8z AITKEN, J. F. (eds) High

Resolution Sequence Stratigraphy: Innovations and Applications. Geological Society Special Publications No. 104, pp. 25-35. DESBRANDES, R. 1985. Encyclopedia of well logging. Institut Francais du Petrole, Graham and Trotman Ltd, London. DRESSER ATLAS. 1982. Well logging and interpretation techniques (3rd edition). Dresser Industries Inc., USA. LEEDER, M. R., RAISWELL,R., AL-BIATTY,H., MCMAHON, A. & HARDMAN, M. 1990. Carboniferous stratigraphy, sedimentation and correlation of well 48/3-3 in the southern North Sea Basin: integrated use of palynology, natural gamma/ sonic logs and carbon/sulphur geochemistry. Journal of the Geological Society, London, 147, pp. 287-300. MYERS, K. J & BRISTOW, C. S. 1989. Detailed sedimentology and gamma ray log characteristics of a Namurian deltaic succession II: Gamma ray logging. In: WHATELEY,M. K. C. & PICKERING,K. T. (eds) Deltas." Sites and Traps for Fossil Fuels, Geological Society Special Publications No. 41, pp. 81-88. RIDER, M. H. 1986. The Geological Interpretation of Well Logs, Blackie Halsted Press, Glasgow. SELLEY, R. C. 1978. Concepts and methods of subsurface facies analysis. American Association of Petroleum Geologists, Continuing Education Short Notes 9.

A review of up-scaling and cross-scaling issues in core and log data interpretation and prediction P. W. M. C O R B E T T , J. L. J E N S E N 1 & K. S. S O R B I E

Department of Petroleum Engineering Heriot-Watt University, Edinburgh, EH14 4AS, UK 1 Present address." University o f Alaska at Fairbanks, Alaska Abstract: In a heterogeneous geological formation, each rock petrophysical property (e.g., permeability, porosity, and electrical conductivity) reflects the heterogeneity and varies in a manner related to the underlying changes in fabric (grainsize, mineralogy, lamination, wettability, etc.). However, measurements, both laboratory and downhole, are made at certain volume scales dictated by the size of the core plug used or the wireline log resolution. The comparison of core and log data needs to account for both the scale and physics of the particular measurements and how these relate to the underlying scale of the geological heterogeneity of the formation. In this review, these two fundamental issues are addressed as follows: (a) measurement scale and how it relates to the 'true' or 'required' petrophysical properties of the formation is defined as 'up-scaling'; (b) measurement physics and how we relate the physics of one measurement (e.g. permeability) to that of another (e.g. density, electrical, or acoustic properties) is termed 'cross-scaling'. We illustrate how these two issues arise in the comparison and prediction of permeability using several published studies. We also outline an approach to petrophysical measurement reconciliation termed 'genetic petrophysics'. This combines all three elements--measurement scale, measurement physics, and geology--to provide an integrated and robust model. We illustrate this approach for permeability to provide fit-for-purpose models of anisotropy in the near-well region of a reservoir. It has been appreciated for some time that there is a problem of scale in reservoir engineering (e.g. Warren et al. 1961; Haldorsen 1986). The volume of a reservoir under production greatly exceeds the volume of rock recovered from cores or investigated by wireline logs. There are many efforts underway to improve the modelling of reservoirs, which particularly address the extrapolation from the sparse core-log data to the interwell volumes. Computer flow models of reservoirs involve grid blocks that are by necessity large, relative to the investigation volumes of core or logs. Therefore engineers have to integrate the core and log data for use in simulation models in a process loosely referred to as 'up-scaling'. Permeability is a particular property of interest and several techniques have been developed for its up-scaling, e.g. power averaging, renormalization, and pseudo-isation. The aim of up-scaling is to estimate the 'effective' or equivalent properties at the chosen volume scale, e.g. grid blocks. The adjectives 'effective' and 'pseudo' are often used interchangeably in the petroleum literature to denote an up-scaled property, but there is a subtle difference. The former attempts to be intrinsic to the rock/fluid system and aims to be independent of boundary conditions,

including time. The latter, on the other hand, applies on some coarse grid as a replacement of a fine grid domain, but it may change radically if the boundary conditions are changed. It will emerge from our discussion that we are frequently talking about pseudo properties when we refer to core-log data integration. The petrophysical community have appreciated for some time that there are also scaleup problems in making comparisons between core and log data (e.g. Knutson et al. 1961). However, historical practice relied on the sampling of cores with plug-size measurements at one-foot spacing (Fig. l a). These were then compared directly with the log measurements, recorded at half-foot intervals. Shifts between core depths and log depths accounted for the offset (if present) between the core and log. Occasionally, a primitive up-scaling technique using a running average ( 1 : 2 : 1 weighting) was used for the plug data prior to comparing with the log data. Although the scale discrepancies were often appreciated, there was not much else that could be done. The development of high resolution petrophysical measurements in the laboratory (probe permeameter) and downhole (image logs) has presented new opportunities to address the scale

CORBETT,P. W. M. JENSEN, J. L. & SORBIE,K. S. 1998. A review of up-scaling and cross-scaling issues in core and log data interpretation and prediction In: HARVEY,P. K. • LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 9-16

10

P . W . M . CORBETT E T AL.

Fig. 1. A comparison between (a) the traditional core plug and logging tool scales of measurement and sample spacing and, (b) the new opportunities provided by closely spaced probe data and high resolution logging tools. Both schemes are shown schematically against a core interval with a missing section. In (b) there is more scope for identifying small scale heterogeneities and less sensitivity to missing core material. Depth matching is also improved.

issues between core and log measurements. These high resolution measurements image the geology far more effectively than the conventional, low resolution devices. Indeed, image logs were developed specifically to image the geology in the subsurface, potentially replacing the need for core data. With data at high sampling densities and small volumes of measurement, the comparison between logs and cores becomes more tractable (Fig. lb) and therefore gives us a feasible approach to corelog scaling. Since the laboratory probe permeameter measures a different physics (gas flow rate) to a subsurface image log (acoustic reflection or electrical conductivity) with different boundary conditions, there are also cross-scaling relationships (see below) that must be considered in addition to volume scale and sampling density effects. In this paper, we illustrate the cross-scaling and up-scaling of permeability between core and wireline logs for subsurface prediction of permeability. Larger scale dynamic data are used to justify the methods presented. Having reviewed the method, we discuss the implications for other properties and outline a new approach to petrophysics--genetic petrophysics--which is

Fig. 2. A comparison between (a) Up-scaling and (b) Cross-scaling. Numbers refer to approximate volumes in cubic metres. Refer to the text for definition of these terms. being developed. This approach is tied directly to the needs of reservoir modeUers and offers a way of integrating data and procedures from the original geological conceptual model, through the petrophysical data acquisition, the up-scaling/cross-scaling, and the construction of the numerical reservoir simulation model.

Definitions In this paper, we define the terms up-scaling and cross-scaling as follows (Fig. 2): Up-scaling: The determination of an effective (or pseudo) property at a scale larger than that of the original measurement. An example would be using the arithmetic average of a set of layer permeabilities as an estimator of the horizontal permeability of the composite layered media (Jensen et al. 1997, pp. 137139). Comparing probe to plug to well test permeabilities is an up-scaling problem (Corbett et al. 1996a). The issue of measurement scale for the same petrophysical property is the process of upscaling. Reservoir engineers are familiar with the up-scaling of permeability for reservoir simulation. Cross-scaling is a much less familiar concept and may be defined as follows: Cross-scaling: The determination of a relationship between two different physical prop-

A REVIEW OF UP-SCALING AND CROSS-SCALING

11

erties. Using regression to summarize the relationship between porosity and permeability for a suite of core plugs is a simple example. Comparing compressional wave transit time with porosity is a cross-scaling procedure. Cross-scaling provides the relationship--if there is one--between measurements of different petrophysical properties, at different measurement volume scales which are affected by the (different again) underlying volume scale of the geological heterogeneity. This clearly concerns the transfer of information on a certain required property via a more 'easy-to-measure' surrogate. The scales at which these transfers take place are critical to assessing the appropriateness--or inappropriateness--of the surrogate property. The definition of these terms helps us distinguish the impact of geology (largely up-scaling) from the physics (largely cross-scaling) in a more systematic fashion. These concepts are useful in the comparison of core and log data. In the next two sections, we look first at the cross-scaling of permeability and resistivity at compatible scales. These data are then up-scaled for comparison with larger scale dynamic data. Together these case studies show that cross-scaling and upscaling of permeability can be achieved in practice.

Fig. 3. Measurement of properties in the laboratory at similar volume scales with a resistivity probe (above) and permeability probe (below). Refer to Jackson et al. (1994) for more details.

Case studies We consider three examples of the cross-scaling between permeability and resistivity which have been carried out and which have been reported in the literature.

Fig. 4. Correlation between resistivity (shown a formation resistivy factor= measured resistivity/brine resistivity) against probe permeability for a slab of Lochabriggs Sandstone.

Laboratory study Jackson et al. (1994) measured permeability and resistivity with probe devices for an aeolian sample that was saturated with brine in the laboratory (Fig. 3). The resistivity probe was carefully designed to investigate a volume similar to that of a steady state probe permeameter and both volumes were comparable to the sample's scale of sedimentary variation. A strong relationship was observed (Fig. 4) and this can be related to the fundamental physical control.

As-Sarah study Ball et al. (1997) carried out a probe permeameter study on a fluvial sandstone. They found that averaged probe data (at 10cm spacing) correlated reasonably well with microresistivity

(Fig. 5a) and provided the basis of a permeability predictor which was a considerable improvement over methods based on the density log and core plugs (Fig. 5b).

Morecambe Bay study Thomas et al. (1996, 1997) undertook a detailed probe study over a fluvial interval for which resistivity images had also been acquired. They found a strong correlation between the probe permeability and microscanner resistivity (Fig. 6). In all of these cases, an empirical relationship existed and was reflected in the measurements at similar scales. Such relationships could reflect an underlying physical relationship, explained by an existing analytical model (e.g. Biot's and the C a r m a n - K o z e n y models) or might provide

12

P.W.M. CORBETT E T AL.

Fig. 5. Correlation between (a) probe permeability (averaged over a 30 cm window) and micro-spherically focussed log (MSFL) resistivity and (b) plug permeability and wireline density for an interval of PUC-B Reservoir. Refer to Ball et al. (1997) for more details. insight into the need for new petrophysical analysis.

Cross-scaling permeability and resistivity In the three studies just mentioned, the relationship is driven by the effects of pore geometry and porosity upon both the hydraulic and electrical conductivities. Several workers (e.g. Doyen 1988; Katz and Thompson 1987) have shown that both transport properties depend on a characteristic pore size in the rock. The form of that dependency differs for hydraulic and electrical conductivity, thus making the hydraulic--electrical relationship strength dependent also upon the level of rock heterogeneity. In a homogeneous sample, one characteristic length and its mutual effects upon both permeability and conductivity will give rise to a strong electrical-hydraulic relationship. Heterogeneity, however, will diminish the relationship strength because different portions of a sample will have differing characteristic sizes. This explains why data at the lamination scale (e.g. Jackson et al.

Fig. 6. Comparison between probe permeability, formation image and FMI resistance for an interval of Sherwood Sandstone. Refer to Thomas et al. 0996, 1997) for more details. 1994; Thomas et al. 1996) show very strong resistivity-permeability relations, while laminaset measurements (Ball et al. 1997) exhibit a weaker, though still useful, relationship. In all cases, the effects of geological variation were mitigated by chosing similar measurement volumes.

Up-scaling permeability In two of the cases presented above, the permeability was up-scaled for comparison with some larger scale dynamic data. In the As-Sarah study, the permeability predictor developed from the microresistivity was used to predict permeability in the uncored sections of several wells. With a continuous permeability log, the cumulative permeabilitythickness product, the transmissivity, was compared with a production log spinner survey. A good comparison was found supporting the appropriateness of the predictor (Fig. 7). This predictor continues to form the basis for permeability models in the field (von Winterfeld, pers. comm.).

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In the Morecambe Bay study, the up-scaling of both horizontal (kh) and vertical (kv) permeability were required for comparison with a borehole pressure measurement. The horizontal permeability was up-scaled by taking the arithmetic average of the probe data, the vertical permeability by taking the harmonic average (Thomas et al. 1996, 1997). The up-scaled

properties (effective kv/kh ratio) compared well with a larger-scale dynamic measurement (Fig. 8). The importance of the geology in the upscaling is well illustrated in two ways in this second study. Firstly, the abrupt decline in vertical permeability (i.e. increase in anisotropy) occurs at the bed length scale which, for these stacked fluvial channels, represents several feet. The image log picks out the geological features associated with bedding and this can be exploited to produce improved prediction of formation anisotropy. Secondly, there is an assumption, supported by geological analysis of similar beds in outcrop, that the layers or beds observed at the wellbore extend well beyond the volume of investigation of the dynamic measurement. This is in contrast to the anisotropy shown by plug scale measurements which is notably poor in estimating effective kv/kh at larger scales. Averaging plug scale kv/kh ratios is also an inappropriate up-scaling method for this parameter, which is very sensitive to scale changes (Corbett et al. 1996b, Cowan 7 Bradney 1997). The comparison of up-scaled permeability (probe, plug, or wireline) with the well test can provide additional corroboration of permeability predictors. For these larger scales, the effects of the organization of the geology (i.e. sedimentary structure) can also be important. This level of up-scaling is beyond the scope of this review (refer to Corbett et al., 1996a). Nonetheless, it is important to note that up-scaling from core to log must be tied with a consistent geological framework to the scales of well tests and full field numerical grid blocks.

14

P.W. M. CORBETT E T AL.

Plug permeability - density log cross-scaling revisited We can revisit the As-Sarah example to compare the probe-microresistivity method with the plugdensity method for permeability prediction 9 This will reveal the nature of improvements provided to the petrophysicist by the smaller scale measurements 9 It seems ironic that solutions to the up-scaling problem have been facilitated (i.e. they are more accurate, not necessarily faster) by having more 'smaller' scale petrophysical measurements. This irony, however, overlooks the role of the geology in the scaling process: smaller-scale measurements are often more easily interpreted in their geological context 9 The geology provides information regarding the volume and shape of each event, allowing analysts to make inferences about the validity and frequency of the value in the unsampled regions 9 If we examine the porosity-permeability relationship (Fig. 9) for the As-Sarah reservoir, we see that it is very weak. The lack of relationship is due to a number of factors-variable grain size and sorting in the fluvial sediments, patchy rhizocretionary cements, plug orientation with respect to heterogeneities, and others. Weak porosity-permeability relationships in fluvial reservoirs are often observed (Brayshaw et al. 1996). The cross-scaling relationship, in this case, is strongly obscured by the geological heterogeneity--a smaller or larger volume scale is suggested or separation of the grain size classes (Hogg et al. 1996). Any porosity-permeability relationship from these data will be associated with a high degree of uncertainty if used to predict permeability 9 On the (weak) assumption that porosity and permeability are related, the wireline density derived porosity might be used to predict permeability. The density log has a volume of investigation that is larger than the small scale (lamination) textural features that control permeability. In this example, it also proved very difficult to depth match the plug data with the log data, the probe data were more useful in this respect. The 'true' variation in permeability shown by the probe did not correlate well with the poor resolution of the density log. Any permeability predictor based on the latter will eliminate a scale of heterogeneity that may be important to the sweep efficiency of the reservoir. The up-scaling of permeability, if this method had been followed, would result in a more uniform reservoir permeability field, which may have been inappropriate for modelling oil recovery 9

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Genetic petrophysics The Morecambe Bay example shows the power (for prediction) of scale-compatible cross-scaling and geologically-assisted up-scaling. Fig. 8 shows that the effective property (in this case, k v / k h ) varies at certain geological length scales. There is a significant and abrupt change at the bed scale (4ft) and the bedset scale (12ft). Above the bedset scale, there appears to be less variability in the estimates and close agreement with the Modular Dynamic Tool (MDT) response. While the cost implications of MDT versus image log have to be considered, image log based predictors, calibrated by MDT measurements at carefully selected intervals, hold potential for improved anisotropy estimates in the future. Anisotropy in sediments is strongly affected by bedding, so it is only appropriate that a predictor based on a log that 'sees' the bedding will be better than estimates from small volume, plug measurements. The length scales (i.e. the geological architecture) provide important guidance for the petrophysicist--the length scales for combining or comparing appropriate measurements and also the length scales to be avoided for sampling intervals. Sampling close to the frequency of the data (volume or wavelength) is a notoriously poor procedure in geophysical measurements. Unfortunately, the 1-inch plug size and 1-foot sampling interval are close to the Nyquist frequency of lamina and beds!

A REVIEW OF UP-SCALING AND CROSS-SCALING Smaller scale measurements mean more data and more work in reducing, summarizing, and integrating the data. In that respect, their development is not welcome in the time- and personnel-challenged climate demanded by industry. However, the very fact that there are representative elements within reservoirs (e.g. stratal elements, genetic units, architectural elements) can also be exploited. The fundamental rock measurements can be targetted on these representative elements. The effective properties (or even pseudos) can then be determined--by simulation or measurement for these elements (Corbett et al. 1992; Ringrose et al. 1993; Pickup et al. 1995; Huang et al. 1995). The modelling of these elements--geobodies in G O C A D - - c a n then be accompanied by the appropriate petrophysics--genetic petrophysics. This method involves a more selective use of petrophysical measurement which is intended to be more costeffective. Indeed, a genetic petrophysics approach which explicitly recognizes and solves the cross-scaling problem may be the only successful route to true data integration.

Conclusions Cross-scaling between petrophysical properties is best achieved when the scales and density of measurements are comparable. Up-scaling of petrophysical properties benefits when the geological architecture is accounted for. Probe data and image logs can be jointly used to predict permeability (horizontal and vertical in the subsurface), demonstrating that consistent-volume cross-scaling and geologically-constrained up-scaling can be effective. The tools, understanding, and techniques are now available for the development of a more geologically-based petrophysics m e t h o d - - w h i c h we refer to as genetic petrophysics--that is fit for the purposes of reservoir modelling. It is understood that improved modelling prepares the way for improved oil recovery--the ultimate motivation behind this work. The authors acknowledge the support of Wintershall and British Gas in the studies discussed above. They also wish to acknowledge the support of EPSRC and industrial co-sponsors (Amerada Hess, Amoco, BHP Petroleum, British Gas, Chevron, Fina, Saga, Schlumberger, Shell, Statoil, Texaco) for continued work in this area under the PEGASUS project. The authors also acknowledge the contributions of the various authors of the case studies from which this overview has been drawn L. Ball, J. Lewis, S. Thomas, D. Bowen and M. Jackson. Their insights while working with the data have helped to formulate and illustrate these concepts.

15

References BALL, L. D., CORBETT,P. W. M., JENSEN,J. L. & LEWIS, J. M. 1997. The role of geology in the behavior and choice of permeability predictors. SPE Formation Evaluation, 12, 32-39. BRAYSHAW,A. C., DAVIES,G. W. • CORBETT,P. W. M. 1996. Depositional controls on primary permeability and porosity at the bedform scale in fluvial reservoir sandstones. In CARLING, P. A. & DAWSON, M. R. (eds) Advances influvial dynamics and stratigraphy, John Wiley & Sons, 373-394. CORBETT, P. W. M., RINGROSE,P. S., JENSEN, J. L. & SORBIE,K. S. 1992. Laminated clastic reservoirs-The interplay of capillary pressure and sedimentary architecture. SPE 24699. Proceedings of the 67th SPE Annual Technical Conference and Exhibition, October, Washington, 365-376. --, PINISETTI, M., TORO-RIVERA, M. & STEWART, G. 1996a. The comparison of plug and well test permeabilities, Dialog, 4-8. --, GOOD, T., JENSEN, J. L., LEWIS, J. J. M., PICKUP, G., RINGROSE, P. S. & SORBIE, K. S. 1996b. Reservoir description in the 1990s: A perspective from the flow simulation through layercake parasequence flow units. In: GLENNIE, K. & HURST, A. (eds), AD 1995: N W Europe's Hydrocarbon Industry, Geological Society, London, 169-178. COWAN, G. & BRADNEY,J. 1997. Regional diagenetic controls on reservoir properties in the Millom accumulation: implications for field development. In: MEADOWS,N. S., TRUEBLOOD,S. P., HARDMAN, M. & COWAN,G. (eds) Petroleum Geology of the Irish Sea and Adjacent Areas. Geological Society, London, Special Publications, 124, 373-386. DOYEN, P. M. 1988. Permeability, conductivity, and pore geometry of sandstone. Journal of Geophysical Research, 93, 7729-7740. HALDORSEN, H. H. 1986. Simulator parameter assignment and the problem of scale in reservoir engineering. In: LAKE, L. W. & CARROLL, H. B. (eds), Reservoir Characterisation, Academic Press, Orlando. HOGG, A. J. C., MITCHELL,A. W. & YOUNG, S. 1996. Predicting well productivity from grain size analysis and logging while drilling. Petroleum Geoscience, 2, 1-15. HUANG, Y. RINGROSE, P. S. & SORBIE, K. S. 1995. Capillary trapping mechanisms in water-wet laminated rocks. SPE Reservoir Engineering, 10, 287-292. JACKSON,M. A., BOWEN,D. G., JENSEN,J. L. & TODD, A. C. 1994. Resistivity and permeability mapping at the lamina scale. Proceedings of the International Symposium of the Society of Core Analysts, Stavanger, 12-14 Sept., paper SCA-9415, 163-172. JENSEN, J. L., LAKE, L. W., CORBETT, P. W. M. & GOGGIN, D. J. 1997. Statistics for Petroleum Engineers and Geoscientists, Prentice-Hall, New Jersey. KATZ, A. J. & THOMPSON, A. H. 1987. Prediction of rock electrical conductivity from mercury injec-

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tion measurements. Journal of Geophysical Research, 92, 599-607. KNUTSON, C. F., CONLEY, F. R., BOHOR, B. F. & TIMKO, D. J. 1961. Characterization of the San Miguel Sandstone by a coordinated logging and coring program. Journal of Petroleum Technology, 13, 425-432. PICKUP, G. E., RINGROSE, P. S., CORBETT, P. W. M., JENSEN, J. L. ~; SORBIE, K. S. 1995. Geology, g e o m e t r y , and effective flow. Petroleum Geoscience, 1, 37-42. RIN~ROSE, P. S., SORBIE, K. S., CORBETT, P. W. M. & JENSEN, J. L. 1993. Immiscible flow behaviour in laminated and cross-bedded sandstones. Journal of Petroleum Science and Engineering, 9, 103-124. THOMAS, S. D., CORBETT, P. W. M. & JENSEN, J. L. 1997. Permeability anisotropy estimation within the Sherwood Sandstone, Morecambe Bay Gas

Field: a numerical approach using probe permeametry. In: OAKMAN, C. D., MARTIN, J. H. CORBETT, P. W. M. (eds) Coresfrom the Northwest European Hydrocarbon Province." An illustration of geological applications from exploration to development. Geological Society, London, 197-203. & JENSEN,J. L. 1996. Permeability and permeability anisotropy characterization in the near well-bore: a numerical model using probe permeability and formation micro-resistivity data, Transactions of The Society of Professional Well Log Analysts Thirty-Seventh Annual Logging Symposium, New Orleans, 16-19 June, paper JJJ. WARREN,J. E., SK1BA,F. F. & PRICE, H. S. 1961. An evaluation of the significance of permeability measurements. Journal of Petroleum Technology, 13, 739-744. -

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Quantitative density measurements from X-ray radiometry A. R. D U N C A N 2, G. D E A N 1 & D. A. L. C O L L I E 2

t Amerada Hess Limited, 33 Grosvenor Place, London, S W 1 X 7HY, UK 2 Robertson Research International Limited, Unit 7, Wellheads Crescent, Wellheads Industrial Estate, Dyce, Aberdeen, AB21 7GA, UK Abstract: Qualitative linear X-ray scanning has an established role in the non-destructive imaging of both slabbed and whole core and has been routinely used in visual assessment and quality control of material being subjected to other physical measurements. Since core may be observed in real time, whole core can be oriented to maximum dip prior to slabbing, especially useful where core has been resin-stabilized within an outer liner. Linear scanning is also useful in the observation of heterogeneous lithologies; the features observed are distinguished by their penetrabilities to X-rays. As a result, the linear scanner produces an image which reflects the density variation in the section analysed. A joint project carried out by Robertson Research International Limited and Amerada Hess Limited on 108ft of heterogeneous sediments has shown that the digital X-ray penetrability values ('luminance') can be extracted in order to produce a surface density variation log. X-ray luminance values show a linear relationship with the downhole Formation Density Log and may, therefore, provide an accurate tool for the correlation of core density with log density.

Qualitative linear X-ray scanning already has an established role in non-destructive imaging of both slabbed and whole core and has been routinely used in visual assessment and quality control of material being subjected to other physical measurements (for example Algeo et al. 1994; Rigsby et al. 1994). Since core may be observed in real time, whole core can be oriented to maximum dip prior to plugging or slabbing, especially useful where the core has been resinstabilized within fibreglass, pvc or aluminium liners. This ability to examine interactively, in detail and non-destructively, the 3-D nature of the internal structure of the core material is particularly important. Linear scanning is therefore useful in the observation of both heterogeneous and apparently homogenous lithologies and the following features are commonly characterized: (a) bedding features and sedimentary structures; (b) bioturbation (ichnofacies analysis), especially in slabbed sections; (c) identification of remnant structure (not readily visible to the naked eye) which has been obscured by bioturbation; (d) natural and coring-induced fractures and shears (cemented/uncemented/open); (e) cement distribution; (f) small scale grain size variation; (g) assessment of resin competence in preserved and/or sleeved core. These features are distinguished by their

different penetrabilities to X-rays. As a result the linear scanner produces an image which reflects the density variation in the section analysed (Tolansky 1961). A project carried out on 108ft of heterogeneous sediments (Duncan et al. 1996) has shown that a digital measure of the X-ray penetrability values ('luminance') can be extracted in order to produce a surface density variation log. These X-ray luminance values may yield data at close and equally spaced points producing a log with significant advantages over the data from conventional core analysis (where sample spacing may be irregular, widely spaced and lithologically chosen, or where Gamma Ray response may be poor). Such data can be compared directly with the wireline logs and it is found that the X-ray luminance values show a linear relationship with the downhole Formation Density Log (FDL). The X-ray luminance data may therefore provide an accurate tool for the correlation of core density with log density. Database

The Scott partner group provided access to a range of core and associated materials: (a) 108ft of lithologically/mineralogically variable sediments (resinated archive slabs); (b) wireline logs for the analysed interval including the appropriate FDL traces; (c) the sedimentological composite log;

DUNCAN, A. R., DEAN, G. & COLLIE,D. A. L. 1998. Quantitative density measurements from X-ray radiometry In. HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 17-24

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A. R. DUNCAN ET AL.

18

(d) petrographic data for the seven thin section samples which fall within the analysed interval; (e) core analysis data (porosity, permeability and grain density) for the analysed interval.

Brief description of cores Section A The analysed interval commences within relatively 'clean', blocky, medium grained sandstones. These sandstones are assigned to the Piper Formation Depositional Unit 4c and are interpreted to be shoreface sandstones, possibly representing gully fill within an upper delta front system. At a depth of 6 ft below top of section these deposits are underlain by variably argillaceous sandstones with sandy, argillaceous siltstone interbeds. The sandstones are generally fine grained and are frequently apparently structureless or faintly laminated. Current ripples and burrows are locally observed. Bioturbation is more commonly observed in the finer grained units, some units are micaceous and locally contain carbonaceous material. Nodular calcareous cement is observed at approximately 36 ft below top of section. These lower deposits are assigned to Piper Formation Depositional Unit 4b and are interpreted to belong to an offshore transition zone. They are believed to be the deposits from turbidite flows in the lower delta front to pro-delta. Thin section analysis indicates that the predominant cement is quartz (8-11.5%) with relatively minor calcite (13.5%). Authigenic clays are dominated by kaolinite (0-3 %).

Section B The sediments within Section B are also assigned to Piper Formation Depositional Unit 4b and similarly consist of relatively clean, fine grained sandstones interbedded with siltier, argillaceous sediments which are moderately to highly bioturbated. The finer material is frequently micaceous and carbonaceous debris is locally recorded. Some of the coarser units show development of calcareous cement which is locally nodular. Thin section analysis indicates that the predominant cement is calcite (4-40%) with subordinate quartz (1-12%). No authigenic clays are recorded from the two samples analysed.

Section C The sediments analysed from Section C are

again assigned to Piper Formation Depositional Unit 4b. They also consist of interbedded fine or very fine grained sandstones and silty, argillaceous deposits. The modal grain size of the sandstones is seen to decrease towards the lower part of the analysed section. Pervasive calcareous and dolomitic, nodular cements are locally common. Thin section analysis indicates that the predominant cement is dolomite (42.5-48%) with subordinate calcite (3.5-5%) and minor quartz (0.5-2%). Authigenic kaolinite accounts for only 0.5%.

Methodology Production of the X-ray scan images A schematic representation of the scanning system is shown in Fig. 1. Although the imaging system has been developed to operate with core material of various forms and dimensions, the present investigations employed 3 ft resinated archive slabs for the imaging and quantitative density measurements. This presents a thickness of rock material for analysis which is relatively constant, both across the core diameter and along its length, and for which the 3-D inhomogeneities are reduced. In this way, differences in the core thickness and variability due to the curvature of the core are reduced and interpretation can be simplified to essentially 2-D. The X-rays passing through the rock create an inverted image of the material on an electronic image intensifier. This visible image of the X-ray field is picked up by a CCD camera and subsequently digitized. This image may be viewed in real time (i.e. the 'live' image on screen moves as the rock is transported along the gantry) and approximately 6-7in of core are observed at any one time within the camera image frame. These frames may be enhanced by a dedicated image processing computer and can be combined to produce a composite image of the 3 ft section. After positioning the core, each 'live' frame is frozen and a digital filter, which enhances the edge and structure information, is used to sharpen the image. Once the optimum image has been captured it is electronically transferred to a PC computer terminal and stored as a TIFF format file. Overlaps of approximately lin between neighbouring frames are used in order to ensure the optimum matching in the composite. The individual images are manipulated on the PC, using conventional image processing software, to produce the composite image, which is similarly stored in TIFF format. 'Hard

QUANTITATIVE X-RAY DENSIMETRY

19

Fig. 1. Schematic of X-ray scanner system. copy' images are produced using a grey scale printer matching the resolution of the digitized images. To further ensure accurate and straightforward matching of each frame to form the 3 ft composite image, a steel mesh with a grid of V2in is placed alongside each resin slab as it is inserted into the scanner. This is especially useful in sections of the core which appear structureless and homogenous. The steel mesh is positioned to avoid obscuration of the core and its image may optionally remain on the composite image for scaling and quality control purposes. Where no rock is present the image appears to be very bright (white). This is due to saturation or 'burn out' within the image intensifier, caused by the higher intensity of X-rays where there is little core material present to block them. This 'burn-out' of the image artificially increases the intensifier output in closely neighbouring areas, resulting in the surrounding rock appearing 'bleached'. In order to avoid the possible misinterpretation of the X-ray intensity in these areas, disks and/or strips of lead shielding approximating the density of the resinated slab material are placed into plug holes, and other significant gaps.

Production of the quantitative X-ray density data The digital images which are obtained from the scanner are composed of pixels of varying grey scale (0 to 255). The grey scale can be read, in

real time, at any given point across the image. Thus, by taking regularly spaced readings, a profile of the variation in the grey scale can be produced. These data (referred to as luminance values) indicates the penetrability of the rock material to X-rays and are, therefore, related to the density of the rock (Tolansky 1961). Higher luminance values represent greater penetration of the rock by the X-rays and, therefore, lower density. Conversely, lower luminance represents areas of rock with higher density and therefore greater X-ray 'stopping ability'. The luminance values (which vary between approximately 60 and 200 for typical core material) are, therefore, inversely related to the rock density. Despite the high quality of the imaging system used in the capture of the information, each of the images contains an astigmatic error. This means that there is an apparent density variation between the centre of the image compared with the edges. While this does not significantly effect the visual interpretation of the images; it is undesirable in the point luminance data. To eliminate this error, therefore, the luminance measurements are recorded from a fixed point within the X-ray field. The luminance profile is obtained by moving the rock (using the scanner transport mechanism) and recording the values at the known fixed point within the field of view. Measurements may be recorded at any required spacing; with 1 92 in spacing being used in the current project. The luminance measurements are made using a 'live' image, from which the background

20

A. R. DUNCAN E T AL.

Fig. 2. Example X-ray image frame. The included grid is of '/2 in mesh.

Fig. 3. (a) Correlation plot of luminance values from slabbed material and bulk density data from wireline logs. Core depth to log depth correction is shown schematically by tie lines. (b) Luminance data after depth correction to log depth; with wireline bulk density data overlaid. Arbitrary luminance and density scales. Luminance: solid line, Density: dashed line.

QUANTITATIVE X-RAY DENSIMETRY

21

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Fig. 4. Cross plot of luminance values from slabbed material and bulk density values derived from wireline logs. Luminance values shown both uncorrected and corrected for variations in thickness of core material. 'noise' is reduced by using a moving average filter (i.e. each measurement is made on an image comprising the average of 20 scans of the stationary rock). This is done automatically, and in 'pseudo real time', using the image processing computer. Measurements of the thickness of the slab at each luminance recording point are noted. In addition, luminance values for an aluminium block 'standard' placed at the top and base of each 3ft section are measured. Where necessary the scanner controls can be adjusted prior to scanning to ensure that the observed luminance from these calibration standards remains consistent. These data, along with the luminance values are entered into a spreadsheet and stored on the PC for subsequent analysis

Description of X-ray images Figure 2 presents a single frame showing the Xray image at the point 'F' marked on Fig. 3. The bright, irregular lines represent core breaks which are likely to be coring induced. The core breaks are locally bedding-parallel. This frame displays very clearly a partially cemented fracture running subvertically through the core. The contrast produced by variations in the core material density allows detailed examination of these and other features. A conventional core analysis plug hole, with included masking, is shown, as is the '/2 in alignment grid.

Discussion The luminance values indicate the penetrability of the rock material to X-rays and are, therefore, related to the density of the rock. Higher luminance values represent greater penetration of the rock by the X-rays and, therefore, lower density. Conversely, lower luminance represents areas of rock with higher density and therefore greater 'stopping ability' of the X-radiation. The luminance values are, therefore, inversely related to the rock density. A comparison of this luminance data, representing density, with traditional wireline log density measurements is presented in Figs 3(a and b). Figure 3(a) shows the correlation of the luminance data with the FDL trace. The tie lines indicate the core to log depth shifts appropriate for this core material. Clearly excellent correlation between the luminance profile and wireline log is observed, with Fig. 3(b) showing the luminance data depth shifted and superimposed on the FDL trace. The luminance values are smoothed (using a simple 5 point moving average filter) but are otherwise unprocessed. A cross plot of luminance against bulk density (from the wireline log) is shown in Fig. 4. Luminance values are shown both uncorrected and corrected for slab thickness variations. The correction is performed assuming a simple reciprocal relationship between thickness and luminance value: this is considered to adequately

22

A. R. DUNCAN E T AL.

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Fig. 6. Cross plot of luminance values and bulk density values, both from analysis of selected core plugs, Data differentiated by lithological unit. (Independent plug set).

describe the interaction of the X-rays with the bulk material over the relatively small observed variation in both luminance and thickness. This thickness c o r r e c t i o n is seen to h a v e little significance in the final correlation of the logs.

Detailed conventional core analysis and sedimentological analysis has been carried out on the core sections analysed for this project; this data has been c o m p a r e d with the luminance data measured from the slabbed section close to the

QUANTITATIVE X-RAY DENSIMETRY plug locations. Figure 5 shows, for example, a cross plot of porosity of the CCA plug samples against the luminance values. Again the linear relationship between luminance and this key physical property is well defined. As further confirmation of these relationships, X-ray luminance values were measured for an independent and varied collection of conventional core analysis plug samples whose physical and geological properties are well established (Duncan 1993). Figure 6 shows the bulk density for these samples plotted against luminance; the strong linear relationship is again confirmed. These data are further differentiated by lithology and while it is interesting to speculate on the relationship between lithology and luminance, this dataset is considered too sparse to prompt any reliable conclusion. Interestingly, and as a positive demonstration of the utility of these X-ray densimetry measurements, the original core to wireline shifts for the slabbed core sections were taken to be: Wireline Log Depth equals Core Depth + six feet. Comparison of the quantitative FDL (log) and luminance measurements, however, indicates that a core to wireline correction of eight feet (downhole) for section A is more appropriate. A shift of six feet for Sections B and C is confirmed by comparison of the density and luminance traces. These revised depth shifts have been applied to the luminance data presented in Fig. 3(b). While the sections analysed for this project were chosen in part for their known variation in sedimentological structure, the success of this correlation technique in its most basic form without any significant data processing clearly demonstrates the potential of these measurements. It is believed that refinement of the processing could yield considerable additional data which, coupled to the non-destructive nature of the methods, the ability to analyse material within opaque liners and the speed of data capture, makes the technique of very considerable importance.

Development Technically, the performance of the scanner is excellent. Quantitative investigations of the physical performance of the scanner, for example of the effects of variations in X-ray power output or the influence of 'burn-out' around unshielded plug holes etc., could potentially lead to direct calibration of the luminance values in terms of physical properties of the core material. Improvements in the operating procedures, in

23

the loading of the core material and, in particular, in the automated capture of the image and luminance data could allow higher throughput; enabling greater data sampling densities and potentially more detailed data processing and analysis. Perhaps of greatest interest is the elimination of the optical distortion error in the individual image frames. While the necessary geometry of the scanner is a major contributory factor to this error, it is believed that image processing may yield a significant and reliable reduction. By viewing the image of a homogenous standard (such as an aluminium block of similar size to the core section), it is possible to store the variations in the image due to this error in digital form. By 'deconvolving' the standard image obtained in this way from the images of the scanned rock it may be possible to provide a much flatter response from the imaging system. This intermediate processing would allow an accurate digital representation of the density of the core across the full image to be produced. Instead of collecting data at specific points, it would then be possible to map the density variation of the rock slab in two dimensions. This would provide, not only a more accurate log for comparison with downhole logs, but also allow the density variation to be plotted as a three-dimensional map, potentially highlighting more subtle variations in the core structure. Initial work carried out is encouraging.

Conclusion Linear X-ray scanning has an established role in non-destructive imaging of core, with the variation in image reflecting the density variation in the core section. The techniques described here allow not only the qualitative X-ray image to be produced, but also quantitative luminance values to be extracted. These correlate very well with physical core properties, for example bulk density and porosity, derived from wireline or conventional core analysis techniques. These luminance values thus provide a valuable core to log correlation tool which may be of particular value where traditional Gamma Ray or Core Analysis techniques are unavailable or relatively unreliable due to poor response or sparse data. The possibility of improving the operating procedures, in particular the sampling interval and processing methods, as well as ultimately providing full density maps of the core section promise to yield even greater benefits, and confirm the importance of X-ray imaging as a core analysis tool.

24

A . R . DUNCAN ET AL.

We gratefully acknowledge the permission to publish this material granted by the Scott partner group: Amerada Hess Limited, Amoco (UK) Exploration, Deminex (UK) Oil and Gas, Enterprise Oil, Kerr McGee Oil (UK), Superior Oil (UK) and Premier Pict Petroleum. Our thanks goes to F. Matheson at Robertson Research Int. Ltd who diligently and expeditiously undertook the preparation and measurements of the core material analysed during this project.

References ALGEO, T. J., PHILLIPS,M., JAM1NSKI,J. & FENWlCK,M. 1994. High resolution X-radiography of laminated sediment cores. Journal of Sedimentary Research A: Sedimentary Petrology and Processes, A64, 665-668.

DUNCAN, A. R., 1993. A sedimentological, petrographic and reservoir geological study of the Devonian age, old red sandstone of Gamrie Bay, Grampian Region. M.Sc. Thesis, University of Aberdeen. DUNCAN, A. R., MATHESON,F. E., & COLLIE, D. A. L. 1996. Quantitative X-ray density imaging of selected cores. Robertson Research International Ltd Project Report No D213 for Amerada Hess Ltd. RIGSBY, C. A., ZIERENBERG,R. A. & BAKER, P. A. 1994. Sedimentary and diagenetic structures and textures in turbiditic and hemiturbiditic strata as revealed by whole-core X-radiography; Middle Valley, northern Juan de Fuca Ridge. Proceedings, Scientific Results, ODP leg 139, 105-111. TOLANSKY, S. 1961. Introduction to Atomic Physics. Longmans.

The estimation of modal mineralogy: a problem of accuracy in core-log calibration P. K. H A R V E Y , 1 T. S. B R E W E R , 1 M. A. L O V E L L 1 & S. A. K E R R 2

1Borehole Research, Department o f Geology, University of Leicester, Leicester, LE1 7RH, UK 2 British Petroleum, Chertsey Road, Sunbury-on-Thames. Middlesex, TW16 7LN, UK

Abstract: In the case study described here the quantitative modal mineralogy of a number of core samples was determined with the objective of using these modes to calibrate geochemical logs. Modal estimates were obtained for the core samples by quantitative X-ray diffraction, infrared spectroscopy, point counting of thin sections, and indirectly by calculation from a complete chemical analysis of the samples. In the case of calculated modes, three different algorithms were applied. A by-product of this particularly complete dataset is the possibility of evaluating the most accurate method of modal analysis, and although no certain conclusion is reached on this point the analysis of these data does demonstrate the difficulty of obtaining accurate modal estimates. The core samples, taken at regular intervals through a sand, sandy-shale sequence, capped by a carbonate unit, have a mineralogy which, although dominated by quartz, includes feldspars, carbonates, and clays (illite, kaolinite) together with minor phases. There was generally good agreement between methods in the estimation of quartz, total carbonate, albite, kaolinite, total clay and pyrite. The results for illite and K-feldspar were poor, a reflection of their relatively low concentrations (< 10%), and problems of compositional co-linearity in the calculated modes.

A useful way of presenting data from geochemical logging tools is to transform the raw oxide curves into mineralogy logs. In a recent exercise aimed at calibrating geochemical logs in a UK borehole a number of core samples (103) were taken and analysed extensively in the laboratory for both chemistry and mineralogy, to provide a database to support the log calibration. For all 103 core plugs quantitative mineralogy was determined by X-ray diffraction at the British Petroleum laboratories in Sunbury and by infrared spectroscopy (MINERALOG) at Core Laboratories. In addition a petrographic examination was carried out, and a minimal point count made (200 points per thin section) on approximately half the samples to provide approximate modal data. All core plugs were also chemiclly analysed by X-Ray Assay Laboratories (XRAL) in Ottawa for all major and all potentially significant trace elements (a total of 69 elements per sample). From the chemical data, estimates of the modal mineralogy were calculated using a selection of different algorithms. Together these analyses result in a range of modal estimates and the purpose of this contribution is to compare these estimates in an attempt to evaluate the accuracy of the different methods. Apart from the petrographic work, all

measurements were made on aliquots of the same crushed and thoroughly homogenized rock powder for each sample. There is, therefore, essentially no scaling problem involved to explain variations in the modal estimates, and a minimal problem of sub-sampling from the rock powder.

Background Through the use of pulsed neutron devices, direct activtion of the formation by appropriate isotopes, and the natural gamma spectra it is possible to obtain an almost complete, and continuous log of the major element chemistry of a formation. These techniques were pioneered by Schlumberger (Hertzog & Plasek 1979; Hertzog et al. 1987a, 1987b, 1989; Galford et al. 1988; Rupp et al. 1989) with their Geochemical Logging Tool (GLT) offering measurements of Si, A1, Ti, Fe, Ca, K, S, the minor elements Gd, Th, and U, together with H and C1. Other tools are now available (Wyatt & Jacobson et al. 1993; Odom et al. 1994; Jacobson & Wyatt 1996, Herron & Herron 1998). Transformation of the major elements into the more conventional oxide form gives virtually complete major element oxide analysis at each measured depth interval,

HARVEY,P. K., BREWER,T. S., LOVELL,M. A. & KERR, S. A. 1998. The estimation of modal mineralogy: a problem of accuracy in core-log calibration In. HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 25-38

25

26

P. K. HARVEY E T AL.

typically every 15cm, down the borehole. One approach to the interpretation of the resulting geochemical logs is their conversion into computed mineral assemblages, the so called 'chemical modes' of Wright and Doherty (1970). The resulting mineralogy logs are valuable in their own right but may be used in addition with a suitable rock classification filter to produce lithological logs (Herron 1988), or estimation of other formation properties such as matrix (grain) density, porosity, Cation Exchange Capacity (Chapman et al. 1987; Herron 1987b; Herron & Grau 1987), thermal conductivity (Dove & Williams 1988), heat flow (Anderson & Dove 1988), photoelectric factor, Pe (Kerr et al. 1992), magnetic susceptibility (Harvey et al. 1997), fluid saturation (Hastings 1988), neutron capture cross-section (Herron 1987b), and, indirectly and probably only in formationspecific situations, permeability (Herron 1987a). The transformation of a rock's elemental composition to mineral assemblages has been the subject of many contributions. Igneous petrologists have long employed the C.I.P.W. norm (Cross et al. 1903), and similar ideas have been extended to metamorphic rocks with Niggli's normative procedures (Burri 1964). In calculating norms there is no requirement that the mineral assemblage used is that which actually occurs in a rock, and as such the C.I.P.W. norm, for example, was developed originally for purposes of classification and employed a strict and standard list of 'possible'. minerals. However, for the purposes to which geochemically derived mineralogy logs might be put (lithological analysis, basin modelling, petrophysical estimation) it is necessary to estimate the percentage of the minerals that are actually present in the rock. The latter is the 'mode' of the rock which is conventionally determined directly by micrometric analysis (point counting of a thin section) or spectral methods such as Xray diffraction or infrared spectroscopy (Harville & Freeman 1988; Adam et al. 1989; Matteson & Herron 1993). The alternative approach is to compute the mode from a complete chemical analysis. Numerous authors have offered specific solutions to this inversion problem including modified normative schemes (Imbrie & Poldervaart 1959; Nicholls 1962; Pearson 1978), graphical models (Miesch 1962; Fuh 1973) and a variety of numerical models including least squares minimization, linear programming and genetic algorithms (Wright & Doherty 1970; Albarede & Provost 1976; Fang et al. 1996), while others have considered the strategy and associated practical problems of performing the inversion

on a routine basis (Herron 1986; Harvey et al. 1990; Harvey & Lovell 1992; Harvey et al. 1992; Lofts et al. 1994, 1995b). For the case study described here a particularly complete dataset is available consisting of conventional (physical) modal mineral measurements, together with comprehensive chemical analyses from which calculated modes could be obtained. It is the particularly complete nature of these data which justifies a comparison between physical and calculated methods of modal analysis, and the opportunity to make some comment on the accuracy of modes. Using natural samples (borehole core plugs) in this case, however, precludes any definite means by which one method can be chosen as 'more accurate' than another, unless a particular mode is 'obviously' wrong. As a first order assumption, however, it is likely that if two or more unrelated methods of estimation give essentially the same result then they are probably close to the true value. This level of uncertainty arises because all methods of modal estimation can give seriously erroneous results sometimes, and for some minerals; it is not simply a question of calibration and precision (repeat measurement error). With the spectral techniques particular problems arise from spectral overlap and poor resolution at the lower concentrations. With computed modes it is the choice of the correct mineral assemblage, the correct 'composition' and possible problems of compositional colinearity which are important. Modes are usually obtained by micrometric analysis (point counting of a thin section) and as such are usually expressed in volume percent of the optically identified minerals. In contrast, a set of modal proportions may be calculated, to give 'norms', by assuming an ideal or theoretical suite of minerals, and the compositions for those minerals. From these data some sort of fit may be found that partitions the mineral compositions within the initial rock analysis. Norms are usually expressed in weight percentages, and have found very wide application, particularly in igneous petrology, for characterization and classification. In these applications comparison between rocks is made with a common set of (chosen) minerals, unlike the mode, which reflects the actual minerals present. Normative mineralogy logs may be useful in the early stages of an investigation but are no substitute for the estimation of the actual mineral percentages present if attempts are to be made later to generate, for instance, a grain density log. In this report the attempt is made to calculate chemical modes, or the mineral proportions of the actual minerals present in the sample.

THE ESTIMATION OF MODAL MINERALOGY

27

Table 1. Mineralogy of the core samples. Technique Silica Feldspars Carbonates

Clays/Micas

Minor phases

Quartz Albite K-feldspar Calcite Ankerite Dolomite Siderite Muscovite Illite Smectite Kaolinite Chlorite Zircon Barite Pyrite Apatite

XRD

MINERALOG

Petrography

* * * * *

* * * *

* * * *

* *

* * * *

* * *

* * *

*

* *

* * * * * *

* mineral detected in at least one of the core samples. Anhydrite and chlorite were not detected in any of the samples by the infrared (MINERALOG) technique. See report for further comments.

Mineralogy of the core samples The mineralogy of the core samples derived from a combination of XRD, infrared (MINERALOG) and petrographic information is summarized in Table 1, and also shown as a downhole mineralogy log in Fig. 1. The 100 metre sequence consists of clastics covered by some 8 m of limestone. For purposes of description the section can be divided into five units: (Unit 1) (360-368 re)virtually pure calcite limestone; (Unit 2) (368-382m) quartz rich (60-65%) section with sub-equal quantities of feldspar (both albite and K-feldspar) and clays. Both kaolinite and illite (the latter generally in excess) are present; (Unit 3) (382-397m) quartz-carbonate dominant lithology with quartz in excess, and sub-equal proportions of kaolinite and illite; (Unit 4) (397-430 m) Very inhomogeneous section with 40 to 70% quartz, no significant carbonate, and clay concentrations up to c.30%; kaolinite generally in excess of illite. Locally high concentrations of pyrite (included with 'minor' minerals in Figure 1). ( U n i t 5) ( 4 3 0 - 4 6 0 m ) R e l a t i v e l y u n i f o r m quartz-rich (70-80%) section with minor feldspar, about 15% clay, some two-thirds of which is kaolinite, with a few percent of minor minerals. Of the possible carbonate phases calcite,

dolomite and siderite are clearly distinguishable petrographically in stained thin sections and are quantified as those species in the infrared ( M I N E R A L O G ) results. In the X R D analyses, ankerite (a Ca, Mg, Fe carbonate) is quantified in place of dolomite, so that the two spectral techniques are not estimating the same carbonate species. The carbonates occurring in Units 2 and 3 are dominantly dolomite or ankerite, with minor amounts of siderite, while the limestone unit at the top of the logged section (Unit 1) is a virtually pure calcite limestone. For the clay and other phyllosilicate minerals, kaolinite and chlorite are measured together in the X R D analyses. Chlorite was only seen in thin section in occasional grains and was not detected in the infrared figures. For this study chlorite is considered to be absent. Muscovite and illite are also determined together by XRD. Occasional distinct flakes of white mica are present in a number of sections but in no case would these make up more than a fraction of one percent of the rock. While white mica (assumed to be muscovite) is known to be present in a very small amount in some samples it was not detected by infrared spectroscopy, and is virtually impossible to calculate with any reliability due to a strong compositional colinearity with K-feldspar, illite and kaolinite which are present in significant proportions. Included amongst the minor phases which occur in at least some of the samples are zircon, barite, apatite and pyrite, all of which have been identified petrographically. Of these, only pyrite occurs locally in sufficient quantity to be identified and measured by both X R D and

28

P.K. HARVEY E T AL.

Fig. 1. Computed mineralogy log (Model A) for the section under study and showing the stratigraphic units discussed in the text. For clarity only, the major mineral groups are shown. The depth scale in arbitary. infrared. Of the other three minerals barite was detected by infrared, but both apatite and zircon were too low for the spectral methods.

Numerical modelling of the core sample mineralogy The estimation of a modal mineral assemblage from the chemical analysis of a sample requires the minerals in the assemblage to be chosen, and the compositions of those minerals to be defined. Given this information there are a variety of solution methods and strategies that can be employed to solve for the mode (Harvey et al. 1990, 1992; Lofts et al. 1994; Fang et al. 1996). For the modelling of the samples described here, the main minerals are quartz, feldspars (albite, K-feldspar), carbonates (calcite, dolomite, siderite), clays (kaolinite, illite) and the minor phases (zircon, barite, apatite, pyrite).

From the mineral data above, all the observed mineral assemblages can be established, and these, minor phases excluded, are summarized in Table 2; in all a total of thirteen different assemblages. From the chemical viewpoint the following components are available for modelling: SiO2, A1203, TiO2, Fe203, MgO, CaO, Na20, K20, MnO, P205, S, CO 2 and H 2 0 + , expressed in weight percent, together with Ba and Zr which were reported in parts per million. No other 'minor' elements are in sufficient concentration to be expected to form discrete mineral phases, or significantly alter the modal estimates of other mineral phases in which they might occur as trace lattice components. Zr and Ba are considered to occur only in zircon and barite, respectively. In addition, amongst the oxide components P205 almost certainly occurs at significant levels only in

29

THE ESTIMATION OF MODAL MINERALOGY

Table 2.

Observed mineral assemblages in the core samples (excluding minor phases).

Assemblage

l

2

3

4

5

6

7

8

9

10

11

12

Silica Feldspars

* * * *

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Quartz Albite K-feldspar Carbonates Calcite Dolomite + Siderite Clays/Micas Illite Kaolinite

* *

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13

*

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* mineral detected in at least one of the core samples. Anhydrite and chlorite were not detected in any of the samples by the infrared (MINERALOG) technique. See report for further comments. + dolomite or ankerite. See text for explanation. apatite which has been identified petrographically. TiO2 poses a problem, and in the first instance is best calculated as rutile, though there is no evidence that this mineral actually occurs in any of the samples. TiO2 may also be present in one of the clay phases, and this problem is discussed later where there is good evidence that it actually occurs in different minerals in different parts of the section. Sulphur is assumed to be present only as a component of pyrite. Other minerals, such as gypsum or anhydrite are possibilities, though there is no evidence for any sulphates being present, and pyrite is the only identified sulphide. Manganese, which is only present at a very low level (maximum 0.75% MnO, 90% of measurements less than 0.18% MnO), was a d d e d to iron (as FeO) for purposes of computation. Manganese often substitutes for iron, and the significant correlation (at a = 0.05; r = 0.58) between the two elements in these data is consistent with this occurring here. Removing MnO leaves a total of 14 possible mineral phases and 15 chemical components to consider. Of the several strategies employed in the modelling of the mineral assemblages in this case history three simple methods are presented here. In each case the data were pre-processed to remove the minor phases rutile, apatite, barite, zircon and pyrite which were calculated out of each core analysis assuming ideal stoichiometric compositions. Provided the chemical analyses of the core samples are accurate this procedure gives excellent estimates for these minerals which cannot be matched by any direct measurement. Although treated here as a minor phase, pyrite does reach significant concentrations in a few samples; the variation in pyrite downhole is shown in Fig. 2, and is discussed later. With extraction of these minor minerals TiO2,

BaO, ZrO2, S and P205 were removed from the data matrix leaving SiO2, A1203, FeO, MgO, CaO, Na20, K20, CO2 and H 2 0 + to be distributed, as appropriate, between the important remaining mineral phases: quartz, albite, Kfeldspar, calcite, dolomite, siderite, kaolinite, illite and possibly smectite. The simultaneous estimation of all these minerals together would constitute a fully determined system for methods of inversion involving the solution of systems of equations.

Strategies for extraction of the main mineral phases: Models A, B, C To remove complications related to methods of solution a simple unconstrained and unweighted least squares method has been used throughout (Harvey, et al. 1990) for inverting the different models. One consequence of the lack of constraint is that mineral proportions may be negative, implying an insufficiency of a combination of elements with respect to a 'perfect' solution. Such negative estimates, while impossible, offer a guide to the fact that either the modelled assemblage is wrong, or one or more of the mineral compositions are in error. Clearly, such a solution is unacceptable. One approach, then, is to model a given composition with all likely minerals, and to reject those minerals which turn out negative. This is the basis of Model A, described below. Another approach is to model each given composition to all the mineral assemblages which are known to occur in the section (Table 2), and chose the best fit as the appropriate solution. This is the basis of Model B. For Model C the mineral assemblage obtained from the X R D analysis was taken as correct, and the given composition fitted to that assemblage. This latter approach, in principle,

P. K. HARVEY ET AL.

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Fig. 2. Pseudo-log showing the mineralogical variation of the core samples downhole for quartz, K-feldspar, albite and pyrite. Qtz-(a): quartz computed from Model A, Qtz-(b): ditto, for Model B, Qtz-(m): MINERALOG measurement of quartz, Qtz-(x): XRD measurement of quartz. Coding as for quartz for: pyrite (Py), K-feldspar (Kf) and albite (Ab).

removes the 'guesswork' out of the choice of mineral assemblage. M o d e l A. minerals

iterative removal o f n e g a t i v e

Solutions with negative compositions imply that there is an inconsistency in the postulated mineral compositions (as stated above) and a simple means of overcoming this is by removing the mineral from the analysis. Wholesale removal of all negative minerals in one pass,

however, cannot be justified because of the complex interaction of phases in a least squares model. With little or no formal justification one procedure we have found very effective is to remove the most negative phase and re-solve the system. If negative phases still occur the procedure is repeated until all phases are positive. The procedure is illustrated in Table 3 for sample K78. The least squares fit using all nine minerals is good but dolomite is slightly negative (a) at --0.21. Removing dolomite as one of the phases sends calcite slightly negative

31

THE ESTIMATION OF MODAL MINERALOGY

Table 3. Example of the successive removal of negativephasesfor sample K78. a Quartz Albite K-feldspar Calcite Dolomite Siderite Kaolinite Illite Smectite Std. Err.

79.81 0.21 5.42 0.01 -0.21 0.22 12.13 0.00 1.29 0.011

b

c

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80.05 0.00 5.30

0.21 12.35 1.14 1.22 0.057

0.13 12.44 0.81 0.96 0.067

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MINERALOG

88 0 3 0 0 0 7 0 0

80 0 7 0 0 0 5 7 0

(a) unconstrained and unweighted least squares model. (b) as (a) but with dolomite (negative proportion) excluded. (c) as (b) but with calcite removed to give a fully positive solution. Corresponding XRD (X-ray diffraction) and Mlog (MINERALOG) estimates are given for comparison. (XRD includes 2% pyrite, Mlog 1%). Std. Err.: standard error (see text for calculation). (b). In a final stage calcite in removed (c) to give the final estimate. The mineralogical pseudo-log shown in Fig. 1 was produced using this model. For completion in Fig. 1, the trace minerals were added and the Model A derived mineralogy normalized to make the assemblages sum to 100%. The standard error given in Table 3 is a measure of the fit of the core and mineral chemistry and is computed between the original (input) chemistry, and the composition backcalculated from the derived (output) mineralogy (Harvey et al. 1990).

For each core sample a list of the minerals present had already been identified by X R D , petrography or infrared analysis. For this model the X R D mineral assemblage was chosen for each sample and then fitted accordingly. Poor fits, often with negative mineral estimates, identify a real incompatibility between the rock and mineral chemistry assuming that the phases are correctly identified by the XRD.

Model B. choice o f known mineral assemblages

Comparison of measured and calculated estimates of mineralogy

Thirteen possible parageneses for the core samples have been identified, within limits of detection, from the X R D , petrographic and infrared data. These are summarized in Table 2. Most assemblages contain five or six phases; only one (assemblage 3) contains over six (7), so that the number of minerals is generally at least two less than the number of chemical components. The procedure was to fit each sample to each of these possible assemblages. The optimum assemblage was then chosen using the following criteria:

Figs 2 through 4 summarize the variation in measured (XRD & M I N E R A L O G ) and computed (Models A & B) values for quartz, feldspars and pyrite (Fig. 2), carbonates (Fig. 3), and the clays (Fig. 4). A more detailed comparison may be made by examination of Table 4 which shows the correlations between all models and the physically derived measurements. In Table 4, based on 103 core samples, correlations > 0.19 are different from zero at a significance level of 0.05, and > 0.25, at a level of 0.01. For quartz, the best correlation is between Model A and the infrared ( M I N E R A L O G ) data; the relationship is linear (Fig. 5) and close to the 1:1 line. The agreement between the two is particularly good in the lower half of the section (Units 4 & 5), though in the upper part, below the limestone cap, the computed quartz estimates are almost consistently lower. Regression analysis of this relationship gives a slope of

(a) in virtually all cases this procedure yields a selection of potentially acceptable (nonnegative) assemblages; the one with the smallest standard error is then chosen; (b) but, if all possibilities gave at least one negative mineral proportion the assemblage with the smallest absolute negative sum was chosen.

Model C: fit to the individual mineral assemblage for each core sample

32

P. K. HARVEY ET AL. ,

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1.03 and standard error of 2.36% quartz after removal of four outlying points. In view of this excellent relationship is of note that the correlation for quartz between the X R D and the infrared is slightly lower with the X R D measurements being generally higher than the infrared, often by several percent. Overall for quartz, all models perform reasonably well and show similar patterns of variation. It is not at all clear which set of data is correct! Of the feldspars, both albite and potashfeldspar are present in small quantities. Albite is virtually restricted to Units 2 and 3, with

estimates averaging between 7% and 9% for Models A and B, respectively. The infrared figures agree at 7.4%; the X R D average is lower at 2.6%. The highest linear correlation is again shown between Model A and the MINERALOG, and it is likely that these methods are giving close to the true result. For the calculated modes the albite concentration is constrained by the sodium concentration, and in the absence of any other sodium bearing mineral, an accurate estimate should be expected. With albite occurring in concentrations below 10% there are problems of sensitivity and detection limit with

THE ESTIMATION OF MODAL MINERALOGY

33

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Fig. 4. As Figure 2 for the clay phases: kaolinite (Kaol); Illite (Ill); and total clay (Clay) which is taken as the sum of (kaolinite + illite + smectite).

Table 4. Correlations between computed modes and infrared (MINERALOG) estimates (top table) and between computed modes and XRD (lower table). Mineral

A/Mlg

Quartz Albite K-feldspar Kaolinite Illite Calcite Siderite Dolomite E carbonates E clays Pyrite

0.987 0.865 0.193 0.928 0.025 0.976 0.869 0.901 0.998 0.911 0.975

B/Mlg 0.980 0.837 0.431 0.916 0.512 0.975 0.852 0.587 0.998 0.888 0.975

C/MIg

Mlg/XRD

0.954 0.662 0.192 0.973 0.760 0.975

A/XRD

0.962 0.712 0.515 0.785 0.587 0.994 0.848 0.953 0.994 0.856 0.968

0.966 0.847 0.146 0.809 -0.071 0.983 0.836 0.904 0.993 0.875 0.972

B/XRD 0.953 0.862 0.433 0.784 0.227 0.979 0.802 0.529 0.994 0.821 0.972

C/XRD 0.948 0.586 0.274 0.978 0.771 0.972

Bold: highest correlation for that mineral.

Italic: correlations insignificantly different from zero at a level of 0.01. A, B, C: computed modes, Models A, B and C, respectively. XRD: X-ray diffraction. Mlg: MINERALOG.

both X R D and infrared methods; in view of this the agreement between all methods is remarkable. Potash feldspar occurs throughout the section below the limestone cap at concentration levels similar to albite (Fig. 2). There is poor agreement in detail between the models; Model B shows the highest correlation of the computed models, but is little lower than the weak correlation of 0.515 between the X R D and infrared figures (Table 4). The estimation of Kfeldspar suffers both from problems of low concentration in the physically derived estimates and potential compositional co-linearity in the

computed models (Harvey et al. 1992). For the carbonates there is overall excellent agreement between all the methods (Figs 3 & 6) in that the total amount of carbonate (calcite + dolomite/ankerite+ siderite) determined by the different methods is essentially the same. In detail, however, there are some distinct differences between the measured and calculated mineral percentages. These effects are seen clearly in Fig. 3 where calcite is correctly and accurately estimated by all methods in the virtually pure calcite limestone cap, but in Units 2 and 3 below, where more than one carbonate mineral is present, dolomite is severely under-

34

P.K. HARVEY E T AL.

Fig. 5. Crossplot of measured and estimated quartz contents for the three computed models and XRD (yaxis), shown relative to the infrared (MINERALOG) (x-axis) measurements. Qtz-(a): quartz computed from Model A; Qtz-(b): ditto, for Model B; Qtz-(c): ditto, for Model C; Qtz-(x): XRD measurement.

Fig. 6. As Figure 5 for total carbonate (calcite + dolomite/ankerite + siderite)percentage. Carb-(a): total carbonate computed from Model A; Carb-(b): ditto, for Model B; Carb-(c): ditto, for Model C; Carb(x): XRD measurements.

estimated by the calculated models, and siderite is slightly over-estimated. Calcite is included in results of both Models A and B but was not detected by either X R D or infrared methods. These differences are due essentially to the use of ideal carbonate compositions (Table 5) in the mineral calculations and the compositions of the actual carbonates present in these samples not being available. The use of an ankeritic compo-

Fig. 7. Regression expressing kaolinite calculated using modal Model A as a function of the infrared (MINERALOG) measurements. The relationship is seen to be linear with a slope close to unity, but an intercept of some 5%.

sition instead of pure dolomite, for example, would have caused the siderite estimates to be lower (and more comparable with the X R D / infrared figures) and the dolomite/ankerite estimates higher. Hence, for the carbonates the accuracy of the calculated modes is compromised through the use of inappropriate mineral compositions; a knowledge of the latter is essential if accurate solutions are to be obtained (Lofts et al. 1995a) Of the clays, kaolinite and illite were determined by all methods; the comparative results are summarized in Fig. 4. The closest agreement between computed and physically determined estimates of kaolinite are shown between Model A and infrared ( M I N E R A L O G ) ; the agreement between X R D and infrared being distinctly poor. A crossplot of the Model A/infrared relationship is shown in Fig. 7. Apart from three obvious outlying points the latter is essentially linear, with a slope close to 1.0, but an intercept which over-estimates the Model A values compared to infrared, on average, by 5% kaolinite. The two most outlying points in Fig. 7 occur as outliers on other plots and come from a section which the core photographs indicate to be very inhomogeneous. Sample preparation should have removed this problem for laboratory work; their gross deviation remains a problem. For illite there is no real agreement between any of the models. The closest relationship is shown between the X R D and infrared figures, but with a correlation coefficient of only 0.587 it

THE ESTIMATION OF MODAL MINERALOGY

35

Table 5. Compositions of the model minerals used to evaluate Models A, B and C.

SiO2 A1203 FeO MgO CaO Na20 K20 H20 CO2

Qtz

Ab

K-f

Cal

Dol

Sid

100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

68.74 19.44 0.00 0.00 0.00 11.82 0.00 0.00 0.00

64.76 18.32 0.00 0.00 0.00 0.00 16.92 0.00 0.00

0.00 0.00 0.00 0.00 56.03 0.00 0.00 0.00 43.97

0.00 0.00 0.00 30.41 21.86 0.00 0.00 0.00 47.73

0.00 0.00 62.02 0.00 0.00 0.00 0.00 0.00 37.98

Kaol 45.48 39.29 0.65 0.14 0.41 0.00 0.00 14.04 0.00

Ill 57.28 18.55 5.11 2.07 1.59 0.43 5.11 8.86 0.00

Smec 51.14 19.76 0.83 3.22 1.62 0.11 0.04 22.80 0.00

Qtz: quartz, Ab: albite, K-f: K-feldspar, Cal: calcite, Dol: dolomite, Sid: siderite, Kaol: kaolinite, Ill: illite, Smec: smectite. Oxide concentrations are in weight percent oxide. is a poor predictive relationship; Model B has the highest correlation of the computed models (r=0.512), with the infrared estimates, though its correlation with the X R D data is nonsignificant (at a level of 0.05). Depending upon which set of data is believed illite is present throughout most of the section (Fig. 4) with the highest values (10-20%) in Units 2 and 3. It is difficult quantify using the physical methods because of poor sensitivity and spectral variation, and to compute because of uncertainty about the mineral chemistry and chemical variation in these rocks. Despite these problems the total clay curve shown in Fig. 4 shows surprisingly good agreement over the range of methods.

Discussion The results described here came from a study of mineral inversion methods to determine the way in which the most accurate mineralogy log could be obtained from a suite of geochemical logging data. The chosen method would have to involve calculation from the geochemistry, and validation of these results would need to be by comparison with some independent method. For the samples used in this validation a particularly comprehensive dataset was available from X R D and infrared ( M I N E R A L O G ) analyses made on the same samples. The latter, and the geochemical analyses upon which the modal calculations were based, were all measured on aliquots of the same homogenized powder for each sample. All measurements were, hence, made on essentially the same material, and on very similar volumes of the same material in each case. In the comparisons made here there is, therefore, no real problem of heterogeneity or scaling (up or down) involved; just a simple

comparison between a range of methods for the estimation of the proportions of different minerals in a set of samples. The question is, which is the 'best' (most accurate?) method for calibrating mineralogy logs, or whether the calculations on their own are actually superior. All methods obviously have their strengths and weaknesses. The major problems with the spectral methods concern sample preparation (and presentation), sensitivity and spectral resolution at low levels (lowest few percent) and spectral interferences. The main problems with calculating the mode concern the accuracy of the sample's chemical analysis, the need to solve for the correct phase assemblage and to have a good estimate of the phase compositions, and compositional colinearity. Modern methods of quantitative geochemical analysis are much more precise than the direct methods of modal analysis compared here, and with appropriate calibration may be expected to produce estimates of the chemical components which are close to the actual values. Some effects of concentration errors on elementto-mineral inversion are discussed by Herron & Chiaramonte (1993). We have shown elsewhere that if the chemical analysis is correct, the phase assemblage known, and the minerals in that assemblage analysed, then the calculated mode is in excellent agreement with the 'true' mineral proportions (Lofts et al. 1995b). While this may seem self evident the 'quality' of a calculated solution very rapidly deteriorates as analytical errors increase. Where an element is virtually restricted to one mineral phase, and particularly if that mineral is only at levels of a few percent or less, such as sulphur in pyrite, sodium in albite or barium in barite, then the calculated mode for these minerals is likely to give the best estimate. In the more general

36

P.K. HARVEY E T AL.

case where elements are partitioned between different minerals, a good fit between the input sample chemistry and the chemistry reconstructed from the mineral proportions (Harvey et al. 1990) may provide very good evidence that the solution is close to the 'true' mineral proportions. Similar arguments may be produced for the quality of XRD and M I N E R A L O G solutions under specified conditions. For several of the minerals examined here, close agreement can occur between different methods and it may be postulated that if a number of unrelated methods give essentially the same result for a sample it is likely that that result is close to the 'true' value, though this cannot be proven. This would suggest that for the results described here, quartz, pyrite and (total) carbonate are reasonably accurate, despite a minor bias in the quartz crossplot, and variations between the individual carbonates could be easily reconciled by the use of the correct carbonate mineral compositions. It is significant that these minerals which do give reasonable agreement between widely differing methods are characterized by a small number of cations and restricted compositions which result in well defined XRD and infrared spectra. The variation between the different estimates for the feldspars (K-feldspar and albite) and the clays (illite and kaolinite) is more complex. Both spectral methods (XRD and infrared) suffer from problems of overlapping lines for these minerals and poorer sensitivities (i.e. detection limits) at low concentrations. Likewise, calculations of the modes from the chemical analysis suffer from two other problems. The first is the use of the 'true' chemical composition of each mineral phase used in a model. It has been demonstrated previously (Lofts et al. 1995b) how sensitive a solution can be to changes in compositions of the minerals used in calculations; ideal compositions are rarely appropriate. The actual compositions of the kaolinites, illites and the different carbonate minerals were not known in this study. The second problem is that of compositional co-linearity; that is, where four or more of the minerals to be modelled lie on (or very close to) the same compositional plane (Harvey & Lovell 1992). This leads to essentially an infinity of solutions, or, if forced, a very unstable solution. The variable range of estimates for K-feldspar amongst the computed values almost certainly results from this problem, given that the actual compositions of the clays (which have a number of chemical components in common with the feldspars) are not known.

For minor minerals, such as zircon, apatite and barite, which are definitely known to be present (confirmation generally by petrographic examination) the only reliable method of estimation is calculation. In most samples these minerals were not detected by the spectral methods, but as they contain elements which do not normally occur at significant levels in the other minerals present (Zr in zircon, P in apatite, and Ba in barite), and the chemical analyses can be regarded as both more accurate and precise than the spectral mineral techniques, then accurate estimates may be expected. The same is true for pyrite (constrained by the S content) in the absence of other sulphur bearing minerals such as gypsum or anhydrite. If the chemical compositions of all the mineral phases were known, then their use, together with 'good' chemical analyses should always produce the most accurate modal estimates by calculation using the actual mineral assemblage for a given sample. The mineral compositions, however, are rarely known in sufficient detail and quite erroneous estimates can be made if the wrong compositions are used. In the case history described here, Model A was finally chosen for modelling the geochemical log data following further experiments using a reduced number of components to correspond to those measured by the geochemical logging tool.

Conclusions (1) There was generally good agreement between methods in the estimation of quartz, total carbonate and pyrite. It is reasonable to assume, but cannot be proven, that these estimates are close to the 'true' values. It is significant that those minerals which do show good agreement between widely differing methods have fairly simple and limited compositions (i.e. are stoichiometric). (2) Agreement for the clay minerals and the feldspars is much more variable due to problems of sensitivity and spectral interference for the two physical methods of analysis, and problems of uncertain mineral composition and compositional co-linearity for the computed models. Good agreement is seen between methods for albite, even at a low level, and kaolinite. The results for illite and K-feldspar were comparatively poor and it is considerably more difficult here to judge which figures, if any, are close to the correct values. (3) Despite the problems with illite, excellent agreement is seen between the methods for

THE ESTIMATION OF MODAL MINERALOGY

(4)

(5)

(6)

(7)

total clay content, which is particularly useful for the calulation of shale components in lithofacies modelling from the geochemical logs. Poor agreement between methods results from low sensitivity (especially at low concentrations) and spectral interferences in the X-ray and infrared techniques, problems of compositional co-linearity and uncertainty about the actual compositions of some of the minerals in the calculated modes. If the chemical compositions of all the mineral phases were known, then their use, together with 'good' chemical analyses, should always produce the most accurate modal estimates by calculation using the actual mineral assemblage for a given sample. The mineral compositions, however, are rarely known in sufficient detail and quite erroneous estimates can be made if the wrong compositions are used. Whether 'absolute' accuracy is actually important obviously depends on why he mineralogy log is required; probably not if simply to illustrate diagrammatically the relative variation in a sequence but important if the data are to be used, for instance, for quantitative basin modelling or physical property log estimation. Underlying these comparisons is the fact that the accuracy of any measured parameter is virtually impossible to specify, except in (usually quite unrealistic) limiting cases. Even with this particularly comprehensive dataset no definitive conclusions can be demonstrated concerning the accuracy of different estimates; the analysis of these data does, however, demonstrate the difficulty of obtaining accurate modal estimates. And in this case history there are no real problems of homogeneity or scaling in the estimation process!

The authors would like to thank BP Exploration Operating Company for agreeing to the publication of this work. We would also like to thank Core Laboratories for the provision of the MINERALOG measurements which were made in December 1988.

References ADAM,H. G., HARVILLE,D., MEER, D. & FREEMAN,D. 1989. Rapid mineral analysis by Fourier transform infrared spectroscopy, Society of Core Analysts, Annual Technical Conference Reprints, v. 1 (198%1989), part II, Society of Professional Well Log Analysts, paper SCA-8809, I. ALBAREDE, F. & PROVOST,A. 1976. Petrological and geochemical mass-balance equations: an algo-

37

rithm for least-square fitting and general error analysis. IPGP NS 252, 309-326. ANDERSON,R. & DOVE, R. 1988. The determination of heat flow in a wellbore in the South Eugene Island area of offshore Louisiana: implications for fluid migration and hydrocarbon location in the subsurface. Transactions Spectroscopy and Geochemistry Symposium, Schlumberger-Doll Research, Ridgefield, CT. Paper K. BURRI, C. 1964. Petrochemical calculations based on equivalents (Methods of Paul Niggli). Israel Program for Scientific Translations, Sivan Press, Jerusalem. CHAPMAN, S., COLSON,J. L., FLAUM,C., HERTZOG, R. C., PIRIE, G., SCOTT,H., EVERETT,B., HERRON,M. M., SCHWE1TZER,J. S., LA VIGNE,J., QUIREIN,J. & WENDLANDT, R. 1987. The emergence of Geochemical Well Logging. The Technical Review, 35, 27-35. CROSS, W., IDDINGS,J. P., PIRSSON, L. V. & WASHINGTON, H. S. 1903. Quantitative classification of igneous rocks. University of Chicago Press, Chicago. DovE, R. E. & WILLIAMS, C. F. 1988. Thermal conductivity estimated from elemental concentration logs. Transactions Spectroscopy and Geochemistry Symposium, Schlumberger-Doll Research, Ridgefield, CT. Paper J. FANG, J. H., KARR C. L. & STANLEY, D. A. 1966. Transformation of geochemical log data into Mineralogy using genetic algorithms. The Log Analyst, 37, 26-31. FUH, T. M. 1973. The principal of constituent analysis, with special reference to the calculation of weight percentages of minerals in metamorphic rocks. Canadian Journal of Earth Science, 10, 657-669. GALFORD, J. E., HERTZOG, R. C., FLAUM, C. & GALINDO. G. 1988. Improving pulsed neutron gamma ray spectroscopy elemental weight percent estimates through automatic dimensioning of the spectral fitting process. Society of Petroleum Engineers, SPE 18151, 423-430. HARVEY, P. K. & LOVELL, M. A. 1992 Downhole mineralogy logs: mineral inversion methods and the problem of compositional colinearity. In: Hurst, A., Griffiths, C. M. & Worthington, P. F. (eds) Geological Applications of Wireline Logs IL Geological Society, Special Publications No. 65, 361-368. --, BRISTOW,J. F. & LOVELL,M. A. 1990. Mineral transforms and downhole geochemical measurements. Scientific Drilling, 1, 163-176. --, LOFTS,J. C. & LOVELL,M. A. 1992 Mineralogy logs: element to mineral transforms and compositional colinearity in sediments. 33rd Annual Symposium of the Society of Professional Well Log Analysts, Oklahoma city, Oklahoma. --, Lovell, M. A., Lofts, J. C., Pezard, P. A. & Bristow, J. F. 1997 Petrophysical estimation from downhole mineralogy logs, In." LOVELL,M. A. & HARVEY,P. K. (eds) Developments in Petrophysies, Geological Society, Special Publications No. 122, 141-157. HARVILLE,D. G. & FREEMAN,D. L. 1988. The benefits

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and application of rapid mineral analysis provided by Fourier transform infrared spectroscopy. Society of Petroleum Engineers, SPE 18120, 141150. HASTINGS, A. F. 1988. Using the derived elemental concentrations to improve the accuracy of fluid saturations determined from well logs. Transactions Spectroscopy and Geochemistry Symposium, Schlumberger-Doll Research, Ridgefield, CT. Paper T. HERRON, M. M. 1986. Mineralogy from geochemical well logging. Clays and Clay Minerals, 34, 204213. 1987a Estimating the intrinsic permeability of clastic sediments from geochemical data. SPWLA 28th Annual Logging Symposium. paper HH. 1987b. Future applications of elemental concentrations from geophysical logging. Nuclear Geophysics 1, 197-211. 1988. Geochemical classification of terrigenous sands and shales from core or log data. Journal of Sedimentary Petrology, 58, 820-829. & GRAU, J. A. 1987. Clay and framework mineralogy, cation exchange capacity, matrix density and porosity from well logging in Kern County, California. American Association of Petroleum Geologists, 71, 567 575. HERFtON, M. M. & HERRON, S. L. 1998. Quantitative lithology: open and cased hole application derived from integrated core chemistry and mineralogy data base. In." HARVEY, P. K. & LOVELL, M. A. (eds) Core-Log Integration. Geological Society, Special Publication 136 (this volume). HERRON, S. L. & CHIARAMONTE,J. M. 1993. Impact of element-to-mineral matrix concentration errors on geochemical log interpretation. Nuclear Geophysics, 7, 375-381. HERTZOC, R. C. & PLASEK,R. E. 1979, Neutron excited gamma-ray spectrometry for well logging. IEEE Transactions on Nuclear Science, NS-26, 11581563. , COLSON,L. SEEMAN,B. O'BRIEN, M. SCOTT,H. MCKEON, D. WRAIGHT, P. GRAU, J. A. ELLIS, D. SCHWEITZER, J. ~ HERRON, M. 1987a, Geochemical logging with spectrometry tools. SocieO'of Petroleum Engineers, SPE 16792, 447-460. , SORAN, P. D. ~ SCHWEITZER, J. S. 1987b. Detection of Na, Mg, A1 and Si in wells with reactions generated by 14 MeV neutrons. Nuclear Geophysics, 1,243-248. - - , COLSON,L. SEEMAN, B. O'BRIEN, M. SCOTT,H. McKEON, D. WRAmHT, P. GRAU, J. A. ELLIS, D. SCHWEWZER,J. & HERRON, M. 1989. Geochemical logging with spectrometry tools: SPE Formation Evaluation, 4, 153-162. IMBRIE, J. & POLDERVAART,A. 1959. Mineral compositions calculated from chemical analyses of sedimentary rocks. Journal of Sedimentary Petrology, 588-595. -

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JACOBSON, L. A. & WYATT, D. F. 1996. Elemental yields and complex lithology analysis from the pulsed spectral gamma log: The Log Analyst, 37, 50-64. KERR, S. A., GRAU, J. A. & SCHWEITZER,J. S. 1992. A comparison between elemental logs and core data. Nuclear Geophysics, 6, 303-323. LOFTS, J. C., HARVEY, P. K. & LOVELL,M. A. 1994. A stochastic approach to mineral modelling of log derived elemental data. Nuclear Geophysics, 8, 135 148. & 1995a. Reservoir characterization from downhole mineralogy. Marine and Petroleum Geology, 12, 233-246. &- 1995b. The characterisation of' reservoir rocks using nuclear logging tools: evaluation of mineral transform techniques in the laboratory and log environments. The Log Analyst, 36, 16-28. MATTESON, A. & HERRON, M. M. 1993. Quantitative mineral analysis by Fourier transform infrared spectroscopy, Society of Core Analysts, Annual Technical Conference Proceedings, Society of Professional Well Log Analysts, Paper SCA-9308. MIESCH, A. T. 1962. Computing mineral compositions of sedimentary rocks from chemical analyses. Journal of Sedimentary Petrology, 32, 217-225. NICHOLS, G. D. 1962. A scheme for recalculating the chemical analyses of argillaceous rocks for comparative purposes. American Mineralogist, 47, 3446. ODOM, R. C., STREETER, R. W., HOGAN, G. P. & T~TTLE, C. W. 1994. A new 1.625 inch diameter pulsed neutron capture and inelastic/capture spectral combination system provides answers in complex reservoirs. 35th Annual Logging Symposium Transactions, Society of Professional Well Log Analysts. Paper O. PEARSON, M. J. 1978. Quantitative clay mineralogical analyses from the bulk chemistry of sedimentary rocks. Clays and Clay Minerals, 26, 423-433. RuPP, J., HERTZOG, R. & SCHWEITZER,J. 1989. Using log-derived elemental concentrations in stratigraphic correlation of argillaceous sediments: Third International Symposium on Borehole Geophysics for Minerals, Geochemical and Groundwater Applications. WRIGHT, T. L. & DOHERTY, P. C. 1970. A linear programming and least-squares computer method for solving petrologic mixing problems. Geological Socieo' of America Bulletin, 81, 1995-2008. WYATT, D. F., JACOBSON, L. A. & HASHMY, K. 1993. Elemental yields and complex lithology analysis from the Pulsed Spectral Gamma log. 34th Annual Logging Symposium Transactions, Society of Professional Well Log Analysts. Paper UU.

Interpretation of core and log datauintegration or calibration? M. A. L O V E L L , 1 P. K. H A R V E Y , WILLIAMSON

P. D. J A C K S O N , 2 T. S. B R E W E R , 1 G. 1 & C. G. W I L L I A M S 1

1 Geology Department, Leicester University, Leicester, LE1 7RH, UK 2 British Geological Survey, Key~'orth, Nottingham, NG12 5GG Abstract: Core-log interpretation requires the reconciliation of datasets from different

measurements. Measurement process, resolution, scale and quality must be appreciated for each dataset. Calibration of measurements involves the use of standards to enable quantitative comparisons locally or globally; this may involve inter-dataset comparison and the process of equalization with the modification of one dataset in preference for another. Calibration should not be confused with integration which aims to maximize the information in an optimal manner and may require the selective choice of data. The clear recognition of the aims of the study at the earliest opportunity enables the best choice of strategy from measurement acquisition through to integration. The final interpretation should realize the original aims but must be compatible with all observations. The integration of core and log data represents one of the many attempts to utilize geological data obtained by measurements at different scales. This use of data from different sources involves the reconciliation of different observations which may be inter-related through their inherent property or physical basis (e.g. laboratory and in situ velocities or porosities), or through their similar volumes of interrogation (e.g. porosity and permeability measurements on core plugs). Alternatively the data to be integrated may not be related in either of these ways (e.g. core descriptions and FMS images). Integration involves the reconciliation of such data in a way which is defined by the overall aims and objectives of the study. It may involve the calibration of one dataset through some equalization procedure, whereby one dataset is assumed to be correct. Another scenario is where the two or more datasets are integrated through the selective addition of components to enhance the overall picture of the formation represented both downhole and in the recovered core. These datasets may be multiple measurements of the same physical parameter by different techniques or measurements of completely different parameters, In this latter approach each dataset is respected for both its inherent fundamental nature and scale, both datasets are assumed to be correct, neither dataset is defined as superior in preference to the other, and the interpreter attempts to extract the maximum information from the total data available. Today we are faced with core and log data in increasing quantity and sophistication. Integration of core and log data concerns the combina-

tion of two datasets, which may comprise observations ranging from qualitative through to quantitative, with the aim of providing the best data on which to base our interpretation and hence 'explain the meaning of' our observations. Integration in turn may be defined as 'to find the total value of', and without necessarily implying total amalgamation of all available data in a non-selective manner. Yet there appears to be much concern and considerable effort directed towards deciding which of the two different datasets represents the truth--log or core? In this paper we review the basic principles involved in data acquisition and interpretation through consideration of the measurement process, measurement calibration, and measurement integration. Figure 1 summarizes the problems involved. The measurement itself may be characterized in terms of its scale, resolution and quality. These are functions of the parameter being measured and the measurement environment as well as the selected target. These measurements can then be calibrated, either relatively (locally) or absolutely (globally); if the calibration is simply between the individual core and log measurements then the result may be equalization of values with corresponding loss of total information. I n t e g r a t i o n through reconciliation of the different measurements towards an optimized solution should yield the best interpretation; but this depends strongly on the assumptions involved and these should be directly related to the overall aim of the study. Indeed the best approach to the problem of core-log integration is through judicious choice of interpretation target, careful

LOVELL,M. A., HARVEY,P. K., JACKSON,P. D. BREWER,T. S., WILLIAMSON,G. & WILLIAMS,C. G. 1998. Interpretation of core and log data--integration or calibration? In."HARVEY,P. K. & LOVELL, M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 39-51

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40

M.A. LOVELL E T AL. '

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Fig. 1. Measurement, calibration, and integration. The choice and specification of the measurement should be dictated by the aims and objectives of the study, and should dictate both whether there is a need for calibration and the route to integration. and appropriate selection of measurement techniques including necessary calibration, and optimization using well-defined integration. The aim of this paper is to document fundamental concepts in the context of core-log integration in order to form the foundations for data integration and interpretation. In examining these aspects of integration further we need first to consider the nature of measurement itself.

Measurement Measurements are made to determine the value of some parameter. Unfortunately the parameter we are often interested in (for example porosity) cannot be measured directly but is obtained indirectly through the subsequent processing of raw measurements of a related parameter(s). The relationship between the measured parameter(s) and the derived parameter may have a well-constrained physical basis (for example density estimates from

gamma ray attenuation measurements) or simply an empirical relationship (for example porosity and saturation from electrical resistivity measurements). The model used in relating the measurement to the derived parameter may fall within a wide range of variable sophistication. This may impact on both the accuracy and precision of the derived parameter since even given accurate and precise measurements a model that is too simple may not result in a true representation of the actual derived physical properties. We do not propose to consider the role of mathematical or statistical procedures here (see Moss 1997 for a review of the partitioning of petrophysical data) but do sound a warning note on attempts to define the relationship between different parameters or measurements. Correlation coefficients are often used to support an interpretation of a causal relationship between two parameters, but a high coefficient does not necessarily imply such a causal relationship. Indeed, all too frequently, unrelated parameters are correlated in an attempt to derive some solution from our data. Figure 2 demonstrates such a classic mythical relationship between two unrelated parameters. Even in this example the high correlation is provided through only two of the points and any rigorous testing of the relationship is likely to be invalid. Returning to the subject of measurement we examine three primary aspects below: resolution, scale and quality.

INTEGRATION OR CALIBRATION?

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Fig. 3. Averaged estimates of matrix density at the boundary of a calcite dogger in the North Sea (after Lofts 1993). The shaded area represents the difference between averaged log (line) and actual core estimates (dots).

Resolution Resolution concerns the minimum separation between two features such that the two features can be identified individually rather than as one combined feature (see for example Sheriff & Geldart 1982). In terms of log measurements this relates to the physical separation of two features along the length of the well (usually in a vertical sense assuming a vertical drillhole). With respect to core measurements this definition equally applies, although it may be complicated by the consideration of lateral variations or heterogeneities visible in the core sample. While the concept of resolution is easily described, the strict numerical definition of it varies. Theys (1991) provides a theoretical definition of log vertical resolution: 'The full width at half maximum of the response of the measurement to an infinitesimally short event'. He then includes other non-attributable definitions from elsewhere in the literature: qualitative: vertical resolution is the minimum distance, x such that the logging tool is able to resolve distinct events separated by this distance; quantitative: vertical

resolution is the minimum bed thickness for which the sensor measures, possibly on a limited portion of the bed, a parameter related to the real value of the formation. This latter definition still falls short of ideal since, as Theys (1991) points out, it does not necessarily measure the true value of the parameter concerned for the thin bed. If a logging tool is to measure a parameter and yield a true value for even a limited portion of the bed, then the bed thickness must be at least as large as the vertical resolution. This vertical resolution will depend not only on the tool design, but also on the formation and borehole characteristics. Thus in terms of log measurements the vertical resolution must be as much a local value if it is anything, but this value will constrain the conditions under which the measured value relates to the true value of the individual formation. In terms of interpretation, the true value may not be important except where the aim is to quantify that particular parameter (e.g. porosity or saturation). The matrix (or grain) density log from Thistle Hole 211/18-a50 which penetrates the Brent Group in

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M. A. LOVELL E T AL.

Fig. 4. Comparison of three different imaging measurements on a single core and their inherent sampling volumes. Given the different nature of the measurements the similarity of images is unusual, and this is probably due primarily to the orientation of the fabric perpendicular to the length of the sample and the simplicity of the pore structure.

the North Sea Thistle Field (Fig. 3) shows a 'calcite dogger' which is sharply bound on either side by sandstone. The line is derived from the continuous log measurement, whereas the points relate to specific individual point determinations on core samples. This is the classic 'shoulderbed' effect and the discrepancies are due to the averaging caused by interrogation of a larger volume by the log measurement. In effect, neither estimate of matrix density is wrong: they simply relate to different volumes of rock constrained by the measurement design and thus require slightly different interpretations. Bed resolution is nearly always a problem even with the most finely resolved tools and bed boundary effects are always present. Scale

Differences of scale are evident in terms of the relative dimensions of the measurement itself

(e.g. frequency) and the actual physical dimensions over which the measurement is made (e.g. size and shape). Typically, scale may be defined quantitatively with precise descriptions of the size and shape of the measurement (see for example Clark 1979 or H o h n 1988). It is linked to the measurement technique and hence the design of the tool. Except in isotropic, homogenous media the different aspects of scale will be important and will contribute to the measurement data value. Scale may also be linked to the resolution of the measurement. A simple example of the effect of scale concerns the measurement of porosity on core plugs. Doveton (1994) shows how for two porosity datasets extracted by Baker (1957), respectively from whole core and plugs, the mean values may be the same but the variability of the whole core is less than that of the plugs. There is an apparent rotation of the relationship between the two porosity determinations in which the extremes in the smaller

INTEGRATION OR CALIBRATION? samples are averaged out. Doveton (1994) emphasizes that this core-based example is equally applicable to the different volumes sampled by core and log measurements. As an example of the importance of scale, consider a series of different measurements in the laboratory on a sample of Penrith Sandstone (Fig. 4). In addition, the role of orientation of the measurement is also considered. Orientation becomes important where the rock is not both homogeneous and isotropic (i.e. for most measurement scenarios in nature). In this specific experiment the sandstone is an aeolian deposit, comparable to the Rotleigendes of the North Sea, of Permian age. It is characterized as a clean reservoir-type sandstone with rounded quartz grains, quartz overgrowths, and an absence of clay phases. Generally the quartz overgrowths reduce the porosity and increase resistance to both electrical and fluid flow (Harvey et al. 1995). The three images in Fig. 4 were obtained through the application of measurements at the same spacing to the upper surface of the slab (see Jackson & Lovell 1991; Lovell & Jackson 1991; Harvey et al. 1995). Porosity was measured using image analysis of the essentially 2-D surface visual texture, whilst permeability was determined using minipermeametry measurements which involve transient pressure impulses at point locations, again on the upper surface. Conductivity refers to the electrical conductivity (inverse of resistivity) and was measured by an array of surface mounted potential electrodes with remote electrodes passing a uniform current through the full volume of rock. The three comparable images relate to very different volumes of rock: the porosity data is restricted to the surface, whilst the permeability investigates a hemispherical volume of rock (in homogenous isotropic material); in contrast the conductivity is an average value integrated over a vertically orientated rectangular prism. Given these significant differences in both scale and orientation the images may not always show good correlation although as the figure demonstrates, for the Penrith Sandstone, with its relatively simple structure, there is a reasonable relationship between the different properties. This is in part because the sands that we have studied are relatively uniform, but perhaps more importantly the primary fabric of the samples is perpendicular to the longest axis. In less homogeneous materials the different sample volumes investigated would lead to greater disparity between the images. In this way different measurements may perceive different degrees of homogeneity as a function of the sample volume

43

corresponding to that measurement. Quality

Quality is defined by the precision, accuracy and bias of the measurement (Murphy 1969). A good quality measurement will be characterized by high precision and accuracy, and a zero bias. Precision refers to the closeness of agreement between the results obtained by applying the experimental procedure to a sample several times under prescribed conditions. Accuracy refers to the closeness of the measurement to the true value. Whilst precision may be quantifiable for both the laboratory and downhole measurements, the accuracy is more difficult to assess as the actual (true) values are unknown, and standards (samples for whom specific measurements are assumed to be known as 'correct' within definable limits) are virtually non-existent for the measurements under discussion here. Consequently the true value is an idealized concept and the accuracy is a qualitative concept (Theys & Woodhouse 1994). In this way we are not aware of the correct answer in an absolute sense, and hence any bias present remains unknown. Figure 5 (after Kimminau 1994) illustrates the concepts of accuracy and precision, two terms which are frequently misunderstood or confused. ODP Hole 926B on the Ceara Rise penetrates sediments which are predominantly ooze and chalk, with varying concentrations of nannofossils, foraminifera and clay, together with minor components of iron oxide and sulphides. The variation in CaCO3 in ODP Hole 926B is plotted in Fig. 6. The continuous line is derived from shore-based measurements in which some 70g samples were finely crushed, sub-divided and the major elements determined by X-ray fluorescence (XRF) spectrometry. The carbonate percentage was determined directly assuming that all the calcium measured occurred as calcium carbonate. The individual points are from the shipboard measurements which were obtained from measurement of acid-liberated CO2 and the assumption that this was all bound up in calcite. The precision of this method at one standard deviation is reported as less than 1% (Explanatory Notes, Curry et al. 1995) as is that for the CaO XRF determinations. Thus while the measurement method and the analytical volumes are different for the two datasets, the precision is similar, and a bias may have been introduced by the respective (unknown) sampling strategies. It is impossible to say which of the datasets is correct even though there are significant differences between them.

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M.A. LOVELL E T AL.

accurate but imprecise

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Fig. 5. Schematic illustration of the concepts of accuracy and precision (after Kimminau 1994).

Thompson & Theys (1994) note that quality requires the definition of specified requirements rather than expense or luxury. Thus the definition of the target or aim of the measurement needs to be carefully detailed before we can assess its quality. Straley et al. (1995) demonstrate the importance of defining the aims in considering the use of N M R in partially saturated rocks. In attempting to compare downhole and laboratory measurements they note that mercury porosimetry actually characterizes the pore throat size compared to the N M R T1 which primarily responds to pore body size. Thus the two separate measures of pore dimensions respond to different aspects with consequently different answers.

Calibration Calibration is the process by which measurements are compared with known standards for the purpose of enabling the quantitative comparison of measurements. Thus, calibration requires samples for which supposedly 'true' values are known in order that accuracy may be defined (Ruth & Pohjoisrinne 1993). This calibration procedure may involve recourse to local standards in which case the calibrated measurements may be termed relative. Locally calibrated data can be easily compared and used without knowledge of their relationship to world-wide measurements of the same parameter. These local standards may, in turn, be

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calibrated against national or international standards providing so-called absolute values of measurement which can be related through the same measuring stick. Whilst there is much to be said for global standards enabling comparison of all data for a single parameter, calibration does not necessarily imply truth: inherent bias in the measurement of a systematic nature, in which the measurement effectively measures something other than what was intended (Eisenhart 1962), can simply yield consistency. The importance of calibration in the integration process depends more on the nature of the measurements and how they are to be used. In terms of downhole logging calibration is often confused with other measurement checks. The process of calibration of logging tools concerns the production of a specific signal in response to known measurement values within a formation (Theys 1991); thus the transformation

of the raw measurement to usable values is designated separately as the tool response. Consequently, calibration is based on a log measurement in large volume artificial or natural formations. Theys (1991) also considers that the checking of a logging instrument in welldefined conditions both before and after a logging run should really be labelled verification, whilst the matching of surface electronics to downhole signals could be better described as surface system alignment. This is because neither considers the actual calibration of the data, simply the overall working of the tool within predefined calibration constraints. Core measurement calibration, meanwhile, is well-documented and generally involves the use of standards of a similar scale to the samples under test. These small volume local standards can be readily controlled and related to national or international standards. Skopec (1992), however, notes that whilst laboratory determinations

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Fig. 7. Reconciliation of different measurements of electrical resistivity formation factor (FF) at different scales (the formation factor is the rock resistivity normalized with respect to the resistivity of the saturating fluid). The horizontal bar is from a standard industry minicore measurement, the continuous plots are derived from resistivity imaging. Averaging the high resolution log provides a lower resolution log (dots) which approaches the value of the minicore.

are the standards by which in situ log measurements are compared (e.g. nuclear spectroscopy logs), each method must be examined carefully to determine experimental limitations, accuracy and precision in testing, as well as potential mineral alteration processes that can occur when a rock is sampled. Given a satisfactory understanding of our measurement base we can proceed to analyse the data. Often we are concerned with combining two datasets of the same parameter with the aim of producing one, more complete dataset. In this way core data may supplement sections of log data, or indeed duplicate it. The normal procedure here is to assume that one of the datasets is correct and to adjust the other to create a best fit. This equalization can create better coverage of the total borehole section but will inevitably involve manipulation of at least one dataset and the loss of inherent absolute values. This is a standard approach to so-called core-log integration. Unfortunately it does not consider discrepancies between the two measurement sets created by different measurement

techniques, strategies, or acquisition procedures. R a t h e r it involves the process of relative calibration between small and large volume measurements, typically between core and log. Where datasets do not agree it is important to ask whether the issue is one of data quality or the quality of interpretation (Owens 1994; Harvey et al. 1998). As an example of this process, consider Fig. 7. Here two datasets are shown at different scales, but both relate to measurements on core. The continuous electrical Formation Factor log is derived from the electrical resistivity image as presented in Fig. 4. These data are then compared with the solid bar which is the electrical Formation Factor determined in a traditional manner on a minicore or plug. This was originally done during attempts to calibrate the novel imaging system against industry standards (Jackson et al. 1994). The match between the two datasets is improved visually by smoothing the image data further (dots). The smoothing was carried out with a simple moving average with a window width corresponding to

INTEGRATION OR CALIBRATION? the length of the minicore; in this way the smoothed log (dots) is effectively a stepwise integration over the image. As with the CaCO3 estimates shown in Fig. 6, neither dataset is incorrect: both have supporting calibration data referenced to standard materials, but each dataset provides the interpreter with a different perspective of the sample, effectively a different representation of the truth. The image data provides fine detail relating to the structure whilst the minicore provides an average value (though not a simple arithmetic or statistical average). Through correct averaging of the high resolution log there is a remarkable match with the minicore measurement at a similar resolution. A related problem occurs when we are trying to predict petrophysical properties from unrelated logs. Often we derive statistical models or empirical relationships which have no physical foundation but which satisfactorily estimate the parameter of interest at each log depth. Effectively we calibrate our model or algorithm to give answers which are compatible with laboratory or borehole experiments of an unrelated nature. These data demonstrate that the problems of data integration are present at all scales, and whilst this contribution refers explicitly to log and core data the principles remain true for integrating these data with smaller scale (thin section, SEM) and larger scale (VSP, seismic reflection) data.

Integration Integration involves the reconciliation of datasets with or without the equalization involved in calibration procedures. Often this will include the selective addition of data. Different datasets may relate to the same measurement, the same scale, or either or both of these attributes may be different. The overall aim of integration is to maximize the i n f o r m a t i o n available in an optimal manner. Towards this aim, the objective is not simply to compare data but to constrain and characterize some geological process or effect. The effects of sample size and tool resolution in core-log integration is easily demonstrated by a simple Monte Carlo experiment which could go some way to explaining the variation seen in the CaCO3 estimates shown in Fig. 6. In this particular experiment (Fig. 8) a 30 m section of oceanic sediment, with basaltic lava flows, was simulated to evaluate the suitability of core measurements as guides to the accuracy of geochemical log measurements. The section,

47

Fig. 8. Comparison of core and log data simulated for ODP Sites 792 and 793 (Ocean Drilling Program Leg 126) demonstrating the integration of measurements at different resolutions.

based on ODP Sites 792 and 793 in the region of the Izu Bonin Arc (Lovell et al. 1992), was simulated in three steps: (1) generation of a lithological sequence with abundances and thicknesses of simulated units corresponding to the appropriate distribution obtained from core logging; (2) generation of the rock chemistry at 1 cm intervals throughout the section, preserving the average and variance/covariance relationships of the chemistry for each lithology; (3) sampling of the simulated chemical sequence. In Figure 8 this simulated dataset is sampled for alumina (a) as it would respond to the geochemical logging tool (GLT) by averaging over a 60 cm window and reporting measurements every 15 cm to provide the continuous log curve, and (b) as a set of core plug results obtained by randomly sampling a small number of the total 3000 simulated compositions to provide the

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individual point measurements. Both datasets present different perspectives of the same chemical sequence; neither is wrong and consequently neither should be rejected in favour of the other. Thus the integration of different measurements of the s a m e parameter can provide both overall and detailed geological information. Hornby et al. (1992) used downhole electrical images, reflected Stoneley waves and core observations to deduce estimates of fracture apertures. They thus used different observations to comment on fracture extent and connectivity as well as borehole enlargement and rugosity. Furthermore, they point to the use of information obtained at different scales as being the key to further work aimed at fracture quantification. Core-log

interpretation

Figure 9 shows the stratigraphy of ODP Hole 896A, which was drilled in the Equatorial East Pacific as part of ODP Leg 148 (Alt et al. 1993). With the drilling of Ocean Drilling Program Hole 896A, two deep basement holes (i.e. Holes 504B and 896A) now penetrate oceanic crust formed at the Costa Rica Rift. Hole 504B, the deepest basement hole in oceanic crust so far drilled (2100m), is located approximately 200 km to the south of the Costa Rica Rift, in 5.9 Ma old crust. Hole 896A is located approximately 1 km to the south of Hole 504B in crust ,-~2.8x 104 yr older than at Hole 504B. No attempt was made to recover the sedimentary cover in Hole 896A and the position of sediment/basement interface was based upon rubble being felt by the drill bit at 179 mbsf (metres below sea floor) and the hole was cored from 195.1 mbsf to 469 mbsf (Alt et al. 1993). Within this drilled section, core recovery averaged 26.9%. Pillow lavas (57%) and massive flows (38%) dominate the cored material, with breccias (5%) and two small dikes accounting for the remainder of the material. With the exception of pillow rims, the majority of the rocks are slightly altered (< 10%) and variably veined (Alt et al. 1993). Pervasive background reducing alteration coupled with saponite and minor pyrite replacement of olivine has led to the grey colour of the core. Oxidative alteration is manifested by dark grey to yellow and red alteration halos which commonly occur around smectite veins (Alt et al. 1993). In the pillow lavas and massive flows, veins are usually < 1 mm in thickness and commonly infilled by dark and light green saponite and aragonite. Other vein minerals include analcite, fibrous zeolite and pyrite. All of the previous data were

Fig. 9. Stratigraphy derived from core recovery based on core barrel descriptions, compared with stratigraphy and based on downhole electrical FMS images and core observations.

based on an overall poor core recovery (26.9%, Alt et al. 1993), which is also very variable within individual sections of the borehole (Fig. 9). Shipboard scientists produced the lithology shown in Fig. 9 based solely on visual observations of recovered core. In contrast, shore-based scientists (Brewer et al. 1995) have produced a comparable stratigraphy based on the variations in texture of the downhole Formation Micro Scanner (FMS) Images together with sonic, resistivity, and gamma ray logs. The FMS tool produces images of the borehole wall dependent on variations in the measured electrical resistance and these images can be analysed texturally to develop a log-based stratigraphy with reference to the core. As Brewer et al. (1995) demonstrate, there are substantial differences between the stratigra-

INTEGRATION OR CALIBRATION? resistivity (ohm-m)

smoothed pixel value

mean pixel value

49 permeability (roD)

porosity

(%)

i j

\

e

0 -4 o , .t-_.

i

........ 9 JO

.... 0"2 4 O. . . . . . . Ik

-

O" --~-e ....

e

e-..t

--|ii. 11

"lP.

e.. i~ 9i:e

e r e ....

:,

o

o

.!

ie

k 3

4

5

165

175

185

160

170

180

0

e

2000

4000

0

i 2

k 20

~

i 40

Fig. 10. Electrical resistivity data and optical data for an aeolian sandstone. There is a remarkable correspondence between the two datasets for this clean sandstone, yet the raw optical data provide a higher resolution dataset than the resistivity data, enabling inference of the fine-scale resistivity structure of the sample. phy derived solely from core and that derived by integrating core and log information. Here the problem may initially be seen as one of constraining the downhole data through selected core observations, knowing that the recovered core is present in the drilled section, and utilizing the downhole data to extend the interpretation to the full depth of the hole. However, the core data are inherently biased, due possibly to preferential sampling of some lithologies, incomplete recovery, and the defined criteria and procedures used to identify and extrapolate recovered material over the total depth drilled. Thus whilst the core does indeed represent the truth, its allocation to a particular lithology, distribution with depth and continuity may be questioned. In contrast the FMS images are relatively new and lack precise calibration in terms of textural detail and lithological responses. They are usually continuous and are based on electrical, not visual, properties which may or may not be equable, and whose equality may vary within the hole. These images proba~ contain bias in addition to that within the core. Thus, rather than accept one dataset in its entirety as the truth and reject the other, it would be better to use the ground-truth of the recovered and described core as calibration points for the interpretation of the downhole images. This would ideally include the measure-

ment of electrical textures on recovered core for comparison with downhole images, thus constraining in a quantitative manner the interpretation of the downhole data. A different example of the integration of data from different sources is shown using the electrical conductivity image from Fig. 4 converted to a resistivity image (by taking the inverse of each plotted value). In Fig. 10 this resistivity image is converted into a microresistivity log by averaging across each row of values. Similarly, the porosity and permeability images are displayed as averaged micrologs. These logs again show the variable nature of the formation but could easily be incorporated into a petrophysical analysis routine for producing improved estimates of both fluid volume and flow parameters. Here the photographic image has been converted to a pixel log, again by row averaging; this micro-log is then smoothed to a similar resolution to the electrical micro-log. There is an inverse correspondence between the two which suggests the use of photographic images for comparison with electrical images in clean sandstones. Where clay minerals may contribute to conduction processes, the rock is contaminated with mud, there are clay-filled fractures, or the pore fluid is resistive (e.g. fresh water), the correspondence between photographic and electrical images may not be as

50

M. A. LOVELL ET AL.

Hole 896A from FMS images. Scientific Drilling, 5, 87-92. CLARK, I. 1979. Practical Geostatistics. Elsevier, London. CURRY, W. B., SHACKLETON,N. J., RICHTER, C. & the Shipboard Scientific Party. 1995. Proceedings. ODP Initial Reports., 154: College Station, TX Summary (Ocean Drilling Program). (1) Interpretation of core and log data should DOVETON, J. H. 1994. Geological Log Analysis Using Computer Methods. American Association of involve consideration of the measurement proPetroleum Geologists, Computer Applications in cess, calibration, and integration. Calibration Geology, No.2. and integration may not necessarily be included EJSENHART, C. 1963. Realistic evaluation of the in the first interpretation. precision and accuracy of instrument calibration (2) The measurement itself is defined in terms systems. Journal of Research of the National of its scale, resolution and quality. These are Bureau of Standards--C, Engineering and Instrufunctions of the parameter being measured and mentation, 67C, 21-47. (Paper 67C2-128). the measurement environment as well as the HARVEY,P. K., BREWER,T. S., LOVELL,M. A. & KERR, selected target. It is imperative that these S. A. 1998. The estimation of modal mineralogy: a problem of accuracy in core-log calibration. This attributes are considered in any data integration volume. exercise. LOVELL, M. A., JACKSON, P. D., ASHU, P. A., (3) Measurements can be calibrated, either - WILLIAMSON, G., SMITH, A. S., BALL, J. K. & relatively (locally) or absolutely (globally). CauFLINT, R. F. 1995. Electrical resistivity core tion is essential since often this falls to interimaging III: characterisation of an aeolian sanddataset comparison, and involves equalization stone. Scientific Drilling, 5, 165 176. which through the modification of one dataset in HOHN, M. E. 1988. Geostatistics and petroleum geology. preference for the other may negate additional Van Nostrand-Reinholt, New York. benefits which i n t e g r a t i o n may otherwise HORNBY, B. E., LUTH1, S. M. & PLUMB, R. A. 1992. Comparison of fracture apertures computed from achieve. electrical borehole scans and reflected Stoneley (4) Differences of scale in measurement sets waves: an integrated interpretation. The Log may highlight geological features through variaAnalyst, 33, 50-66. tions in the degree and nature of formation JACKSON, P. D. & LOVELL, M. A. 1991. The correheterogeneity. spondence of electrical current and fluid flow in (5) Integration of data should maximize, in rocks--the impact of electrical resistivity core an optimal manner, the information available. imaging. Transactions 14th European Sympo(6) The best c o r e - l o g i n t e r p r e t a t i o n is sium, Society of Professional Well Log Analysts, through judicious choice of objectives, approLondon, UK. Paper J. 9HARVEY, P. K., BALL, J., WILLIAMS, priate selection of measurement process, includC.I FLINT, R. F., ASHU, P. A. & MELDRUM,P. I. ing necessary calibration, and optimization 1994. Advances in resistivity core imaging. using carefully-defined integration. This should Transactions 35th Symposium, Society of Profesrealize an interpretation which maximizes the sional Well Log Analysts. Paper GG. use of available data whilst remaining compaKIMMINAU, S. 1994. Traceability--making decisions tible with all observations. with uncertain data. The Log Analyst, 35, 67-70. LOFTS, J. C. 1993. Integrated geochemical and geophysical studies of sedimentary reservoir rocks. PhD We thank the Natural Environment Research Council thesis, University of Leicester. for support through research grant GST/02/684, LOVELL, M. A. & JACKSON, P. D. 1991. Electrical flow together with Z & S Group for provision of software in rocks: the application of high resolution for the processing and interpretation of FMS data. electrical resistivity core measurements, paper WW in 32nd Annual logging Symposium Transactions: Society of Professional Well Log AnaReferences lysts, Midland, Texas. , PEZARD, P. A. & HARVEY, P. K. 1992. ALT, J. C., KINOSHITA, H., STOKKING, L. B. & the Chemical stratigraphy of boreholes in the IzuShipboard Scientific Party. 1993. Proceedings. Bonin Arc from insitu nuclear measurements. ODP Initial Reports, 148. College Station TX Proceedings of the Ocean Drilling Program, (Ocean Drilling Program). Scientific Results, 126, 593-601. BAKER, P. E. 1957. Density logging with gamma rays. Moss, B. 1997. the partitioning of petrophysical data: Petroleum Transactions of the American Institute a review. In: LOVELL,M. A. & HARVEY,P. K. (eds) of Metallurgical Engineers, 210, 289 294. Developments in Petrophysics, Geological Society BREWER, T. S., LOVELL,M., HARVEY,P. & WILLIAMSON, Special Publications, No. 122, pp 18l 252. G. 1995. Stratigraphy of the ocean crust in ODP

reliable, nor as predictable. Thus discrepancies between optical and resistivity images or logs may also yield information about the nature of the pore space.

INTEGRATION OR CALIBRATION? MURPHY, R. B. 1969. On the meaning of precision and accuracy. In. Ku, H. H. (ed.) Precision Measurement and Calibration. Statistical Concepts and Procedures. United States Department of Commerce National Bureau of Standards Special Publication 300, 1, 357-360. OWENS~J. 1994. Fit-for-purpose data during field life. The Log Analyst, 35, 58-60. RUTH, D. & POnJOISRINNE, T. 1993. The precision of grain volume porosimeters. The Log Analyst, 34, 29-36. SHERIFE, R. E. & GELDARX, L. P. 1982. Exploration Seismology volume 1. history, theory and data acquisition. Cambridge University Press, Cambridge. SIES, H. 1988. A new parameter for sex education.

51

Scientific Correspondence, Nature, 332, p.495. SKOPEC, R. A. 1992. Recent advances in rock characterisation. The Log Analyst, 33, 270-285. STRALEY, C., MORRISS, C. E., KENYON, W. E. HOWARD, J. J. 1995. N M R in partially saturated rocks: laboratory insights on free fluid index and comparison with borehole logs. The Log Analyst, 36, 40-56. THEYS, P. P. 1991. Log data acquisition and quality control. Editions Technip, Paris. THEYS, P. & WOODHOUSE, R. 1994. Society of professional well log analysts topical conference on quality, appendix: metrological definitions. The Log Analyst, 35, p. 71. THOMPSON, B. & THEYS, P. 1994. The importance of quality. The Log Analyst, 35, 13-14.

Estimation of measurement uncertainty for in situ borehole determinations using a geochemical logging tool M. H. R A M S E Y , P. J. W A T K I N S

& M. S. S A M S 1

T. H. Huxley School of Environment, Earth Science and Engineering, Imperial College o f Science Technology and Medicine, London S W7 2BP, UK 1 Present address." Petronas Research and Scientific Services S D N BDH, Hulu Kelang, 54200 Selangor, Malaysia. Abstract: Methods for the estimation of measurement uncertainty are discussed with

particular reference to concentration measurements made by a geochenmical logging tool (GLT; Mark of Schlumberger) in a borehole penetrating a cyclothem sequence at Northumberland, UK. Two components of uncertainty have been quantified for 6 elements determined by the GLT over a wide range of concentrations. The random component was estimated from duplicated determinations within this borehole over a 120 m depth interval. These uncertainty values ranged from 2.7% for Si to 71% for S, expressed at the 95% confidence limit. The systematic component of the uncertainty was estimated by determinations made on corresponding core samples by inductively coupled plasma atomic emission spectrometry (ICP-AES) over a 40 m depth interval. The ultimate basis for this estimation of bias was the certified reference materials which were analysed during the ICPAES determinations. The bias measured by this method was typically in the range + 5% to - 1 4 % for 5 out of the 6 elements determined. This method assumes that the samples analysed by both techniques are physically comparable. By depth averaging the ICP-AES determinations it was possible to reduce errors due to differences in sample size. However, a possible source of bias that was recognised is that samples were dried before ICP-AES determinations, whereas this was not the case for in situ GLT measurements. Such variability in the size of the systematic component of the uncertainty prevents the effective correction of this term as is recommended by the International Standards Organisation. The large values of measurement uncertainty found for some elements (e.g. S) will exert limitations on the geochemical interpretations made from the GLT measurements, in terms of 'fitness-for-purpose' criteria.

Uncertainty of measurements made in boreholes by an 'in situ' geochemical logging tool (GLT) can have p r o f o u n d effects on the realistic interpretation of the geochemical variation across a stratigraphic sequence. Although the importance of the uncertainty is becoming apparent, methods for the estimation of such uncertainty are lacking. Broadly similar studies on the estimation of bias and precision of GLT measurements have been reported (Wendlandt & Bhuyan 1990; Grau et al. 1990), but these did not attempt any rigorous mathematical estimation of the total uncertainty of measurement. For analytical measurements in isolation, the realistic estimation of uncertainty has already become an important issue (ISO 1993a; Eurocheni 1995; A M C 1995). In contaminated land investigations it has recently been shown that it is field sampling, rather than the chemical analysis, that can contribute the largest source of measurement error and will therefore limit the measurement uncertainty (Ramsey et al. 1995a).

Methods have been developed for the quantification of the errors arising from the sampling of one site, by either a single or multiple samplers. For a single sampler, the methods have been applied both to the use of a single sampling protocol (Ramsey 1993) or comparisons between several protocols (Ramsey et al. 1995b). For the case of multiple samplers, different m e t h o d s have been devised d e p e n d e n t on whether all samplers were applying the same protocol (Ramsey et al. 1995a) or different protocols (Argyraki et al. 1995). Applications of these methods were made for the estimation of heavy metals on contaminated land, but the methodologies are equally applicable, in principle, to the measurement of elemental variation within a borehole using a GLT. This paper considers how estimates of measurement uncertainty can be derived, particularly for the case of a single sampler using a single protocol, utilizing a previously published case study (Sams et al. 1995). The objectives of

RAMSEY,M. H., WATKINS,P. J. & SAMS,M. S. 1998. Estimation of measurement uncertainty for in situ borehole determinations using a geochemical logging tool In: HARVEY,P. K. 8~; LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 53-63

53

M. H. RAMSEY ET AL.

54 the work are therefore:

(l) to make the best estimate of uncertainty on the measurements from the case study, as an example of the general approach; (2) to identify additional methodologies that could be applied to give improved estimates of uncertainty; (3) to consider briefly how to identify acceptable levels of uncertainty for particular objectives of interpretation.

Definition and terminology of measurement uncertainty The formal definition of uncertainty has been given as: 'A parameter associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measurand (ISO 1993a). A less formal but more understandable description of measurement uncertainty is that it is 'an interval a r o u n d the result of a measurement that contains the true value with high probability' (Thompson 1995). The standard uncertainty 'u' can be considered equivalent to one standard deviation which is often used to describe a normally distributed random error. The expanded uncertainty 'U" is equal to the product of the standard uncertainty and a coverage factor 'k', which typically has a value of 2 or 3. This is analogous to the use of multiples of standard deviation for the quantification of precision. In the formal definition, systematic errors are not included in estimates of uncertainty, but only any residual random errors left after correction of the systematic errors. However, in this application, the distinction between systematic and random errors becomes blurred. The systematic error of one sampler becomes a random error when assessed as part of a multisampler comparison and systematic errors thereby become incorporated into estimates of uncertainty.

Estimating uncertainty in chemical analysis The methods recently developed for estimating uncertainty in chemical analysis in the laboratory are of two types. They both need to be evaluated as options for the application to in situ borehole sampling and analysis. In the 'bottom up' approach the random error from each individual component of a method is quantified separately as a standard deviation (s). The overall uncertainty is then estimated by summing the individual errors by their variances (s 2)

(Eurochem 1995). The alternative 'top down' approach uses inter-laboratory trials to estimate the total uncertainty of a measurement. In this method, many selected laboratories ( n > 8 ) would all analyse the same sample by the same analytical method (AMC 1995). The scatter of all reported measurements is then used as an overall estimate of uncertainty. The 'bottom up' approach has the limitation that it requires all of the sources of uncertainty to be identified. It is relatively easy to consider the obvious sources of error which are explicit parts of a laboratory method (e.g. weighing, volumetric additions). However, the most important source of uncertainty may not be explicit in the method (e.g. lab. temperature), and is therefore easily overlooked, especially by inexperienced practitioners. Furthermore, it can be a long and expensive procedure to quantify all the component errors, if the method is to be applied rigorously. The benefits of the 'top down' approach can be appreciated from the differences that are often evident between laboratories in interorganizational trials. These differences are often larger than can be accounted for by the individual estimates of uncertainty within each laboratory (AMC 1995). This is because the ' b o t t o m up' a p p r o a c h used by individual laboratories tends to give over-optimistic estimates of the uncertainty. The limitation of the 'top down' approach is that it depends on the selection of the laboratories that contribute. If the laboratories all use a similar source of calibration, they may all be equally biased and therefore give an under-estimate of the uncertainty. Alternatively, one laboratory may have gross errors, atypical of the application of the method as a whole, and this will cause an overestimate of the uncertainty.

Estimating uncertainty for in situ borehole measurements There are two primary limitations in estimating uncertainty of measurements made in situ in boreholes, using the method described for purely laboratory based analytical systems. One problem is that these methods ignore the uncertainty arising from field sampling. It is often quoted that an analysis can never be of better quality than the sample upon which it is made. What has been lacking, however, is the means of estimating the size of the uncertainty which is introduced by field sampling. A second limitation is in the quantification of systematic errors, either from sampling or from chemical analysis.

ESTIMATION OF MEASUREMENT UNCERTAINTY It is proposed to adapt the methods devised originally for estimating analytical uncertainty to the estimation of uncertainty from in situ measurements by addressing these two limitations. With in situ measurements it is useful to consider field sampling and chemical analysis as just two parts of the same 'measurement' process, and to quantify their combined contribution to the uncertainty. Such 'total measurement' uncertainty has therefore four potential components. These are the sampling and analytical contributions to random error (i.e. sampling and analytical precision) and any uncorrected systematic errors (i.e. sampling and analytical bias). Taking the 'bottom up' approach to estimating the total measurement uncertainty we can review the methods available for the estimation of these four components. Analytical precision can be measured by the use of analytical duplicates (Thompson & Howarth 1976) or in combination with sampling precision using a balanced design of sampling and analytical duplicates (Garrett 1969; Ramsey et al. 1992). This is possible for the GLT although, ideally, duplicate sampling would require the use of a second borehole close to the first and assuming lateral homogeneity. Analytical bias is usually estimated by the analysis of certified reference materials (Ramsey et al. 1987), but this approach would be problematic for the GLT. In addition, there are no methods in general use for the estimation of sampling bias. For absolute bias this may require the introduction of reference sampling targets, analogous to reference materials for the estimation of analytical bias (Thompson & Ramsey 1995). Sampling bias has already been estimated however, between the concentration estimates made by the application of different sampling protocols at one site (Ramsey et al. 1995b). This approach does not, however, give bias against an 'accepted reference value', as defined by ISO (ISO, 1993b). Taking the 'top down' approach, it should be possible to use measurements from inter-organizational sampling trials, such as sampling proficiency tests (Argyraki et al. 1995) and collaborative trials (Ramsey et al. 1995a) to estimate uncertainty. The potential advantages and practical feasibility of such an approach to in situ borehole determinations will be considered below.

Experimental details of the GLT case study The details of the case study using the GLT at the Imperial College borehole at Whitchester

55

farm in Northumberland have been given elsewhere (Sams et al. 1995). In brief, GLT measurements were made on a 40m length of borehole drilled through a single Namurian cyclothem, with varied lithologies (Fig. 1). The GLT has been described in detail by Hertzog et al. (1987) and uses three different measurement techniques: (i) The natural radioactivity of K, Th and U is used for their determination with a N a t u r a l G a m m a Ray Spectrometer (NGS; Mark of Schlumberger). The count rates obtained are directly proportional to the mass per cent of the element, providing a borehole correction factor is applied. (ii) A neutron source of 252Cf, emitting neutrons at about 2.3MeV is used to activate A1, which is determined using an AACT; Mark of Schlumberger. Results obtained are proportional to the weight per cent of A1 after an environmental correction is applied. (iii) After neutron capture from a burst of 14 MeV neutrons, Si, Fe, Ca, Ti, S and Gd are determined using a tau-gated thermal neutron capture spectrometer with a Gamma Spectrometer (GST; Mark of Schlumberger). This procedure only provides relative concentrations of these elements, and they have to be converted to absolute values by imposing a closure relationship on the results obtained. An oxide closure relationship is imposed on the results, in order to derive element concentrations from the raw GLT data It is assumed that each element occurs as a single oxide or carbonate in the formation and that the sum of the oxide and carbonate fractions is unity. This assumption is known to be in error, but it is considered that the errors involved will be small (< 5%) for most lithologies. The equation to be solved is (Hertzog et al. 1987): F(~,Xi Yi/Si) + XK WK -[- XA1WA1 = 1.0

where: F is a calibration factor to be determined at each depth point; Yi is the fraction of the measured prompt gamma rays attributed to element i; Si is the tool sensitivity for element i; Xi is the ratio of the mass of the associated oxide or carbonate to the mass of element i. The mass fractions of potassium and aluminium (WK and WA1) must be first corrected for

56

M.H. RAMSEY ET AL.

Fig. 1. Variation with depth of concentrations of Si and Fe determined by the GLT (solid lines) and by ICP-AES (solid circles). Plot (a) represents original data and plot (b) represents ICP-AES data depth-averaged (smoothed) using a 1.4m square window. Sulphur was not determined by ICP-AES but demonstrates that the coal bands (shown at 150 and 154.3m in the stratigraphic log) can be detected with by the GLT even with an estimated random error of 71%.

porosity to give a dry mass per cent. This is normally determined from the density log by assuming a quartz matrix. Duplicate field measurements were taken at the site in Northumberland by lowering the GLT twice through a section 110-230m in depth. On one occasion all raw measurements were processed through the oxide closure to give elemental concentration, but on the second occasion the raw results were initially unprocessed. In order to achieve comparability, the original dataset was divided by the corresponding raw counts for each element at a particular depth. This procedure gave a value for the factor FXi/Si, and both F and Si should be constant at a constant depth. If the raw counts for an element taken from the duplicate dataset are now multiplied by the corresponding value of FXi/Si, an estimate of the absolute value for that element at a particular depth can be made. This procedure is not quite the same as comparing data obtained from two runs processed independently.

Sampling and analysis of core samples Core sampling was aimed at investigating the vertical variations in bulk rock geochemistry

and mineralogy over a single sedimentary cycle using the same depth interval as the GLT (140180 m). Exact correspondence of the volume of rock analysed by both GLT and ICP-AES is clearly impossible (Fig. 2). The rationale behind the sub-sampling of the core used for analysis is that it should characterize the geochemistry of the core at small intervals (e.g. 25cm), so that subsequent mathematical smoothing of results can be employed to approximate the sampling volume analysed by the GLT (110,000 cm3). The inevitable sampling bias introduced by lithologies of widths less than 25cm (e.g. iron rich sideritic nodules) can then be estimated and recognized as a systematic difference between the two techniques. Discrete samples of about 30 g were selected with an average vertical spacing of 25 + 5 cm between samples, the rock chips being taken to represent the full lateral heterogeneity of the core sample. A total of 147 analytical specimens were collected over the entire depth interval and powdered in an agate Tema Mill to less than 75 #m. The details of the analytical procedure have been given elsewhere (Sams et al. 1995). In brief, 0.25g of dry rock powder was totally decomposed by fusing with LiBO2 and dissolving the fused bead in dilute HNO3, with six elements

ESTIMATION OF MEASUREMENT UNCERTAINTY

57

Sampling design

Estimation of random component

25:1:5 cm

The analytical precision of GLT measurements was characterized as a function of concentration using the method using duplicated analyses devised by Thompson & Howarth (1976). The mathematical model of precision was: sc = So + Pc

ca. 60cm

/

/

/

/

// /

/

/

z

/

/

/

/

/ /

20cm diameter borehole for GLT analysis

Corresponding core, with cut sections used for ICP-AES analysis

Fig. 2. Sampling designs for the measurements by GLT and by ICP-AES.

(1)

where so=standard deviation at a particular concentration c; So= standard deviation at zero concentration; c = concentration; 0 = slope factor, related to the high level precision and so, So and c all have the same units of concentration. The random component of the measurement uncertainty, using a coverage factor of two, for zero concentration is given by: Uo = 2s0

being determined by inductively coupled plasma-atomic emission spectrometry (ICP-AES). This instrument was calibrated using 10 international reference materials, (BR, GA, GH, NIM-N, SY-2, UB-N, NIM-G, DTS-1, AN-G and MICA-MG). The certified values for the reference materials are given in Govindaraju (1994) and thus, these act as the traceability of the determinations to 'accepted reference values', as required by ISO (ISO 1993b) for the estimation of bias. The bias between measured and certified values of the reference materials for the 6 elements studied was generally less than 1 %. Values for the precision of analyses of these six elements in silicate rocks by ICP-AES are reported as < 0.5% in Ramsey et al. (1995c).

and for a particular concentration c by: Ur = 2sc Thus, multiplying equation (1) by the coverage factor of two we get: Ur = Uo -+-20c

(2)

The upper limit of the uncertainty on an estimation of concentration c, from equation (2) is given by: c+ Ur=c+ 20c+ Uo

c+ Ur : C(1 + 2 0 ) + Uo

(3)

A useful way of expressing uncertainty is as a relative percentage, given by:

Methodology for estimation of uncertainty in GLT measurements The two components of measurement uncertainty considered initially are the analytical precision (for random error) and the analytical bias (for systematic error). The role of the random component is explicit in the ISO guide (ISO 1993a), but the systematic component for a particular technique such as GLT analysis has to be estimated independently. Whether this latter component is used to correct the concentration estimates, as suggested by ISO, or added into the uncertainty estimate will be discussed below.

Ur% = 200Sc/C

At high levels of concentration where Ur>>Uo, this can be expressed as: Ur% = 2008

(4)

This value of Ur% is approached asymptotically at high concentration. Substituting for 0 from equation (4) in equation (3) we get:

c+ Ur=c(l + Ur%/lOO)+ Uo

(5)

58

M.H. RAMSEY ET AL.

Table 1. Estimates of uncertainty in concentration measurements made using the GLT, in the specified ranges of

concentration. Element

Range (mass-%)

Si AI Fe Ca K Ti S

044 0-12.5 0-13 0-38 0.5 2.8 0.1-2.7 0.6-5

Random Error, Ur %

Uo (mass-%)

2.7 7.6 12.1 5.0 n.d. n.d. 71

1.80 0.62 0.18 1.28 0.48 n.d. 0.56

Translational Bias, (mass-%) - 1.53" 1.12" 0.34* 0.38 0.20* 0.003 n.d.

Rotational Bias, % 3.5 - 10.1" - 54.6* 4.3* - 13.6 11.1 n.d.

n.d. = not determined. * Bias value significantly different from zero (p = 0.05). Table 2. Estimation of detection limit and precision of

GLT determinations (Sams et al. 1995). Element

Detection limit (mass-%)

Si A1 Fe Ca K S

2.70 0.92 0.26 1.92 0.71 0.56

High-level Precision,% (Is) 1.35 3.83 6.05 2.51 n.d. 35.45

n.d. = not determined These two components of random uncertainty (Table 1) were estimated for 6 elements (Si, A], Fe, Ca, K and S) by using a dataset consisting of 600 duplicate borehole measurements taken over a section between l l 0 m and 230m in depth. This method also provides standard errors on these uncertainty estimates, which can be used to check whether the estimates are statistically greater than zero. When the So value is not significantly greater than zero, it is still possible to calculate a maximum value for Uo, that is equivalent to a maximum method detection limit ( M M D L ) (Ramsey et al. 1995c). The precision and detection limits of the G L T determinations are listed in Table 2. Data used for calculations made in this paper is represented graphically in Fig. 3, and a summary of the duplicate data used to determine the precision of G L T measurements is given in Fig. 4 in Sams et al. (1995). The individual data sets obtained from ICP-AES and G L T measurements are available, but too numerous to include in full here. They are available from the Geological Society if required. The variations of uncertainty due to analytical precision for Si and Fe are plotted against concentration (c) in Fig. 3A, to exemplify the

general relationship for all elements studied. The same results are transformed into the form of relative uncertainty (Ur%) in Fig. 3B. For Si, there is a significant increase in uncertainty with concentration, but when converted into relative uncertainty at the high level, this was the best of any element at 2.7%. The uncertainty at zero concentration for Si was 1.8 mass %, which is well below most of the samples from this borehole. Hertzog et al. (1987) do not give a standard deviation at zero mass% but give precisions of 25.6% and 4.9% at 25 mass% Si and 47 mass% Si, respectively. For Fe, considering these random errors in isolation gives a relative uncertainty of 12.1% at high concentration levels, and an uncertainty of 0.16 mass% at zero concentration. The bias detected for this element, discussed below, will however cause this latter estimate to be multiplied by a factor of 2.2, giving 0.35 mass%. Hertzog et al. (1987) quote a precision of 30% at 1 mass% Fe and one of 16% at 5 mass% Fe, which appear broadly comparable. For S, the high level relative uncertainty was found to be over 70% at all concentrations, the highest for any element determined. This may be due to the proximity of sulphur concentrations in the samples to the detection limit of the GLT. It is interesting to examine what limitations such a degree of uncertainty places on the geochemical interpretation of the concentration estimates (see below). Estimation of systematic component

A qualitative estimate of the analytical bias of the G L T measurements was made using X-ray fluorescence analysis on a limited selection of core material by Hertzog et al. (1987). The quantitative estimation of bias needed for uncertainty estimation has been reported by

ESTIMATION OF MEASUREMENT UNCERTAINTY

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comparing the logging results with values obtained from core samples using ICP-AES (Sams et al. 1995). The method employed used simple linear regression to determine the rotational and translational components of bias. Translational bias occurs when the intercept of the regression, line is statistically significantly different from zero. Rotational bias occurs when the slope of the regression line is statistically different from unity. The test for significance used in this study was the student t-test with a probability of 0.05. Potential limitations in this general technique (Thompson 1982) have been shown not to be significant in this case (Sams et al. 1995).

Before comparing results obtained using the two techniques the question of sample size must be addressed (Fig. 2). Samples collected for laboratory analysis represent a depth of approximately 1 cm. Overall, the GLT responds to an approximately spherical volume of rock with a radius of about 30cm, although each measurement technique used, i.e. NGS, AACT and GST, will interact with different volumes of rock. Assuming there is lateral homogeneity, the GLT response is some weighted depth average of vertical variations. In addition, it is usual to apply a vertical smoothing process to the raw data before the oxide closure is performed. A running average of 5 data points, or just over

M.H. RAMSEY ET AL.

60 Silicon

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60cm in depth, was used for the current data. Therefore, raw measurements obtained from the GLT and ICP-AES represent significantly different depth intervals of rock. In order to make a comparison which is not biased by this difference, the ICP-AES data must be depth averaged in a comparable manner to the GLT data, although this assumes that each laboratory measurement is representative of a 25 cm depth interval, i.e. the sampling interval. Smoothing was applied to the ICP-AES data using windows of varying lengths. The window chosen was one that gave maximum correlation between the GLT and ICP-AES data, and a square window 1.4m wide (equivalent to 6 analyses) was found to be the best. Thus the smoothed ICP-AES results, at any particular depth, are based on 6 separate determinations around that depth. The unsmoothed and smoothed ICP-AES measurements for Si and Fe are plotted for comparison alongside the GLT measurements against depth in Fig. 1. In order to compensate for the difference in sample interval the GLT determinations were interpolated using a cubic spline, and depths equivalent to the ICP-AES sampling were taken. It was also necessary to apply a static depth shift of 0.5m to the GLT data to account for a difference between drilling depth and logging depth. Measurements taken by the GLT in the coal layers (150 and 154.3m in depth) were removed from the comparison due to exceptional errors caused by the effect of the anomalous density of the coal propagated through the closure calculation (Sams et al. 1995).

Estimates of the components of the bias in the element concentrations determined by the GLT compared with ICP-AES are given in Table 1. Significant rotational bias was found for all of the elements except Si, and significant translational bias for all except Ca and Ti. Silicon shows a statistically significant translational bias of - 1 . 5 3 mass%, but no detectable rotational bias. This linear model for the bias is perhaps oversimplified as the majority of data points with less than 25 mass% Si fall below the regression line (Fig. 4). In terms of the uncertainty there would seem to be a case for adding 1.53 mass% to all the GLT estimates of Si concentration. This is rather simplistic, partially because of the over-simplified model used for the bias but also because this bias may well be different for different lithologies in different boreholes. It is perhaps more prudent therefore to combine an estimate of the possible bias into the overall estimate of uncertainty. For Fe, measurement by the GLT shows significant bias in both the rotational ( - 5 4 . 6 % ) and translational (0.34 mass%) components, which again needs combining with the random component of the uncertainty for evaluation.

Combined estimates o f measurement uncertainty The total expanded uncertainty (U) can be estimated from the combination of the random component Ur% described in equation (5), and the systematic components resolved into rotational bias (BR) and translational bias (BT). The

ESTIMATION OF MEASUREMENT UNCERTAINTY upper limit of the uncertainty for an estimated concentration c is then given by: c + U = c (1 + Ur%/100)(1 + BR/100) + Uo + BT (6) and the lower limit is given by: c - U = c (1 - Ur~

+ BR/100 )

-

-

Uo + BT (7)

For the case of Si, ( U r % = 2 . 7 % , Uo = 1.8 mass%, B R = 0 and BT = --1.53 mass%) equation (6) gives: c+U =1.027c+1.8+5.53=1.027c+3.33 mass% and equation (7) gives: c - U = 0.973c- 1.8 + 1.53 = 0.973c-0.27 mass% For an estimated Si concentration of 5 mass% the uncertainty interval would therefore be 1.535 to 5.405 mass% Si, and for a high concentration of 40 mass% it would be 35.59 to 41.35 mass% Si. In the case of Fe, again assuming that the bias is not realistically correctable, the upper limit of the combined uncertainty from equation (6), where (Ur%=12.1%, Uo=0.18 mass%, BR = --54.6 and BT = 0.34 mass%) is given by: c + U = c (5 + 12.1%/100)

(1 - 54.6/100) + 0.16 + 0.34 c + U = cx 1.121 x0.454 + 0.16 + 0.34 c + U = 0.509c + 0.50 and the lower limit of the uncertainty from equation (7) is given by: c - U = c (1 - 12.1%/100)

(1 - 54.6/100)- 0.16 + 0.34 c+ U=cx0.879x0.454 -0.16+0.34 c + U=0.399c+0.18 For an Fe concentration of 1 mass% the uncertainty would range from 0.579 to 1.009 mass% Fe, and for 5 mass% Fe from 2.175 to 3.045 mass% Fe. The range of these uncertainties are large, but could be allowed for in geochemical interpretation of the concentration values. Although not relevant to the calculation of uncertainty, it is interesting to speculate on the cause of this systematic error. Two possible causes have been suggested for the large negative rotational bias for Fe determinations using the G L T (Sams et al. 5995). Firstly, it is possible

61

that the sensitivity factor for iron, Sve, is too high. Secondly, it is possible that occasionally the sampling procedure used for ICP-AES determinations dramatically over-estimates the Fe content of some samples. As mentioned earlier, the ICP-AES data is assumed to be representative of a 25 cm depth interval. However, in the case of Fe this may not be true. Iron occurs partially as sideritic nodules in the mudstones. If present in a core sample these nodules could produce an analysis of up to 30 mass% Fe, which is probably a gross overestimate of the average value for that 25cm depth interval. If a nodule is not sampled then the average value for the Fe concentration of the 25cm interval will be somewhat under-estimated. Overall therefore the heterogeneity of the iron would not be expected to produce the large bias found in this case.

Discussion The calculations used to estimate the overall uncertainty of Si and Fe are equally applicable for the other elements measured by the GLT, but there are a number of limitations which may mean the values obtained are under-estimates. There are other causes of 'random' error in the measurement system that have not been investigated. In contaminated land measurements it has been shown that multiple applications of the same measurement protocol, on different occasions, by different operators, causes appreciable increases in the uncertainties (Ramsey et al. 1995b). When different measurement protocols are selected to measure the same quantity and applied by different operators then the uncertainty increases even further (Argyraki et al. 1995). This suggests that a more rigorous estimate of the uncertainty for the GLT measurements would require the use of similar interorganization trials with both multiple users of one technique, the comparison of a number of probes in the same borehole, and the use of closely spaced duplicate boreholes to investigate lateral sampling errors, and small scale geochemical variability. A further limitation of the method reported here is that it assumes that the ICP-AES analysis provides an 'accepted reference value' as required by ISO for the detection of bias (ISO 1993b). Although the ICP-AES was calibrated using ten certified reference materials this does not ensure zero bias. This partially because the recommended values for these reference materials also have specified uncertainties, but more especially due to the problems of matching the sample volume. A better solution to the second

62

M. H. RAMSEY ET AL.

aspect would be to establish one borehole as a 'reference sampling target', against which new analytical probes and new operators could measure their systematic error (Thompson & Ramsey, 1995). The 'accepted reference value' of the elemental concentrations at specified depths in this borehole would have to be established by inter-organization sampling trials similar to those described above, with a wide variety of analytical techniques. One extra complication of the measurement system used in the G L T is that the bias in any one element will be propagated through to the other elements by the oxide closure. Such multivariate effects in measurement uncertainty have not been investigated and may cause subtle and unforeseen effects on geochemical interpretation.

Acceptable limits for uncertainty Once realistic values become available for measurement uncertainty then there will be a need to derive acceptable limits for the uncertainty. This is a separate aspect that relates the G L T measurements to the concept of'fitness for purpose'. To take an extreme case, for the vertical correlation of statigrapaphy between two boreholes it could be argued that systematic errors in the measurements are irrelevant. The depth of the coal horizons in the Northumbrian boreholes, for example, could be correlated between boreholes even if the iron concentration is biased by --54.6%. It is only the random component of the uncertainty that could mask the position of such a feature, and as such therefore could be specified in the fitness-forpurpose specification. The coal bands (shown at 150 and 154.3m in Fig. 1) can be detected from the sulphur concentrations measured by the GLT, even though the random component of the uncertainty was estimated as 71%. This shows that the limits for uncertainty need to be related to the geochemical variance (Ramsey et al., 1992). The requirements for uncertainty are very different if the elemental concentration estimates were to be used to infer the mineralogy of a sample. In this case a bias o f - - 5 0 % on one element could clearly have a major impact on the minerals inferred to be present and their calculated proportion in the rock. Because the estimation of uncertainty is more expensive than the simple estimation of elemental concentration, cost-benefit analysis will need to be applied to identify the benefits that the uncertainty information can bring. Furthermore, if there is a choice of several methods to estimate uncertainty, there will be some cases

where a crude estimate will be sufficient and others where more reliable but more expensive methods will be justified. Further clarification of these ideas awaits more case studies of the application of techniques like the G L T that include estimates of uncertainty, and an evaluation of its effects on the geochemical interpretation of the information.

Conclusions 1. Methods are available to estimate the uncertainty of measurements made with the GLT. 2. The random component of the uncertainty can be estimated at a basic level, from a replicate set of measurements from the same borehole. 3. The random component of the uncertainty can be estimated from the chemical analysis of core material by a technique such as ICP-AES, which can be linked directly to 'accepted reference values' of concentration as required by ISO. The uncertainties in the analyses by the method used for comparison (in this case ICP-AES) need to be assessed, but for the ICP-AES method they were much smaller than differences between the measurement techniques. There are also problems with this approach in allowing for the effects of different volumes of rock sampled. 4. Progressively more realistic estimates of uncertainty would require the use of different operators, on different occasions, even with different probes in inter-organization trials. 5. There is a financial need to derive acceptable levels of uncertainty for particular applications, but further case studies reporting uncertainties must be examined before this will be feasible. We would like to thank D. Filmer who performed the ICP-AES analyses. We would also like to thank Agip, Amoco, BP, Elf, Mobil, NERC, Schlumberger and Statoil who funded the Imperial College borehole test site. The third author also acknowledges assistance given by Petronas Research and Scientific Services to enable this work to be completed.

References ANALYTICAL METHODS COMMITTEE 1995. Uncertainty of measurements: implications of its use in analytical science. Analyst, 120, 2303-2308. ARGYRAK1,A., RAMSEY,M. H. & THOMPSON,M. 1995. Proficiency testing in Sampling: Pilot study on Contaminated Land. Analyst, 120, 2799-2804.

ESTIMATION OF MEASUREMENT UNCERTAINTY EUROCHEM 1995. Quantifying uncertainty in analytical measurement. Eurochem Secretariat, Teddington, UK. GARRETT R. G. 1969. The determination of sampling and analytical errors in exploration geochemistry. Economic Geology, 64, 568 569. GOVINDARAJU, K. 1994. Geostandards Newsletter, Special Issue, July 1994. GRAU, J. A., SCHWEITZER,J. S. & HERTZOG,R. C. 1990. Statistical uncertainties of elemental concentrations extracted from neutron-induced gamma-ray measurements. IEEE Transactions on Nuclear Science, 7, 2175-2178. HERTZOG, R., COLSON, L., SEEMAN, B., O'BRIEN, M., SCOTT, H., MCKEON, D., WRA1GHT, P., GRAU, J., ELLIS, D., SCHWEITZER, J. & HERRON, M. 1987. Geochemical logging with spectrometry tools. Society of Petroleum Engineers, Annual Technical Conference 1987, Dallas. SPE 16792. Iso 1993a. Guide to the expression of uncertainty in measurement. ISO, Geneva. Iso 1993b. 3534-1:1993 (EIF) Statistics, Vocabulary and Symbols - Part 1. Probability and General Statistical Terms, ISO, Geneva. RAMSEY, M. H. 1993. Sampling and analytical quality control (SAX) for improved error estimation in the measurement of heavy metals in the environment, using robust analysis of variance. Applied Geochemistry, 2, 149-153. , THOMPSON, M. & BANERJEE, E. K. 1987. A realistic assessment of analytical data quality from inductively coupled plasma atomic emission spectrometry. Analytical Proceedings, 24, 260265.

63

& HALE, M. 1992. Objective evaluation of precision requirement for geochemical analysis using robust analysis of variance. Journal of Geochemical Exploration, 44, 23 36. , ARGYRAKI,A. & THOMPSON,M. 1995a. On the collaborative trial in sampling. Analyst, 120, 2309-2317. & 1995b. Estimation of sampling bias between different sampling protocols on contaminated land. Analyst, 120, 13531356. , POTTS, P. J., WEBB, P. C., WATKINS, P., WATSON, J. S. & COLES, B. J. 1995c. An objective assessment of analytical method precision: comparison of ICP-AES and XRF for the analysis of silicate rocks. Chemical Geology, 124, 1 19. SAMS, M. S., WATKINS,P. J. & RAMSEY, M. H. (1995). Validation of a geochemical logging tool for in situ major element analysis in boreholes using inductively coupled plasma atomic emission spectrometry. Analyst, 120, 1407-1413. THOMPSON, M. 1982. Regression methods in the comparison of accuracy. Analyst, 107, 1169-1180. 1995. Uncertainty in an uncertain world. Analyst, 120, 117N-118N. - & HOWARTH, R. J. 1976. Duplicate analysis in geochemical practice. Analyst, 101, 690-698. & RAMSEV,M. H. (1995). Quality concepts and practices applied to s a m p l i n g - a n exploratory study. Analyst, 120, 261-270. WENDLANDr, R. F. & BHUYAN,K. 1990. Estimation of mineralogy and lithology from geochemical log measurements. American Association of Petroleum Geology Bulletin, 74, 87 856. -

-

Methods for simulating natural gamma ray and density wireline logs from measurements on outcrop exposures and samples: examples from the Upper Jurassic, England Z. M. A H M A D I 1 & A. L. C O E 2

t Department of Geological Sciences, University of Durham, South Road, Durham, DH1 3LE, UK (Present address: Enterprise Oil plc, Grand Buildings, Trafalgar Square, London WC2 4ES, UK) 2Department of Earth Sciences, The Open University, Walton Hall, Milton Keynes, Buckinghamshire, MK7 6AA, UK Abstract: Methods for simulating natural gamma ray and density wireline logs from measurements on outcrop exposures and rock samples have been implemented. The signals have comparable amplitudes and resolution to wireline log signals, although the absolute values do not match precisely. The field gamma ray logs were measured on the outcrops at intervals of 30-45 cm using hand-held gamma ray spectrometers. The field density logs were produced by measuring the volume and grain density of selected rock samples, followed by interpolation and filtering of the data. Both techniques are illustrated for the Upper Jurassic of the Wessex Basin, Southern England, with field data from the exposures on the Dorset coast and wireline log data from 11 boreholes between 0.5 km and 170 km away. The Upper Jurassic comprises a range of rock types, giving a wide range of values on which to test the techniques: wireline gamma ray and density values of these strata cover the ranges 15-140 API and 1.8-2.9 g cm 3, respectively. Thus these techniques should be widely applicable for the purpose of correlating outcrops with borehole data.

Wireline logs provide an intermediate link between the small-scale, high-resolution sedimentological and stratigraphical features visible at outcrop and the large-scale data available from seismic sections. Simple techniques have been developed for producing natural gamma ray and density logs from measurements on outcrop exposures and rock samples to correlate with wireline logs from boreholes. Emphasis has been placed on producing field logs which are at the same resolution and of similar character to typical downhole wireline logs, rather than reproducing the absolute values which might be expected downhole. This approach thus concentrates on reproducing patterns of cyclicity, together with general decreasing and increasing trends, which in turn can be interpreted in terms of cyclostratigraphy; for example, transgression and regression, sequence stratigraphical and Milankovitch cycles. The techniques are illustrated for the Upper Jurassic (Oxfordian, Kimmeridgian and Portlandian stages) of the Wessex Basin, Southern England. This interval is represented by a wide range of sedimentary rock types ranging from deep-marine siliciclastics to shallow and nonmarine siliciclastics and carbonates. The validity

of the methodology has therefore been tested on most sedimentary rock types, giving a full spectrum of typical data. The overall aim of this paper is to reproduce at decimetre resolution gamma ray and density wireline log trends from measurements on outcrops and rock samples, thus improving stratigraphic correlation between outcrops and boreholes. The data and interpretation presented in this paper are part of a wider study on the sequence stratigraphical interpretation of wireline log signatures from over 100 boreholes from the Upper Jurassic of the Wessex Basin. Where available, biostratigraphical data have been used to provide a framework for wireline correlations between boreholes.

Geological setting of exposures and boreholes The Wessex Basin is a Mesozoic extensional basin which is divided into a series of halfgraben, or graben-like sub-basins (Fig. 1). The Upper Jurassic exposures and boreholes used in this paper are from four of these sub-basins. The exposures where field measurements and rock samples were taken are on the Dorset Coast

AHMADI,Z. M. & CoE, A. L. 1998. Methods for simulating natural gamma ray and density wireline logs from measurements on outcrop exposures and samples: examples from the Upper Jurassic, England In: HARVEY,P. K. & LOVELL,M. A. (eds) Core-LogIntegration,Geological Society, London, Soecial Publications, 136, 65-80

65

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Fig. 1. Maps showing the main structural features of the Wessex Basin (after Whittaker 1985) and the location of boreholes and Upper Jurassic exposures. (a) Boreholes in the Weald Sub-basin and position of Fig. l(b). (b) Details of the location of boreholes and outcrops in the Dorset area. between Weymouth and Swanage, which is at the edge of the Central Channel Sub-basin. Boreholes Encombe 1 (SY 9446 7785), 98/11-1 (SZ 1187 8386) and 98/11-3 (SZ 1329 8459) are on the up-thrown northern edge of the Central Channel Sub-basin, and borehole 98/11-4 (SZ 1187 8084) is in the Central Channel Sub-basin. Winterbourne Kingston 1 (SY 8470 9796) borehole is in the Dorset Sub-basin and Marchwood 1 (SU 3991 1118) is in the Mere Sub-basin. All of the other boreholes mentioned are from the Weald Sub-basin, a moderately deep graben in the eastern part of the Wessex Basin (Fig. 1). The Oxfordian Stage is represented in the lower part by mudstones of the Upper Oxford Clay Formation and in the upper part by the

Corallian Group, a complex succession of shallow-marine siliciclastics and carbonates which show marked lateral and vertical variation. The Kimmeridgian Stage (sensu anglico) of Dorset is represented by interbedded organicrich and organic-poor mudstones, with a few thin beds of fine-grained sandstone near the base and the top. These mudstones and sandstones comprise the Kimmeridge Clay Formation, and are generally of wide lateral extent. They can be correlated across the Wessex Basin and into the Wash area and Humberside using outcrops, wireline logs and borehole cores (Gallois & Cox 1974; Cox & Gallois 1981; Penn et al. 1986; Melnyk et al. 1994, 1995). The Portlandian Stage is represented by marine silty and clay-rich

CORRELATION OF WIRELINE LOGS WITH OUTCROP dolomites deposited in a moderate water-depth (Portland Sand Formation) overlain by a shallow and non-marine carbonate ramp system which comprises the Portland Stone Formation and Lulworth Beds (Coe 1996).

Field and laboratory methods for reproducing wireline log trends g a m m a ray logging

Two types of wireline gamma ray sondes exist, the conventional one which records the total natural radiation, and the spectral gamma ray sonde which separately records gamma rays emitted from 4~ 232Th or 238U and their decay products (Serra 1984). The main uses of gamma ray logs are: (i) as an indicator of lithology; (ii) to correlate the wireline signatures between boreholes; (iii) to correlate separate wireline runs within one borehole. The fact that the gamma ray tool is run in all boreholes makes it the key wireline tool for any attempt to make correlations between outcrop and the subsurface. Field gamma ray logs can be constructed using hand-held portable gamma ray spectrometers, which were originally developed and used for uranium ore exploration (Adams & Gasperini 1970). Following the lead of Ettensohn et al. (1979), total gamma ray logs have subsequently been used for surface to subsurface correlation of sedimentary strata (Chamberlain 1984; Cowan & Myers 1988; Slatt et al. 1992; Van Buchem et al. 1992). More recently, portable gamma ray spectrometers have also been used to study the distribution of K, U and Th in sedimentary rocks, and as a tool for stratigraphical correlation between rock exposures (Dypvik & Eriksen 1983; Myers & Bristow 1989; Davies & Elliott 1996; Hesselbo 1996; Parkinson 1996; Bessa & Hesselbo 1997). Previous spectral gamma ray studies on the Upper Kimmeridge Clay Formation in Dorset have been completed by Myers (1987) and Myers & Wignall (1987), who took spectral gamma ray measurements using an Exploranium GR256 on the wave-cut platforms. They utilized these data for a sedimentological and stratigraphical interpretation of organic-rich mudstones. Talwar et al. (1992) completed a study of the gamma ray spectrometry of the Corallian Beds (Oxfordian) at Bran Point,

67

Dorset using a Scintrex Scintillation Counter (SCC) spectrometer. There are two problems with the work of Talwar et al. (1992). Firstly, they appear to have used an exceedingly short sampling time of only 3-6 s, which would result in significant errors; a count time of greater than 60 s for sedimentary rocks is usual (Lovborg & Mose 1987; Parkinson 1996). Secondly, their correlation with two boreholes from the North Dorset and Wiltshire area show little similarity because the lower two-thirds of the Oxfordian strata examined in the boreholes is older than any of the rocks which they illustrate from Bran Point, and thirdly they did not take into account any of the unconformities in the Oxfordian succession (Coe 1992, 1995). Gamma ray logging field procedure. Two portable gamma ray spectrometers have been used and compared in the work reported here: a geoMetrics GR310 (manufactured 1980) and an Exploranium GR320 (manufactured 1996). Both tools use thallium-doped sodium iodide detector crystals. The Exploranium GR320 was calibrated in Toronto by Exploranium Ltd (Canada) and the geoMetrics GR310 was calibrated on the calibration pads at the British Geological Survey, Keyworth. A value for background radiation was measured 2 km offshore from Swanage, Dorset for each tool at the same time (Fig. 1). Detailed explanation of the calibration of portable gamma ray spectrometers is provided by Lovborg (1984) and Lovborg & Mose (1987). The geoMetrics GR310 provides separate measurements of either total gamma ray count, or diagnostic gamma radiation for either K, or U, or Th, and only allows count times of 1, 10, 100 and 1000s to be chosen. Source, detector and recorder are all housed in one unit 9 cm x 18 cm x 28 cm, weighing 3.4 kg. There are several advantages of the Exploranium GR320 for this type of stratigraphical study. Total counts and counts in the K, U and Th fields are all recorded during one counting period the length of which can be set by the user anywhere in the range 1 to 9999 s. The instrument carries out automatic gain stabilization, unlike the geoMetrics GR310 which has to be calibrated by the user. Automatic gain stabilization is important because portable spectrometers are prone to tool drift due to changes in temperature and humidity. The fact that the Exploranium GR320 stabilizes itself at regular intervals saves time and reduces the risk of errors due to incorrect manual stabilization. The inbuilt computer chip allows the spectra to be displayed and the amount of K, U and Th to be

68

Z.M. AHMADI & A. L. COE

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Cliff face Borehole Fig, 2. Sampled volume for a portable gamma ray spectrometer compared to a wireline gamma ray sonde. (a) Dimensions of the sampled volume for a portable spectrometer (modified from Lovborg et al. 1971). (b) Typical orientation and position of the sampled volume for the portable gamma ray spectrometer as used in this study. (c) Spherical sampled volume for a wireline gamma ray sonde in a borehole. This depends on the speed at which the tool is drawn up the hole, as well as the density of the rocks, but typically has a radius of 30 cm (Rider 1991). The sampled volume tends to a more ellipsoidal shape when the tool is drawn up the borehole faster.

calculated directly. The only disadvantage to this instrument compared with the geoMetrics GR310 is that it is bulkier and heavier. This spectrometer comprises two parts, a detector (11.4x39.4cm) and a recording/processing unit (24 x 10 x 25 cm) which have a combined weight of 8.4 kg. The effective sampling region of portable gamma ray spectrometers is shown in Fig. 2a. The dimensions in the figure are only approximate because the density value used by Lovbor~ et al. (1971) to calculate them was 2.8gcm-which is about 0.3-0.5 gcm 3 higher than most of

the sedimentary rocks in this study. Rocks with lower density and the same amount of natural radiation would result in a slightly larger effective sampling region. The most precise absolute values for a particular bed of greater than about 14cm in thickness are obtained by placing the tool on top of a flat bedding surface of at least 1 m diameter. Similar measurements made on beds with a thickness of less than 14 cm will obviously include some component of the underlying bed or beds. The aim of this study, however, was to compare the general trends of field gamma ray logs with wireline data. Therefore the detector was placed perpendicular to bedding (Fig. 2b) so that measurements made on all beds less than about 84cm thick will have been influenced by adjacent beds, as is the case in wireline logging (Fig. 2c). Where possible, all readings were taken on a relatively flat section of the cliff face, avoiding irregularities such as overhangs and corners to ensure that the same volume of rock contributed to each reading. Readings were only taken where the tool could be used at least 1 m above the base of the cliff, thus avoiding errors due to gamma ray contribution from rocks on the beach. Count times of 100s for the geoMetrics GR310 and 200 s for the Exploranium GR320 were used in this study. This resulted in theoretical tool precision errors of < 2.5% and < 1.5%, respectively, for the total count reading. Parkinson (1996) showed that, in practice, departures of measurement geometry from a true plane far outweigh instrument precision as a source of experimental error. In this study, it was found that readings taken along 20m of a bed vary by up to 7% for both the geoMetrics GR310 and the Exploranium GR320. This is probably due to slight lithological variations as well as differences in the volume of the effective sample size due to small undulations in the cliff face. A longer count time was used for the Exploranium GR320 because spectral data were also recorded. Radioactive decay of natural elements is a r a n d o m process, so shorter sampling periods give a greater statistical error. Specifically, the percentage statistical error varies with the number of counts collected: the higher the count, the more accurate the measurement. For typical needs, 1000 counts (3% error) is accurate enough (geoMetrics GR310,

Fig. 3. Composite field gamma ray log for the Upper Jurassic succession exposed between Furzy Cliff and St. Alban's Head, Dorset, measured using the geoMetrics GR310 portable gamma ray spectrometer, plotted against the wireline gamma ray log from borehole 98/11-4 (SZ 1187 8084). Gaps in the composite field log are due to lack of exposure or non-accessibility of the section with a portable gamma ray tool. See Fig. 1 for location of borehole and outcrop sections.

CORRELATION OF WIREEINE LOGS WITH OUTCROP

69

70

Z . M . A H M A D I & A. L. COE

CORRELATION OF WIRELINE LOGS WITH OUTCROP Operating Manual; Lovborg 1984). A longer count time had to be used for the Exploranium GR320 because it takes longer to record sufficient gamma ray counts in the K, Th and U windows than it does for the total gamma ray measurement. The spectral data recorded with the Exploranium GR320 are not discussed further in this paper. The measurement procedure used for both spectrometers was to take a reading once in every bed of less than 50 cm thick and every 3050cm in beds greater than 50cm thick. The geoMetrics GR310 was used to record total gamma ray readings throughout the best Upper Jurassic exposures in Dorset, resulting in 1124 total gamma ray readings with an average sample interval of 45cm over 503m (Fig. 3). Part of the Kimmeridge Clay Formation was selected to compare the results from the two spectrometers. Full spectral gamma ray data were thus recorded with the Exploranium GR320 at 824 sample points over 251 m of the Kimmeridge Clay Formation (average sample interval 30cm; Fig. 4). The average sampling distance of 30-45 cm is within the limits of the effective sampled volume for each spectrometer (84cm; Fig. 2; Lovborg et al. 1971) and each consecutive reading overlaps the previous reading resulting in a moving average, thus making it comparable with the wireline gamma ray tool as it is pulled slowly up the borehole.

Density logging Wireline density logs record the bulk density of rocks, by emitting gamma rays into the formation and recording the number of back-scattered gamma rays at a fixed distance from the source. The bulk density is a function of the density of the matrix and the density of the fluids in the pore space. Therefore any attempt to construct a field density log with the same character and resolution as the wireline density log has to take into account the density of the matrix and the density of the pore fluid. The vertical resolution for older single-detector tools is 40 cm and for more modern two-detector tools is 25 cm (Serra 1984).

Density logging laboratory procedure. Fresh rock samples of smaller than 3.1 cmx3.1 cmx3.7cm

71

representative of the majority of the beds in the Upper Oxford Clay F o r m a t i o n , Corallian Group and Upper Kimmeridge Clay Formation were collected for density analysis. These amounted to 116 samples over 90m of the Upper Oxford Clay Formation and Corallian Group (Fig. 5) and 260 samples over 280m of the Kimmeridge Clay Formation (Fig. 6). All the samples were dried in an oven at a temperature of less than 35~ prior to the measurements being taken. A Ruska Universal Porometer (model 1051-801) was then used to measure the volume and grain density of the samples. The density of each sample was then calculated using a single typical fluid density value of 1.06gcm -3 for pore fluids present within Upper Jurassic rocks of the Wessex Basin; this actual value was recorded at Palmers Wood 3 borehole (TQ 3655 5255) in the Weald Basin (pers. comm. P. Rowe). The raw density curves on Figs 5 and 6 show the density values calculated from the actual samples measured. To obtain the box curve, two further procedures were applied. Firstly, beds from which no samples had been obtained were assigned an average density typical for that particular lithology, calculated from the measured samples collected nearby. Secondly, the same density value was assigned to the whole thickness of the bed. It was noted that the sandstones of the Nothe Grit Formation and the Bencliff Grit Member (top of the Redcliff Formation) had lower density values than those seen on the wireline density logs. This was interpreted to be due to higher porosities of these rocks at outcrop than in the subsurface, resulting from dissolution of calcite cement. The density values of these beds were therefore corrected as follows: their average porosity in the subsurface was estimated by plotting typical density and sonic values on porosity evaluation log interpretation charts (Atlas Wireline Services 1985; Schlumberger 1994). The additional, secondary dissolution porosity that was calculated to be present in the rock samples was multiplied by the difference between the density of calcite and the pore fluid and added to the calculated total density for the samples. The box curve was then filtered using the Atlas Wireline Services field acquisition filter (Atlas Wireline Services 1992). This is an eleven point, Gaus-

Fig. 4. Comparison of the field gamma ray logs measured using the Exploranium GR320 and the geoMetrics GR310 portable gamma ray spectrometers, for that part of the Kimmeridge Clay Formation exposed between Hobarrow Bay and Chapman's Pool, Dorset (SY 896 790-SY 955 771). For detailed sedimentological and stratigraphical log of the section, for the definition of the bed group numbers which have partly been derived from the literature and for formalization of the following beds; Clavell's Hard Stone Band, Little Stone Band and Pectinatus Nodules, see Coe (1992). See Fig. 1 for location of outcrop sections.

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Surface to subsurface correlation

Upper Jurassic composite field gamma ray log The field gamma ray data from nine different locations along the Dorset coast were combined to produce a composite field gamma ray log for the Upper Jurassic strata of Dorset (Fig. 3). Comparison with the wireline gamma ray data from borehole 98/11-4, which are plotted at the same scale, show that the same general trends and wireline log patterns are present in both sets of data throughout the Upper Jurassic interval. Clearly distinguishable in both log signatures are the overall trends of decreasing and increasing response which are interpreted as representing long-term facies changes controlled by relative changes in sea-level (Coe 1992). For instance, the overall upwards decrease in gamma ray values for the pallasioides Zone of the Kimmeridgian to the anguiformis Zone in the Portlandian reflects the change from marine mudstones to carbonates interpreted as a long-term lowering of relative sea-level (Coe 1992, 1996). The one notable difference is that the Lower Kimmeridge Clay (baylei to autissiodorensis zones) is thicker in borehole 98/11-4 than in the outcrop section. This is due to the fact that the measurements for the Lower Kimmeridge Clay were made on exposures situated on the footwall of the Central Channel Sub-basin, where the succession is apparently complete but thinner.

Comparison of the geoMetrics GR310 and the Exploranium GR320 Figure 4 shows a comparison of the total gamma ray measurements taken with the two spectrometers over part of the Kimmeridge Clay Formation. The decreasing and increasing trends, amplitude of variation, and the shape of the peaks and troughs correlate very well. The main difference between the two signatures is the higher resolution of the Exploranium GR320 log, which results from the 30 cm average sample interval compared to a 45 cm average sample interval for the geoMetrics GR310. The correlation coefficient between the two field gamma ray logs over 245 m of the

Kimmeridge Clay Formation is 0.7 (Fig. 4) . This was calculated using Corpac, a signal correlation computer program (Globex Consulting Services, Ltd 1992) which is based on a simple mathematical inverse method to correlate two time series (in this case depth series) described by Martinson et al. (1982). The reason the correlation coefficient is not higher is because the Exploranium GR320 log has higher resolution, and because of the gaps in the data. Higher correlation coefficients are obtained if the two logs are correlated over shorter intervals which contain no gaps in the data.

Correlation of field and wireline gamma ray logs Upper Oxford Clay and Corallian Beds. The field gamma ray log shows, from the base, an overall upwards decreasing and then increasing trend in the gamma ray values, as do the logs in boreholes 98/11-4 and 98/11-3, reflecting the change in lithology from mudstones to sandstones and limestones, and then back to mudstones and iron-rich sandstones (Fig. 7). The Nothe Grit Formation is a better defined gamma ray low in boreholes 98/11-4 and 98/11-3 than in the field gamma ray log, probably because the sands are cleaner in the boreholes and the clays of the overlying Redcliff Formation contain a high percentage of carbonate in the outcrop section. Three prominent gamma ray peaks in the Osmington Oolite Formation can be seen on both the field gamma ray log and in 98/11-4 (Fig. 7). Over a wider geographical area the Corallian Beds are lithologically very variable, being comprised of shallow-marine sandstones and limestones. Sequence stratigraphical interpretation of the wireline logs using the number and character of the cycles does permit a correlation to be made across the Wessex Basin; however, the lateral lithological variability makes correlation based purely on the wireline log character difficult. Kimmeridge Clay Formation. The similarity between the field gamma ray logs produced by the two different spectrometers and the wireline gamma ray log from Encombe 1 borehole (approximately 1 km inland from the outcrops) is shown in Fig. 8. The data acquired using the smaller sample interval with the Exploranium GR320 spectrometer produces a higher resolution curve, despite the fact that the sampling interval is about one third of the effective sampling diameter of the tool (Fig. 2). Using the methodology for calculating correlation

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coefficients described above, the correlation coefficient for the Exploranium GR320 field gamma ray log and the wireline log from the Encombe 1 borehole is 0.92, but it is only 0.82 for the geoMetrics GR310 field gamma ray log and the wireline log. Figure 9 shows the similarity between the geoMetrics GR310 field gamma ray log and wireline gamma ray logs from boreholes up to 170 km away (Fig. 1). There are several particularly prominent features, including the two gamma ray lows with a low amplitude of variation seen in the Collendean Farm (TQ 2480 4429) and Ashdown 1 (TQ 5005 3035) boreholes in the hudlestoni and wheatleyensis zones, which are often referred to as the 'Kimmeridge limestones' (Hancock & Mithen 1987). At outcrop, these two gamma ray lows with a low amplitude of variation are prominent thick homogeneous calcareous mudstone units (middle and upper part of bed 40 and the lower part of bed 44; Fig. 4). The two gamma ray lows in Collendean Farm 1 and Ashdown 1 (Figs 1 and 9) are probably more enhanced than those in 98/11-4 and the field gamma ray log because the sediments have an even higher calcium carbonate content. A higher quartz sand content is discounted because the lithology over the same intervals in the nearby Warlingham borehole (TQ 3476 5719) comprises argillaceous limestones and calcareous mudstones (Worssam et al. 1971). Prominent gamma ray lows on the field gamma ray log like those in the middle of autissiodorensis Zone, at the base of elegans Zone and near the top of scitilus Zone, are carbonate-rich cemented horizons. Similar sharp gamma ray lows in 98/11-4 and Collendean Farm 1 probably also relate to calcareous cemented horizons.

Correlation of field and wireline density logs Upper Oxford Clay and Corallian Beds. Production of an outcrop density log over this interval of mixed siliciclastics and carbonates is more problematic than that for the Kimmeridge Clay Formation. Processing of the data in a similar manner to that of the Kimmeridge Clay Formation resulted in a filtered density curve with very little variation. However, further processing of the data to take into account dissolved carbonate cement at outcrop, as described above, resulted in a curve which is more similar to the borehole density logs. The general trends seen on the filtered density log (Fig. 5) show a positive correlation with the wireline density from Marchwood 1 borehole

and to a certain extent with borehole 98/11-1. The Winterbourne Kingston 1 borehole density log is more difficult to correlate in the lower part due to the lack of variation on the large scale. Fig. 5 also shows the marked lateral and vertical variation of the Oxfordian strata between the wireline logs of 98/11-1, Marchwood 1 and Winterbourne Kingston 1. One notable example of this is the differences seen between the three wells for the density of the Nothe Grit Formation (or its equivalent) and the Osmington Oolite Formation.

Kimmeridge Clay Formation. Figure 6 shows the comparison between the processed field density log for the Dorset coast (filtered density log of Fig. 6) against the nearby Encombe 1 borehole, and the Bletchingley 1 (TQ 3622 4772) and Detention 1 (TQ 7478 4020) boreholes in the Weald Basin. The general trends and the character of all of these logs is remarkably similar. The four high peaks which straddle the pectinatus to hudlestoni zonal boundary in both the outcrop density log and Encombe 1 log represent more carbonate-rich cementstone beds. The distinctive increase in density seen in all the logs at the top of the lower third of the hudlestoni Zone represents at outcrop a change from interbedded organic-rich and organic-poor mudstones to a thick calcareous mudstone (Coe 1992). Similar lithological changes are interpreted to occur in the borehole sections. Conclusions (1) The geoMetrics GR310 and Exploranium GR320 gamma ray spectrometers can both be used to produce field gamma ray logs which are comparable with borehole gamma ray wireline logs. Whilst the newer Exploranium GR320 is more accurate and can be used to gather spectral data more quickly, the older geoMetrics GR310 does produce excellent data with repeatable and comparable gamma ray trends. The most comparable signal between hand-held spectrometers and wireline log tools is produced by using the hand-held spectrometer perpendicular to the bedding with a sample interval of 30 cm or less. Field gamma ray logs produced for the Kimmeridge Clay Formation can be used to positively correlate, often down to the bed (typically < l m ) but at least down to the bed group scale (typically 10 in), with wireline gamma ray data from nearby boreholes. Larger gamma ray features can also be correlated with boreholes as far as 170km

CORRELATION OF WIRELINE LOGS WITH OUTCROP away in the Weald Sub-basin. Field gamma ray and wireline gamma ray data for the Oxfordian show similar trends but complex local lithological heterogeneity may be misleading. The concepts of sequence stratigraphy (i.e. recognition of the metre to tens of metre scale cycles) considerably aid in making the correlation. This is because the interpretation relies on the recognition of wireline log trends rather than correlating similar lithologies, and requires identification of stratigraphic gaps and condensed intervals. (2) Small rock samples from outcrop can be used to produce a field density log. Some processing of the data is required to produce a signal which is directly comparable with the wireline tool. The method could easily be applied to small rock samples from core or washed cuttings. Excellent results comparable with the wireline signature were obtained for a thick succession of interbedded organic-rich and organic-poor mudstones and cementstones (Kimmeridge Clay Formation). Where the lithology varies more widely and shallowmarine sandstones and limestones (e.g. Corallian Beds) are present, it is necessary to take into account the differences in porosity between the borehole and outcrop section and apply a further correction factor to the outcrop density data. (3) The measurement and processing of the physical characteristics of rock exposures to produce a wireline log signature is invaluable in the understanding of boreholes where core is not available. The data can be readily used to supplement and enhance conventional litho- and bio- stratigraphical correlations between boreholes, and boreholes and outcrop. Z. Ahmadi was supported by a Durham University Research Studentship and an AAPG-PESGB Grantsin-Aid grant for field and laboratory studies. We thank Charlotte Martin and Toby Harrold for their assistance in the field, and Brian Turner for the loan of his geoMetrics GR310 gamma ray spectrometer. The Exploranium GR320 was purchased from a grant awarded to A. L. Coe from the Open University Research Development Fund. M. Oates of British Gas provided the wireline and biostratigraphical data from boreholes 98/11-1, 98/11-3 and 98/11-4, and H. Bailey of the British Geological Survey provided the wireline data for the onshore boreholes in the Wessex Basin. We would particularly like to thank N. Goulty for his constructive comments during the preparation of this paper and two anonymous referees are thanked for reviewing this paper.

79

References ADAMS, J. A. S. & GASPERINI, P. 1970. Gamma ray spectrometry of rocks, Elsevier, Holland. ATLAS WIREL1NE SERVICES. 1985. Log Interpretation Charts. Western Atlas International, Inc. ATLAS WIRELINE SERVICES. 1992. WDS advanced log evaluation - documentation. Western Atlas International, Inc. BESSA, J. L. & HESSELBO, S. P. 1997. Gamma ray character and correlation of the Lower Lias, SW Britain. Proceedings of the Geologists' Association, 108, 113-129. CHAMBERLAIN,A. K. 1984. Surface gamma ray logs: a correlation tool for frontier areas. American Association of Petroleum Geologists Bulletin, 68, 1040-1043. COE, A. L. 1992. Unconformities within the Upper Jurassic of the Wessex Basin, Southern England, DPhil Thesis, University of Oxford. 1995. A comparison of the Oxfordian successions of Dorset, Oxfordshire, and Yorkshire. In: TAYLOR, P. D. (ed.) Field Geology of the British Jurassic. Geological Society, London, 151-172. 1996. Unconformities within the Portlandian Stage of the Wessex Basin and their sequencestratigraphical significance. In: HESSELBO,S. P. & PARKINSON, D. N. (eds) Sequence Stratigraphy in British Geology, Geological Society Special Publications No. 103, 109 143. COWAN, D. R. & MYERS, K. T. 1988. Surface gamma ray logs: A correlation tool for frontier areas: Discussion. American Association of Petroleum Geologists Bulletin, 72, 634-636. Cox, B. M. & GALLOIS,R. W. 1981. The stratigraphy of the Kimmeridge Clay of the Dorset type area and its correlation with some other Kimmeridgian sequences. Report of the Institute of Geological Sciences, 80/4. DAVIES, S. J. & ELLIOTT,T. 1996. Spectral gamma ray characterisation of high resolution sequence stratigraphy: examples from Upper Carboniferous fluvio~leltaic systems, County Clare, Ireland. In: HOWELL, J. A. & AITKEN, J. F. (eds) High Resolution Sequence Stratigraphy." innovations and applications, Geological Society Special Publications No. 104, 25-35. DYPVIK, H. & ERIKSEN, D. O. 1983. Natural radioactivity of clastic sediments and the contributions of U, Th and K. Journal of Petroleum Geology, 5, 4094 16. ETTENSOHN, F. R., FULTON, L. P. & KEPFERLE, R. C. 1979. Use of scintillometer and gamma ray logs for correlation and stratigraphy in homogeneous black shales. Geological Society of America Bulletin, part II, 90, 828-849. GALLOIS,R. W. 8z Cox, B. M. 1974. Stratigraphy of the Upper Kimmeridge Clay of the Wash area. Bulletin of Geological Survey of Great Britain, 47, 1-16. HANCOCK,F. R. P. & MITHEN,D. P. 1987. The geology of the Humbly Grove Oilfield, Hampshire, UK. In: BROOKS, J. & GLENNIE, K. (eds) Petroleum Geology of North West Europe, Graham & Trot-

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man, 161-170. HESSELBO, S. P. 1996. Spectral gamma ray logs in relation to clay mineralogy and sequence stratigraphy, Cenozoic of the Atlantic Margin, offshore New Jersey. In: MOUNTAIN, G. S, MILLER, K. G, BLUM, P., POAG, C. W. & TWlCHELL, D. C. (eds) Proceedings of the Ocean Drilling Program Scientific Results, 150. LOVBORG, L. 1984. The calibration of portable and airborne gamma ray spectrometers - theoo', problems and facilities, Report Riso-M-2456, Riso National Laboratory, Denmark. & MOSE, E. 1987. Counting statistics in radioelement assaying with a portable spectrometer. Geophysics, 52, 555-563. , WOLLENBERG,H., SORENSEN,P. & HANSEN, J. 1971. Field determination of uranium and thorium by gamma ray spectrometry, exemplified by measurements in the llimaussaq alkaline intrusion, South Greenland. Economic Geology, 66, 368-384. MARTINSON,D. G., MENKE,W. & STOFFA,P. 1982. An inverse approach to signal correlation. Journal of Geophysical Research, 87, 4807~4818. MELNYK, D. H., SMITH, D. G. & AMIRI-GARROUSSl,K. 1994. Filtering and frequency mapping as tools in subsurface cyclostratigraphy, with examples from the Wessex Basin, UK. In: DE BOER, P. L. & SMITH, D. G. (eds) Orbital Jorcing and cyclic sedimentary sequences, International Association of Sedimentologists, Special Publications 19, 35 46. , ATHERSUCH,J., AINSWORTH,N. & BRITTON, P. D. 1995. Measuring the dispersion of ostracod and foraminifera extinction events in the subsurface Kimmeridge Clay and Portland beds, Upper Jurassic, United Kingdom. In: MANN, K. O., LANE, H. R. & SCHOLLE,P. A., Graphic correlation, Society of Economic Paleontologists and Mineralogists, Special Publications, 53, 185-203. MYERS, K. J. 1987. Onshore-outcrop gamma ray spectrometry as a tool in sedimentological studies. PhD thesis, University of London. - - & BRISTOW,C. S. 1989. Detailed sedimentology and gamma ray log characteristics of a Namurian deltaic succession II: gamma ray logging. In: WHATELEY, M. K. G. & PICKERING,K. T. (eds), Deltas." Sites and traps for jbssil fuels, Geological Society Special Publications, 41, 81-88. & W1ONALL, P. B. 1987. Understanding Jurassic organic-rich mudrocks new concepts

using gamma ray spectrometry and palaeoecology: examples from the Kimmeridge Clay of Dorset and the Jet Rock of Yorkshire. In: LE~GETT, J. K. & ZUFFA, G. G. (eds) Marine Clastic Sedimentology - concepts and case studies, Graham & Trotman, London, 172-189. PARKINSON,D. N. 1996. Gamma ray spectrometry as a tool for stratigraphical interpretation: examples from the western European Lower Jurassic. In: HESSELBO, S. P. & PARKINSON, O. N. (eds) Sequence Stratigraphy in British Geology, Geological Society Special Publications, 103, 231-255. PENN, I. E., Cox, B. M. & GALLOIS, R. W. 1986. Towards precision in stratigraphy: geophysical log correlation of Upper Jurassic (including Callovian) strata of the Eastern England Shelf. Journal of the Geological Society, London, 143, 381-410. RIDER, M. H. 1991. The geological interpretation of well logs. Whittles Publishing, Caithness. SCHLUMBEROER 1994. Log Interpretation Charts. Schlumberger Wireline & Testing, Houston, Texas. SERRA, O. 1984. Fundamentals qf well-log interpretation 1. The acquisition of logging data. Developments in Petroleum Science 15A. Elsevier, Holland. SLATT, R. M., JORDAN. D. W., D'AGOSTINO, A. E. & GILLESPIE, R. H. 1992. Outcrop gamma ray logging to improve understanding of subsurface well log correlation. In." HURST, A., GR1FFITHS, C. M. & WORTHINGTON, P. F. (eds) Geological Applications of Wireline Logs H, Geological Society Special Publications, 65, 3-19. TALWAR, A. D., HENDERSON, A. S. & HART, M. B. 1992. Simple gamma ray response of the Upper Jurassic from the Dorset coast - a preliminary investigation using the scintillometer profile technique. Proceedings of the Ussher Society, 8, 70-72. VAN BUCHEM,F. S. P., MELNYK,D. H. & McCAvE, [. N. 1992. Chemical cyclicity and correlation of Lower Lias mudstones using gamma ray logs, Yorkshire, UK. Journal of the Geological Society, London, 149, 991-1002. WmTTAKER, A. (ed.) 1985. Atlas of Onshore Sedimentary Basins in England and Wales: Post-Carboniferous Tectonics and Stratigraphy. Blackie, Glasgow. WORSSAM, B. C., IVIMEY-COOK, H. C. 1971. The stratigraphy of the Geological Survey Borehole at Warlingham, Surrey. Bulletin of the Geological Survey of Great Britain, 36, 1-146.

Quantitative lithology: open and cased hole application derived from integrated core chemistry and mineralogy database M. M. H E R R O N & S. L. H E R R O N Schlumberger-Doll Research, Old Quarry Road, Ridgefield, C T 06877-4108, USA

Abstract: A new quantitative lithology interpretation is based on elemental concentrations of silicon, iron, calcium and sulfur available from logs. The lithology interpretation is founded on an integrated chemistry-mineralogy core database comprising over 400 samples from many wells of predominantly sand and shaly sand composition located on four continents. The lithological components include 'clay', which is the sum of all clay minerals; 'carbonate', which is the sum of calcite and dolomite; "anhydrite', which is the sum of anhydrite plus gypsum; and 'sand' or 'quartz-feldspa~mica', which is the remainder of the formation essentially constituting the sand fraction. The new interpretation demonstrates that the elements aluminium alone or a combination of silicon, calcium, and iron provide a much more accurate estimation of clay than either gamma ray or its individual components potassium, thorium and uranium. Calcium alone or calcium and magnesium are used to determine carbonate concentrations. Calcium and sulfur can be used to estimate the anhydrite fraction. Having estimated the total clay, carbonate, and anhydrite fractions, the remainder of the formation is assumed to be composed quartz, feldspar, and mica minerals. Examples of the new lithology interpretation are provided for core data and also for geochemical log data from both open and cased hole environments.

The accurate determination of formation lithology from common geophysical logs is hindered by a lack of sensitivity coupled with nonunique responses to the minerals that reside in sedimentary rocks. The interpretation of lithology for the purpose of wireline petrophysical evaluation or geological characterization primarily consists of estimating fractions of shale, sand, and carbonate. Nuclear logs, either gamma ray, photoelectric factor, and/or a combination of neutron and density are the most commonly used logs for lithology interpretation, A desire for improved accuracy in Ethological description led to the introduction of several generations of nuclear spectroscopy logs. Recent developments in open and cased hole logging have made it possible to obtain accurate concentration logs for the elements silicon, calcium, iron, sulfur, titanium, and gadolinium at relatively low cost and high logging speeds (Herron 1995). A new lithological interpretation has been developed to capitalize on these new logging capabilities. It is founded on an extensive database of core chemistry and mineralogy. The new interpretation provides quantitative estimates of: total clay, which is the sum of all clay minerals; carbonate, which is the sum of calcite and dolomite; anhydrite, which is the sum of anhydrite plus gypsum; and quartz-feldsparmica (Q F-M), which is the remainder of the formation essentially constituting the sand frac-

tion. The clay, carbonate, and quartz-feldsparmica portions of this interpretation have been presented previously (Herron & Herron 1996). This paper provides a brief introduction to the new geochemical logging capabilities in both open and cased holes and a detailed examination of the new core-based interpretation.

Elemental concentration logs The recently developed technique to estimate elemental concentrations from a single, inducedneutron gamma ray spectrometer (Herron 1995) is an adaptation of a geochemical oxides closure model already employed in the computation of elemental concentrations from multiple nuclear sondes (Hertzog et al. 1987; Schweitzer et al. 1988; Grau & Schweitzer 1989; Grau et al. 1989). The most significant modifications are: (1) the elimination of aluminium and potassium as necessary inputs to the geochemical closure model, thus considerably reducing the number of wireline sondes necessary to produce elemental concentrations of potassium; (2) a change in the elemental associations of iron. Figure 1 presents examples of elemental concentration logs from the new processing using data

HERRON, M. M. & HERRON,S. L. 1998. Quantitative lithology: open and cased hole application derived from integrated core chemistry and mineralogy database. In. HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 81-95

81

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900 0

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20 40 0 10 20 0 10 20 0 2 4 0 20 40 Calcium wt% Iron + .t4AI ~% Sulfur wt% Titanium wt% Gadolinium ppm

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Fig. 1. Openhole elemental concentrations from the Elemental Capture Spectroscopy (ECS; Mark of Schlumberger) sonde.

x 104

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Fig. 2. Cased hole elemental concentrations from the (RST; Mark of Schlumberger) Reservoir Saturation Tool.

from an open hole Elemental Capture Spectroscopy (ECS; Mark of Schlumberger) sonde. This is a nuclear spectroscopy device which uses a standard AmBe source and a BGO detector. It is combinable and can log at up to 540 m hr -l (1800 fthr-~). Chemical concentrations measured on

core samples are shown for comparison. Two points should be made when examining the data. The first is that since the uncorrected prompt capture yield for iron contains gamma rays from both Fe and A1, the log Fe should be approximately equal to Fe+0.14A1. Accordingly, the

QUANTITATIVE LITHOLOGY

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Fig. 3. Synthetic gamma ray (computed from Th, U and K concentrations) plotted against total clay (kaolinite, illite, smectite, chlorite and glauconite) measured on the same sample for 12 datasets. Although GR crudely correlates with total clay, the slopes and offsets vary widely from well to well.

core points plotted for c o m p a r i s o n are Fe+0.14A1. The second point is that the log concentrations agree well with core data. A second example is provided from a cased hole Reservoir Saturation Tool (RST; Mark of Schlumberger) log acquired from a well in Venezuela (Fig. 2). This example is processed using new elemental standards to derive the far detector capture yields (Roscoe et al. 1995), and corrections are made for casing and a 3.8cm cement annulus. The results show good agreement between log concentrations and the sparse core data.

tion coupled plasma mass spectrometry, for whole rock elemental concentrations of silicon (Si), aluminium (A1), iron (Fe), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), phosphorus (P), titanium (Ti), manganese (Mn) and chromium (Cr), expressed as oxides, plus Loss on Ignition (LOI) representing total volatiles, H20 +, H 2 0 , sulfur (S), organic carbon, thorium (Th), uranium (U), gadolinium (Gd) and boron (B). A synthetic core gamma ray (GR) computed from core chemistry using the gamma ray response is given by equation (1) G R = 4 T h + 8 U + 16 K

Core database The development of the new quantitative lithology interpretation begins with a core database that contains chemistry and mineralogy measurements on over 400 core plug samples from numerous wells on four continents. The wells are diverse in age and geographic location, but all are predominantly sands and shaly sands. To analyse the samples, rocks were crushed and split with a microsplitter into chemistry and mineralogy fractions. The chemistry fraction was analysed at X-Ray Assay Laboratories using XRay fluorescence, neutron activation and induc-

(1)

where Th and U concentrations are in ppm and K concentrations are in wt% (Ellis 1987). The mineralogy fraction was analysed using a new Fourier Transform-Infrared (FT-IR) procedure which simultaneously analyses the mid-IR and far-IR frequencies. The mid-IR procedure was described in Matteson & Herron (1993). Since that time the number of mineral standards has been increased to 26 with approximately the same level of accuracy (better than +2 wt %). The mineral standard set includes quartz, albite, anorthite, K-feldspar, muscovite, biotite, kaolinite, illite, smectite, chlorite, glauconite, calcite,

84

M. M. HERRON & S. L. HERRON o 1~176 9 dP

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loo

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o

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.t .

5 Gadolinium

10

20

40

loo[ r t o

25 Silicon wt%

50

0

15

Iron wt%

30

0

Calcium wt%

Fig. 4. Comparison of individual chemical elements that can be measured by logging against total clay for Well 3. A1 shows a strong positive correlation that is mirrored by the negative correlation with Si.

dolomite, siderite, ankerite, magnesite, aragonite, gypsum, anhydrite, hematite, barite and opal. Total clay is the sum of kaolinite, illite, smectite, chlorite and glauconite. Although there are significant amounts of mica, another layered silicate, they are not included in the total clay fraction. At high clay concentrations there is sometimes interference between illite and mica phases. Exploring elemental relationships

The most complex aspect of the new lithology interpretation is the computation of the clay mineral fraction. In the logging world, clay, or more often volume of shale, is most frequently estimated from the gamma ray log. However, there are many type of clay minerals with widely differing compositions and log responses, so shale estimates often carry large uncertainties. The estimation is further degraded by the many non-clay minerals which contribute significantly to the gamma ray.

Gamma ray and clay With the core database, it is possible to evaluate the relationship between total clay determined by FT-IR and the computed gamma ray on a

porosity-free basis, as recently advocated by K a t a h a r a (1995). The relationship for core samples from 12 data sets is presented in Fig. 3. A line connecting the origin with 100% clay and 250 API is included for visual reference. As expected, gamma ray content generally increases as clay content increases. However, there are a number of characteristics in the clay-gamma ray plots that highlight the weaknesses inherent in this approach; many of these have been recently discussed by Bhuyan & Passey (1994) and Hurst & Milodowski (1994). The first major feature is the large range of slopes in the gamma ray versus clay plots which demonstrates the necessity for local calibration. For example, in Well 1, a linear trend predicts a maximum gamma ray value of about 100 API for the pure clay end member, whereas Well 2 would predict 500 API. For Well 12, a pure clay would have a gamma ray of only about 150 API. In several wells, either the data or an extrapolation of the data to zero clay indicate a near zero minimum gamma ray, but Well 4 has a minimum gamma ray of 30 API, and in Well 12 an extrapolation points to 70 API for minimum gamma ray. The difference between evaluating these plots and using only log data is that with core calibration the amount of clay is known, and it is possible to accurately extrapolate to

QUANTITATIVE LITHOLOGY

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Fig. 5. Comparison of individual chemical elements that can be measured by logging against total clay for Well 5. A1 again shows a strong positive correlation with clay. The negative correlation with Si is slightly perturbed by high Fe siderite samples. ,100]

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Fig. 6. Comparison of individual chemical elements that can be measured by logging against total clay for Well 6. A1 shows a strong positive correlation. The anticorrelation with Si is significantly perturbed by carbonates.

86

M.M. HERRON & S. L. HERRON

comparison of clay with aluminium. In Well 3, aluminium displays a strong relationship with total clay. The remaining elements in this figure are some that can be obtained by prompt thermal neutron capture spectroscopy logging devices. The two elements remaining in the second row of the figure are titanium and gadolinium. These elements are commonly enriched in shales, but they show only a loose correlation with total clay. The third row holds the key to a new technique for estimating clay. It begins with silicon, which is a major constituent of rock forming minerals. Although silicon is commonly associated with quartz, it is actually the second most abundant element after oxygen in both sandstones and shales. Because it is a major element, its abundance is not affected by trace minerals, and concentrations form a smooth continuum between high silicon sandstones and medium silicon shales. For reference, quartz has 46.8 wt% silicon. The next element is iron, which has numerous associations, including heavy minerals such as siderite, pyrite, hematite, and magnetite and the clay minerals illite, chlorite, glauconite and some smectites. High concentrations of the heavy iron minerals can interrupt the smooth relationship between silicon and clay content. The final element is calcium which is mainly associated with the carbonate minerals calcite and dolomite. The low calcium concentrations indicate the absence of carbonate minerals in Well 3. The same type of comparison between total clay and elemental concentrations is presented in Fig. 5 for Well 5. For this well, none of the individual elements (Th, U and K) contributing Seeking an elemental alternative to natural gamma ray is any better correlated with clay than is total gamma ray. In contrast, One goal of this study is to identify an aluminium again shows a tight correlation with alternative, less subjective approach to determining clay content using elemental data available clay content. Silicon again shows a strong from nuclear spectroscopy logging devices. The negative correlation with clay, but there are two data points which clearly deviate from the technology exists to measure elemental concenmajor trend. These two samples contain 13 and trations from natural radioactivity (Th, U and 38 wt% siderite (FeCO3) as reflected by the two K), neutron activation (AI), and capture gamma high iron points. As in Well 3, the near absence ray spectroscopy (Si, Ca, Fe, Ti, Gd and S). of calcium reflects the absence of calcite and Figure 4 shows a comparison of clay content with all available logging elements (except sulfur) dolomite. A final example of the element-clay comparfor Well 3. The three components of natural gamma ray; Th, U, and K, are presented in the isons is presented in Fig. 6 for Well 6. In this first row. Thorium and uranium show wide well, thorium and potassium exhibit positive scatter and little correlation with clay. In this correlations with clay, but the degree of scatter well, potassium shows a strong correlation with precludes the use of these elements for accurate clay, but examination of data from four other clay prediction, especially at low clay contents. Aluminium again shows a strong positive correwells in the field reveals that this correlation breaks down entirely in sands containing less lation with clay. Silicon again shows a negative correlation with clay, but the impact of carbothan 25% clay. The second row of Fig. 4 begins with a nate minerals on the silicon--clay curve is much

zero clay. With only log data, one must choose a minimum and maximum gamma ray value without knowing the correspondence to real clay concentrations, and the picture is further complicated by porosity variations. For Well 11, the minimum gamma ray value observed on the log is about the same as the 50 API minimum computed for the core data. This value would normally be assigned to zero clay instead of the actual 25 wt% clay. Clearly, such a log interpretation would severely under-estimate the clay content in the well. The second dominant feature in Fig. 3 is the scatter in the data, particularly in Wells 1-10. In these wells, even if the observed correlation between gamma ray and clay were known, the scatter in the data would produce an uncertainty of as much as +20 wt% clay or more. For Wells 3, 5, 7 and 9, at levels of about 20% clay, observed gamma ray values span almost the full range from clean sand to shale. The relative error is particularly large in sands. A third and less common feature is that some wells exhibit a small dynamic range in gamma ray while clay content varies considerably. This is notable in Well 12 which is a typical offshore Gulf of Mexico example. It is also true in Wells 4 and 11. In spite of the problems outlined above, it would be possible to make good clay predictions in Wells 2, 11 and 12 if detailed and accurate core data were available. Without such a calibration, it is doubtful that the picks for GRmax and GRmi n from the log data would match the core calibration parameters.

QUANTITATIVE LITHOLOGY

100

a

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.

Aluminum wt%

00

Alumi10num wt%

20 0

10 ' 20 0 Aluminumwt%

Alumi1()num wt%

20

Fig. 7. Aluminium versus total clay for all 12 wells. The correlation with total clay is much tighter for aluminium

than for GR. In addition, the slopes are about the same and most wells show a near-zero offset.

more obvious 9There are many samples with high calcium reflecting calcite concentrations that range from 0 to 85 wt%. This mineral assemblage produces a ternary composition diagram in the silicon-clay plot with the vertices representing pure carbonate, clean sand, and shale. Summarizing the observations in Figs 4 through 6, it appears that aluminium is the best single elemental indicator of clay. Silicon shows a complementary anti-correlation to clay content, but the simple linear relationship between silicon and clay is distorted by carbonate minerals. The carbonate content is chemically represented by calcium and/or iron. These trends are typical of those observed in the other data sets. Having observed the strong relationship between aluminium and clay, it is useful to examine the data for all 12 wells, as shown in Fig. 7. In 10 of the 12 wells, the slope of the aluminium-clay plot is nearly constant. In 9 of the 12 wells, the intercept of the aluminium-clay linear relationship is essentially zero. Comparison between Fig. 7 and Fig. 3 shows that aluminium is a much better clay estimator than gamma ray in most wells. This is true even when a porosity-free core calibration is available for gamma ray, and it is especially true in the sands 9

The improvement of aluminium over gamma ray is marginal in Well 8, but it is significant for the cleanest sands. In Wells 11 and 12, the aluminium and gamma ray are comparable clay indicators if the core calibration is known. However, a log interpreter who equates the minimum gamma ray response with zero clay introduces a 20 to 25 wt% error in the clay estimation. Aluminium has an even more striking relationship with the sum of clay plus mica. This is demonstrated in Fig. 8. Improvements in the correlation with aluminium are most notable in Wells 7 and 8, and the effects are most obvious in the shales. The lines drawn in Fig. 8 represent a slope of 6.4 and the relationship for the first 10 wells has a correlation coefficient of 0.98. It is possible that some of the differences between Figs 7 and 8 are due to analytical interference between illite and mica phases in shales 9 The decision to include or exclude mica from the clay fraction depends on the application. Since micas do not contribute significantly to clay counterion conductivity, they are not generally included in saturation interpretation. On the other hand, like clays, micas can be detrimental to formation productivity. There are several reasons for the strong

88

M.M. HERRON & S. L. HERRON 100

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Fig. 8. Aluminium versus total clay +mica for all 12 wells shows an even tighter and more universal relationship than aluminium versus clay.

correlation between aluminium and total clay mineral content. Clays are aluminosilicates; aluminium is a major element in and an integral part of the chemical composition of virtually all clays. This is very different from the case of thorium and uranium which occur at trace (ppm) levels and are not structural components of the clays. Of course, the clay-A1 relationship is a simplified picture and is not expected to be perfect. Different clay minerals have different A1 concentrations and there are important nonclay minerals that contain aluminium.

Relationship between AI and Si, Ca, Mg and Fe Although aluminium is the best element for clay estimation, its measurement in a borehole is accomplished by induced neutron activation and currently requires a chemical source, two gamma ray spectrometers, and an independent measurement of formation capture cross-section, making it an expensive measurement. Fortunately, an alternative exists due to the complementary relationship between aluminium and the elements silicon, calcium, magnesium and iron. This relationship is illustrated in Fig. 9, which combines elemental data from all 12 data sets

into three plots. For samples containing more than 2 wt% organic carbon, the elemental data must be normalized to an organic-free matrix or else they will perturb the linear relationship. Earlier, Figs 4 to 6 showed that as clay increases, silicon decreases. Therefore, as aluminium increases, silicon decreases. In Fig. 9a, silicon is converted to SiO2 (by multiplying by 2.139) and subtracted from 100. Now, we see that as AI increases, 100-SiO2 also increases. In this presentation, carbonate minerals drive the data toward A1 of zero and ( 1 0 0 - SiO2) values of 100 wt%. We can use concentrations of Ca and Mg to compensate for the presence of calcite (CaCO3) and dolomite (CaMg(CO3)2). Fig. 9b shows that concentrations of A1 vary linearly when plotted against 1 0 0 - S i O 2 - CaCO3 - MgCO3 concentrations and that the additional terms remove almost all of the disturbance of that major trend. The few remaining outliers are predominantly siderite or pyrite, and they can be removed as 1.99Fe where the coefficient of 1.99 is optimized on these data. The trend in Fig. 9c can be used to estimate the aluminium concentration from A1 = 0.34(100 - SiO2 - CaCO3 MgCO3-1.99Fe),

(2)

.r

89

QUANTITATIVE LITHOLOGY

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9. Aluminium is estimated from the other major elements in sedimentary rocks. (a) A1 vs 100-SIO2 shows a clear trend that is disturbed primarily by carbonates. (b) A1 vs 100 SiO2-siderite and dolomite shows a very tight trend that is disturbed only by siderite and pyrite rich samples. (c) When the high-Fe minerals are corrected for, A1 can be estimated from Si, Ca and Fe.

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Fig. 10. Aluminium estimated from Si, Ca, Mg and Fe closely matches measured aluminium in all 12 wells. which produces estimates of A1 with a correlation coefficient of 0.99 and a standard error of 0.6 wt% A1. Figure 10 presents a comparison of measured A1 concentrations with those estimated from equation (2) for each of the 12 wells. Clearly, this is a robust means of estimating A1 from Si, Ca, Mg and Fe.

Quantitative lithology The strong correlation between aluminium and clay provides the cornerstone of the lithology

interpretation. This relationship can be quantified to estimate clay, and the elements calcium, magnesium, and sulfur can be used to estimate the other major mineralogical components. The mineralogical fractions defined here are different from the lithologies commonly used in log interpretation. The main difference is that a clay fraction rather than a shale fraction is computed. According to Bhuyan & Passey (1994), shales commonly have about 60 wt% clay minerals and 40 wt% Q - F - M . Using this ratio, a rock with 60 wt% clay is 100 wt% shale. The other difference

90

M. M. HERRON & S. L. HERRON 100 .........

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Fig. 11. Clay estimated from Si, Ca, Mg and Fe plotted against total clay for all 12 wells is a near duplicate of Fig. 7.

is that the values determined here are all on porosity-free (or matrix) basis, and they are weight rather than volume fractions.

minimum of 1.3 for Well 10. If we solve for clay plus mica (Fig. 8) instead of clay, we obtain the following equation:

Estimating clay

Clay + Mica = 2.20(100- SiO2-

The two major points from the preceding section are that A1 correlates well with clay content and that aluminium concentrations can be estimated from Si, Ca, Mg and Fe. The next logical step is to estimate clay content from Si, Ca, Mg and Fe using the form of equation (2). The problem is set up to determine clay content by optimizing the slope. Samples from Wells 11 and 12 are excluded from the optimization because, as seen in Fig. 7, the relationship between aluminium and clay differs significantly from the relationships observed in Wells 1 through 10. The new clay algorithm is: Clay = 1.67(100 - SiO2 - CaCO3 M g C O 3 - 1.99Fe),

(3)

which has a correlation coefficient of 0.94 and a standard error of 6.9 wt% clay. The slope of 1.67 obtained here is representative of the combined datasets. Slopes optimized on individual datasets range from a maximum of 2.0 for Well 1 to a

C a C O 3 - M g C O 3 - 1.99Fe),

(4)

with a correlation coefficient of 0.97 and a standard error of 6.5 wt%. Figure 11 presents measured clay content and estimates from equation (3) for all 12 wells. The estimated clay concentrations are in good agreement with the measured values for Wells 1-7, 9 and 10. They are almost the same as the estimates from aluminium shown in Fig. 7. For most of the first ten wells, the clay estimates portrayed in Fig. 11 constitute an improvement over those attainable from gamma ray. The scatter in the estimate is drastically reduced, particularly at the low clay concentrations where clay estimation is most critical. This is especially clear in Wells 1-7 and 9 and 10. In Well 8, the estimate of clay shows a less spectacular effect relative to gamma ray, but it does offer slight improvement in the clean sands. In this well, an estimate of clay plus mica would clearly be superior to gamma ray estimates. Equation (3) is a general algorithm for

QUANTITATIVE LITHOLOGY

91

estimating clay from elemental data. It has broad applicability and does not require picks of minimum and maximum values. Unlike neut r o n - d e n s i t y separation, equation 3 is not affected by the presence of light hydrocarbons or gas. Although the slope would vary if optimized on individual datasets, the overall slope of 1.67 in equation (3) produces a good clay estimate. For Wells 11 and 12 the clay estimated from equation (3) agrees with the measured clay in the cleanest samples but under-estimates the clay content of the shales. The cleanest samples in these two wells have 20 and 28 wt% clay, and it is not obvious which way the data would trend in cleaner rocks. This is the same trend observed for these two wells in the comparison of aluminium versus clay. The problem with the interpretation of clay from A1 or from Si, Ca and Fe in Wells 11 and 12 is basically the same as the problem with interpreting gamma ray. Inherent in both interpretation schemes is the presumption that nonclay minerals do not interfere. For most wells, this is true for aluminium. However, Wells 11 and 12 are characterized by feldspar-rich sands. This is true to a lesser degree for Well 4. In fact, for all three of these wells, there is an anticorrelation between clay and non-clay aluminosilicates (feldspars plus micas). The high feldspar content of the sands can be either authigenic as in Well 11 or detrital as in Well 12. In spite of vast geological differences, Wells 11 and 12 show similar patterns in terms of aluminium vs clay. This suggests that a common algorithm might exist to interpret clay content in these wells, and if so, it might be broadly applicable to feldspar- or mica-rich sands. The relationship determined by least absolute error optimization on the combined Well 11 and Well 12 datasets is:

feldspathic sands, so the application of equation (5) requires some external knowledge.

Clay2 = -20.8 + 3.1 (100 - SiO2 -

Here, the non-zero offset of - 7 . 5 wt% accounts for the small calcium contribution from plagioclase feldspar in sandstones, and the offset and

C a C O 3 - M g C O 3 - 1.99Fe)

(5)

This differs from equation (4) by modifying the slope and introducing an intercept. The results for Wells 11 and 12 are compared to measured clay in Fig. 12. Data from Well 4, which also has moderately feldspar-rich sandstones, are included as different symbols; this well was not included in the optimization. Equation (5) for feldspar-rich sandstones gives reasonable results for clay contents in the reservoir rocks despite the fact that these wells are from very different geological environments. Using the geochemical data alone, it is not possible to identify such

loo ;g/

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50

0 ~0

50 1 oo Estimated Clay2

Fig. 12. Clay estimated from equation (5) for feldsparrich sands and shales vs measured clay for Wells 11 and l 2 (o) and Well 4 (+). Estimating carbonate The second c o m p o n e n t in this lithological description is the carbonate fraction. The carbonate fraction will be determined from calcium, but first we need to consider the calcium concentration which we obtain from log data. Pure calcite (CaCO3) formations have Ca concentrations of 40 wt%, and this concentration is accurately reflected by log data. A complication arises in dolomites (CaMg(CO3)2) because magnesium has not normally been detected by spectroscopy logs. As a result, the log calcium concentration in a pure dolomite is also 40 wt% (see Hertzog et al. 1987 and Roscoe et al. 1995 for detecting Mg from logs). This is equivalent to saying that the Ca detected by logs equals C a + 1.455Mg, an expression that equals 40 wt% in either pure calcite or dolomite. Using the core data base, calcite plus dolomite concentrations were optimized as a function of (Ca + 1.455 Mg) to produce equation (6): Calcite + Dolomite - 7.5 + 2.69(Ca + 1.455Mg). =

(6)

/ ,/ o

o 50 1 oo Estimated Calcite + Dolomite

Fig. 13. Calcite plus dolomite estimated from equation (6) vs measured calcite plus dolomite for all twelve wells.

92

M.M. HERRON & S. L. HERRON

oo[

o

so

0

0 50 100 0 50 100 E~imated Clay wt% Estimated Carbonate wt%

0 50 100 Estimated Q-F-M wt%

Fig. 14. Comparison of estimated and measured quantities of clay, carbonate, and quartz-feldspar-mica on samples from all 12 wells.

slope (2.69) are balances to provide the correct answer in pure carbonate. The carbonate estimate from equation (6) closely approximates the sum of calcite plus dolomite from all 12 wells (Fig. 13) with a correlation coefficient of 0.98. A distinction of calcite from dolomite is possible with the inclusion of magnesium (Hertzog et al. 1987; Roscoe et al. 1995).

Estimating quartz-feldspar-mica The third component of the new lithological description is the sand fraction composed primarily of quartz, feldspars and micas ( Q - F M). This fraction is determined by subtracting the clay and carbonate fractions from 100 wt%. Figure 14 shows the estimated and measured concentrations of clay, carbonate, and quartzfeldspar-mica for all 12 wells. In the reservoir rocks, where clay content is less than 30 wt%, the agreement between measured and estimated concentrations is remarkably good for all components. In the shales, particularly where clay exceeds 50 wt%, the interpretation tends to under-estimate clay and over-estimate Q - F - M . Obviously, the clay algorithm could be optimized to give more accurate estimates in shales. The carbonate estimates are good over the entire dynamic range.

Estimating anhydrite This three component lithological description is easily modified to accommodate formations containing significant amounts of anhydrite or gypsum. The anhydrite estimate precedes the carbonate estimate to separate carbonate calcium from anhydrite calcium. Two estimates of anhydrite are made, one from sulfur and one from calcium, according to stoichiometric relationships where the sulfur concentration in anhydrite is 23.55 wt. % and the calcium concentration is 29.44 wt.%.

Anhl = S/23.55

(7)

Anh2 = Ca/29.44.

(8)

The final anhydrite estimate is the minimum of these two to account for the possibility of nonanhydrite sources of either sulfur or calcium. The anhydrite computation precedes the carbonate and clay estimate's and the anhydrite calcium is subtracted from the total calcium prior to the other lithological computations. When solving for anhydrite, the Q - F - M fraction is determined by subtracting the clay, carbonate, and anhydrite fractions from 100 wt%. Fig. 15 presents a comparison of anhydrite measured by FT-IR and anhydrite using calcium and sulfur from a single well in West Texas. 40 35 30 E ~=25 (3.

(-920 +

e15 -E

"O

~r 1 0 < 5 I

I

I

A

10 20 30 Estimated Anhydrite wt%

40

Fig. 15. Comparison of estimated and measured quantity of anhydrite on a single dataset.

Application to log data The ultimate goal of this study is to identify an objective, robust, and efficient means of estimating lithology from spectroscopy logs. The two simultaneous developments that have made this possible are the determination of elemental

QUANTITATIVE LITHOLOGY concentrations from induced gamma ray spectroscopy logs and the derivation of the lithology algorithms presented above. To apply these relationships using the data from Figs 1 and 2 requires that the clay algorithms be modified to account for the known aluminium interference in the iron measurement. Equations (3), (4) and (5) for computing clay or clay plus mica become: ClayL = 1.91(100 -- SiO2 - CaCO3 - 1.99FeA1) (9) Clay + MicaL = 2.43(100

-- SiO2 -

(10)

CaCO3-1.99FeA1)

Clay2L = -- 18.5 + 3.34(100-SiO2-- CaCO3 - 1.99FeA1)

(11)

where the L subscript designates the application to log data. FeA1 designates the quantity that would be detected as iron by a spectroscopy device and is equal to Fe + 0.14A1. The clay, carbonate and Q - F - M fractions calculated using the Fig. 1 open hole spectroscopy data from Well 8 are presented in Fig. 16. Also shown are the core clay, carbonate and Q F - M fractions determined from the F T A R mineralogy. The agreement between core and log data is quite good, in spite of the fact that

93

Well 8 is probably the worst example of the A1clay relationship. The interpretation of the cased hole spectroscopy logs from Well 3 (Fig. 2) is presented in Fig. 17. The agreement between core and log data is quite spectacular considering that these measurements are made with a ll~in, diameter tool through casing and cement. Conclusions

The quantitative lithology presented here has been optimized on core data from numerous wells from around the world. The lithological fractions of clay, carbonate, anhydrite, and quartz-feldspar-mica are ideally suited for the elemental concentration logs of silicon, calcium, iron, and sulfur, which can be acquired by single, induced gamma ray spectroscopy logs. These elemental concentration logs could be available in both open and cased hole. The strength of this elemental approach to estimating lithology lies in the use of major element chemistry as opposed to trace element chemistry which can be so easily impacted by sediment diagenesis, depositional environment, or the spurious introduction of small amounts of heavy minerals. The elements used are major element contributors to the rockforming minerals. Their concentrations in a given mineral are relatively stable, and the

20("

40(

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9

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60(

80( l_ t-t

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100(

=

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1400

160C 0

50

Clay, wt%

1 O0

0

50

Carbonate, wt%

1 O0

0

50

Quartz-Feld-Mica, wt%

1 O0

Fig. 16. Quantitative lithology logs for Well 8 using the openhole elemental concentration logs shown in Fig. 1. FT-IR core measurements are provided for comparison.

94

M.M. HERRON & S. L. HERRON x 10 4 1.01

! w

1.03

L

[1.05

B i

L 1.07

0

50 Clay, wt%

1 O0

~:" 0

i 50 Carbonate, wt%

O0

0

50 1 O0 Quartz-Feld-Mica, wt%

Fig. 17. Quantitative lithology logs for Well 3 using the cased hole elemental concentration logs shown in Fig. 1. FT-IR core measurements are provided for comparison. minerals in which they occur are generally abundant. The S i - C a - F e aluminium emulator gives a demonstrably superior clay interpretation compared to that available from gamma ray. Its strength lies in the near constant slope, small degree of scatter, and near zero intercept. It is also independent of fluid volume, type and density, rendering it free from gas or light hydrocarbon effects, unlike the neutron-density separation. The calcium log provides an unparalleled carbonate estimation. It provides carbonate quantification in complex lithologies. In heavy barite muds, it easily and accurately locates carbonate cementation at levels of 10 to 20 wt% which were previously undetected by conventional log interpretation. The sulfur log provides a very accurate estimate of anhydrite which is of greatest value in carbonate/evaporate lithologies. While the relationships presented here have demonstrated a large degree of universality, each algorithm can be further optimized on a field or regional basis to give improved lithological estimates.

References BHVVAN, K. & PASSEY, Q. R. 1994. Clay estimation from GR and neutron~tensity porosity logs,

-

paper DDD. In: 35th Annual Logging Symposium Transactions: Society of Professional Well Log Analysts, pp. D1 15. ELLIs, D. V. 1987. Well Logging for Earth Scientists. Elsevier, New York. GRAU, J. A. & SCHWEITZER, J. S. 1989. Elemental concentrations from thermal neutron capture gamma-ray spectra in geological formations. Nuclear Geophysics, 3, 1 9. --, ELLIS, D. V. & HERTZOa, R. C. 1989. A geological model for gamma-ray spectroscopy logging measurements. Nuclear Geophysics, 3, 351-359. HERRON, S. L. 1995. Method and apparatus for determining elemental concentrations for "/ ray spectroscopy tools, U.S. Patent 5,471,057. & HERRON,M. M. 1996. Quantitative lithology: An application for open and eased hole spectroscopy. In. 37th Annual Logging Symposium Transactions: Society of Professional Well Log Analysts, pp. E1 14. HERTZOG,R. C., COLSON,L., SEEMAN,B., O'BRIEN,M., SCOTT, H., McKEoN, D., WRA~GHT,P., GRAU, L, ELLlS, D., SCHWEITZER, J. & HERRON, M. 1987. Geochemical logging with spectrometry tools, SPE-16792. In. 62nd Annual Technical Conference and Exhibition Proceedings: Society of Petroleum Engineers. HURST,A. & MILODOWSK1,T. 1994. Characterization of clays in sandstones: Thorium content and spectral log data, paper S. In: Sixteenth European Formation Evaluation Symposium: Society of Professional Well Log Analysts. -

QUANTITATIVE LITHOLOGY KATAHARA, K. W. 1995. Gamma ray log response in shaley sands. The Log Analyst, 36, 50--55. MATTESON, A. & HERRON, M. M. 1993. Quantitative mineral analysis by Fourier transform infrared spectroscopy, Society of Core Analysts Technical Conference, August 9 11, 1993, SCA 9308. RoscoE, B., GRAU, L, CAO MINH, C. (~; FREEMAN, D. 1995 Non-conventional applications of through-

95

tubing carbon-oxygen logging tools, paper QQ. In: in 34th Annual Logging Symposium Transactions: Society of Professional Well Log Analysts. SCHWEITZER, J. S., ELLIS, D. V., GRAU, J. A. & HERTZOG, R. C. 1988. Elemental concentrations from gamma-ray spectroscopy logs. Nuclear Geophysics, 2, 175 181.

The comparison of core and geophysical log measurements obtained in the Nirex investigation of the Sellafield region A. K I N G D O N , S. F. ROGERS, C. J. E V A N S & N. R. B R E R E T O N

British Geological Survey, Keyworth, Nottingham, NG12 5GG, UK

Abstract: The Sellafield region, west Cumbria, is the focus of one of the most thorough geological investigations in the United Kingdom. The Sellafield Site is defined as an area immediately around the potential repository, extending 6.5 km north-south by 8 km eastwest. Twenty six deep boreholes were drilled within the area up to the end of 1995, with a total depth of approximately 28 km. Most of these boreholes have been continuously cored, a total of over 17 kilometres of core, with average core recovery well in excess of 90%. All boreholes were logged with a comprehensive suite of geophysical logs, including many state of the art tools. Laboratory physical property analysis of hundreds of sample cores has been carried out. Pilot studies were carried out to compare and contrast datasets and to investigate the relationships between the different data scales. Various techniques, including fractal analysis and Artificial Neural Networks, were tried in order to explore the relationships of these data at a variety of measurement scales. The pilot study was conducted in two stages: (1) evaluation of the primary controlling factors of the physical properties; (2) testing the validity of 'Up-scaling'. The rocks of the Borrowdale Volcanic Group provided the most challenging problems due to the physical properties being dominated by fracturing and associated alteration zones. Relationships between data types at different scales were established suggesting that the extrapolation of properties derived from core and wireline logs across three-dimensional seismic grids would allow an understanding of the properties throughout a threedimensional volume. Nirex is responsible for the development of a deep geological repository for solid, intermediate level and some low-level radioactive wastes. Following preliminary geological investigations of two sites, an area near Sellafield, west Cumbria, was chosen in 1991 for further study. The Nirex science programme aimed to assess the suitability of the Sellafield site as the host for the repository. Such an assessment required, among other things, an understanding of the geology and hydrogeological characteristics of the area. The Sellafield region in west Cumbria, England was the focus of one of the most detailed site investigations projects ever undertaken. This investigation aimed to characterize the geology and hydrogeology of the site to determine whether the site at Sellafield showed sufficient promise of meeting regulatory targets to permit Nirex to submit a planning application for a deep repository. An underground Rock Characterization Facility (RCF) had been proposed in order to allow more detailed characterization of the geology and hydrogeology of the area using direct observations from underground

excavations and to allow in situ experiments on rock and groundwater behaviour. These measurements were required to provide information on ground conditions that could only be obtained from an underground facility and to test models of the geology, hydrogeology and geotechnical characteristics and behaviour of the rocks. In the course of the Sellafield site investigations, data at a range of scales from microscopic to regional have been collected. The large volume of data available from the Nirex investigations presents problems with respect to the estimation of properties at the very large scales required by performance models. In most practical applications, the scale of the sample measurements is not directly comparable with the scale required for the model estimates needed for the calculations. It is important to evaluate the scale of the sample data and the scale required for the final estimates and to apply some correction to the sample scale, if they are different. This corrective process is generally termed 'up-scaling'. In the context of Sellafield, physical property parameters important to the

KINGDON, A., ROGERS, S. F., EVANS, C. J. & BRERETON,N. R. 1998. The comparison of core and geophysical log measurements obtained in the Nirex investigation of the Sellafield region. In: HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 97-113

97

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A. KINGDON E T AL.

Fig. 1. Location of the Sellafield boreholes and the potential repository zone.

construction of underground vaults required, on the scale of tens of metres may only be measured on core samples at the scale of centimetres or from geophysical logging of the boreholes at a scale of a few metres. The difficulty of extrapolating properties using data from varying scales makes it difficult to use data derived at one scale, for example borehole core, to another, such as a threedimensional seismic survey. In the context of the Sellafield investigations, parameters that are important to tunnelling, such as indices of rock strength need to be derived at one scale and then extrapolated to another scale. Techniques to allow this to be undertaken must have therefore to be both derived and tested. This paper examines the possible techniques for comparing data derived at three separate scales: borehole core (centimetre scale), geophysical borehole logs (metre scale) and threedimensional seismic survey data (10 metre scale). In particular, mathematical techniques were studied that examine relationships between data scales; thus demonstrating the validity of the methodology of 'up-scaling'. This study largely concentrated on the Potential Repository Zone (PRZ) an approximately four square kilometre area near the village of

Gosforth. The key task of this study was to define an index of rock properties derived from geophysical log measurements down each of the boreholes in the PRZ. This index was then used to extrapolate those properties across a volume, as sampled by the three-dimensional seismic survey, aiming to allow prediction of rock properties at any location within that volume.

Location and geological setting The Sellafield site is in west Cumbria, England and situated between the coast of the East Irish Sea and the Lake District National Park. A map of the area is shown as Fig. 1. Up to the end of 1995 twenty-six boreholes were drilled within the area as part of the site investigation. The geology from each borehole has been fully described ( Nirex 1993; 1995a,b). The regional basement in the Sellafield area is the Borrowdale Volcanic Group (BVG) which consists of a complex group of Ordovician tufts, lapilli tufts and acidic lavas with local intermediate and basic intrusions, and volcaniclastic sediments. (Millward et al. 1994). The BVG was deposited as a largely sub-aerial volcanic system formed by an island arc on the southern margin of the Iapetus Ocean. The BVG is unconform-

THE NIREX INVESTIGATION OF THE SELLAFIELD REGION ably overlain by a south-westerly thickening Carboniferous Limestone and Permo-Triassic succession. The Permo-Triassic forms the subcrop across most of the Sellafield area. The BVG and (where present) the Carboniferous Limestone are unconformably overlain by the Brockram, a Permian fluvial breccia conglomerate with up to cobble sized clasts. The upper part of the Brockram near the coast passes laterally into the St Bees Shale and Evaporite (Nirex 1993). The St Bees Evaporite comprises dolomite and anhydrite, and the St Bees Shale is a laminated sandstone, siltstone and claystone formation. The St Bees Shale is conformably overlain by the dominantly fluvial St Bees Sandstone (Barnes et al. 1994) of Triassic age. This is a sandstone and claystone, with the claystone increasing markedly down succession, particularly in the North Head Member at the base. The St Bees Sandstone is overlain by the Triassic aeolian Calder Sandstone which forms the subcrop in the PRZ area. The easternmost of the Sellafield boreholes (9A and 9B) were drilled into outcropping BVG with the Permo-Triassic succession outcropping to the southwest of these boreholes. This succession thickens towards the southwest reaching a thickness of 1700m at the Irish Sea coast. In the PRZ area there is 400 to 500 m of sedimentary cover overlying the basement. The Permo-Triassic succession occurs on the eastern margin of the East Irish Sea Basin, an extensional basin associated with prolonged east-west extension resulting in the dominantly north-south faulting seen today (Jackson et al., 1995).

Data sources High quality geological and geophysical data have been acquired across the Sellafield region during the site investigation. All twenty-six boreholes have been geophysically logged using comprehensive suites of state of the art tools, including borehole imaging. In addition, the boreholes have been extensively cored, allowing continuous detailed geological and discontinuity description to be undertaken. Detailed gravity and magnetic survey data has been acquired across the Sellafield region, as well as twodimensional seismic data. The geology of the PRZ area has been the subject of a highly detailed investigation. Up to the end of 1995 eleven boreholes (Boreholes 2, 4 & 5; RCF1, 2 & 3, RCM1, 2 & 3; PRZ2 & 3) were drilled within an area measuring only 1200 m by 800 m across the ground surface. All penetrated to the BVG, the deepest borehole

99

penetrated to 1600m below ground level. All boreholes have been cored from within the St Bees Sandstone succession to terminal depth within the BVG, with total core recovery in excess of 95% (close to 100% in some of the later boreholes). In addition, a high quality trial three-dimensional seismic survey has been acquired across part of the PRZ area. Data acquired within the PRZ area therefore allows particular scope for both deriving detailed rock properties and up-scaling between different datasets.

Deriving characteristic rock properties The first stage of this project involved the derivation of the average rock properties in each borehole, for each of the major formations in the Sellafield area. These average properties were then used to determine whether a particular formation was essentially constant across the area or whether there were significant regional and/or local variations in the rock properties. Where significant variations in rock properties were found the possible causes for the variation were examined. This exercise was first carried out on a regional scale and then concentrated in more detail upon the PRZ area. Rock properties were studied by comparison of geophysical borehole logs from across the area. Geophysical logs were chosen because of the consistent way that the data was acquired, both in terms of techniques and sampling rates. This allows for easy comparison between boreholes some distance apart. The main characteristics of the rock properties studied were identified by statistical and graphical techniques of data comparison.

Stud), o f velocity Although many rock properties have been measured at various scales, compressional velocity is one of the few to have been measured at all scales, from core to seismic scale. It was therefore chosen as the most representative property for analysis as an example of the average rock property behaviour. The compressional velocity of a rock formation is controlled by the matrix density, the porosity and the fluid composition. Compressional velocity data for each of the three data scales were derived by different techniques. Core scale data for each of the main rock types were provided by laboratory testing on core samples. Wireline log scale data were derived from sonic velocity logging. Larger scale

100

A. KINGDON ET AL.

Fig. 2. Percentage frequency histogram of bulk compressional velocity for the main stratigraphic units of the PRZ.

data were derived directly from the two way transit velocities from seismic survey information. Figure 2 shows a frequency histogram of bulk compressional velocity. This shows the velocity distributions for the three rock types present in this area (the St Bees Sandstone, Brockram and BVG) in the PRZ. The statistics are derived from the total geophysical logging measurements in each borehole from within the area. The mean velocity of the St Bees Sandstone is shown to be between 3.5 to 4.5 kms -l, the Brockram between 4.5 to 5.5 kms -1 and the BVG between 6.0 to 7.0 km s 1. Variations in velocity within the range for the individual rock type are caused by differences in the properties of the materials. This study aimed to identify these differences and to attempt to understand their origin. More detailed studies of the nature of the velocity distributions have been made for two rock types: the St Bees Sandstone and the BVG. This was done initially on a regional scale and subsequently more locally in the PRZ area. The Brockram is of a fairly consistent thickness across the Sellafield Region (approximately 100m) and shows remarkably homogeneous properties. As a consequence, no further attempt has been made to characterize variation in rock properties for this lithology.

Regional studies of velocity The regional pattern of velocity was studied using graphs of midpoint depth against mean formation velocity for each borehole. The midpoint depth of a formation is defined as the point equidistant between the top and base of the sampled section of a formation, regardless of whether the borehole had sampled the entire thickness of the formation. This allows comparison of the compressional velocities between boreholes without any overprinting of the effects of velocity changes within the formation. The mean velocities have been derived from geophysical logs of the formation and are expressed on the graph as kilometres per second. Figure 3 is a graph of midpoint depth against velocity for the St Bees Sandstone in each of the boreholes in the Sellafield region where the formation is present. Figure 4 shows the same data types for the BVG. Boreholes are shown using different symbols depending on whether they fall inside or outside the boundaries of the PRZ area. Also plotted on the graph are typical core sample data that have been pressurized under laboratory conditions to simulated depths.

Results of studies of velocity against midpoint depths. Figure 3 shows that mean compressional

THE NIREX INVESTIGATION OF THE SELLAFIELD REGION

Fig. 3. A graph of midpoint depth against compressional velocity for the St Bees Sandstone.

Fig. 4. A graph of midpoint depth against velocity for the Borrowdale Volcanics Group.

101

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A. KINGDON ET AL.

velocity of the St Bees Sandstone increases with midpoint depth in a smooth curve. This exponential increase in velocity with depth is well documented as being caused by decreasing effective porosity due to increased overburden pressure (Birch 1960). The results from the core samples also demonstrate that increasing depth results in an increase in compressional velocity. The velocity results from the core samples is somewhat lower than the velocities from equivalent bulk depths derived from geophysical logs in the boreholes. This is a consequence of the samples being tested whilst dry (i.e. the pore space was air filled rather than saturated with water). Figure 4 shows a plot of midpoint depth against compressional velocity for those boreholes in the Sellafield region where the BVG is present. This plot shows a relatively complex pattern in comparison with the St Bees Sandstone. Most boreholes show a mean velocity over the BVG interval of between 5.0 to 5.5 km s l.The boreholes in the PRZ area (with the exception of borehole PRZ3) showing a distinct clustering of mean velocities. Three boreholes 9B, 8B and PRZ3 show significantly lower mean velocities. The first two of these boreholes penetrate a short distance into the BVG. Detailed logging of the Borehole 8B core (Nirex 1995a) suggested that the top section of the BVG is highly altered, which was the only part of the BVG sampled in this borehole. In the case of Borehole 9B, which was drilled where the BVG outcrops at surface, the rocks will have been affected by recent weathering. As these effects will have had a significant influence on rock properties of these two boreholes, the data are not comparable with the other boreholes and they have not therefore been included in the study of the average rock properties of the deep BVG. In the case of borehole PRZ3, where the BVG is covered by a thick Permo-Triassic succession, the difference could not be readily explained in this way and the BVG velocities from this borehole are therefore seen to be significantly anomalous. An independent quality assurance check of the geophysical logging of Borehole PRZ3 did not indicate any systematic error in the acquisition and processing stages. Borehole PRZ3 was targeted to intersect a fault, Fault F1, in the BVG and it is likely that the mean velocity results from this borehole reflect a large proportion of 'faulted rock'. The velocities of the available core samples in the BVG are slightly higher than the wireline derived mean formation velocities, despite the core samples again having been tested whilst dry. This is inferred to be due to the porosity of

the material being sufficiently small that the compaction effect is not as significant. Of greater significance to the properties of the core samples is the fact that core tests were by definition carried out on samples of intact rock. The properties of these samples therefore varies from the bulk rock sampled by geophysical logs, which includes the effects of non-intact and fractured rock. This suggests that the variations of the bulk rock properties from those of intact rock may be a consequence of the discontinuities within the rock mass.

Local studies gf velocity More detailed analysis of the bulk rock velocity properties for the boreholes in the PRZ region was carried out using box and whisker plots. Box and whisker plots (Figs 5, 6 and 7 and 11) are a graphical technique which permits an overview of a complete data distribution, excluding only anomalous data at the extremes of the distribution. This permits the statistical comparison of almost the entire data distribution between all the boreholes. The variation in the range of data as well as average properties can therefore be assessed. Figure 5 shows the symbols used to describe different parts of the data distribution on box and whisker plots. Figure 6 is a box and whisker plot of the velocity distributions of the St Bees Sandstone for the boreholes in the PRZ. The column on the right-hand side of this diagram shows the combined properties for all the boreholes. The quartile range of the velocity for all the PRZ boreholes is between 3.6 and 4.4 kms 1. The bulk rock properties from all boreholes are essentially consistent between all the PRZ boreholes. This indicates that there is little regional variation in the bulk rock properties of the St Bees Sandstone in this area. Two additional datasets are also displayed on this plot, the core properties and the faulted rock properties. Core properties were derived from laboratory tests on intact core samples. Laboratory testing of the properties of the fault rock was difficult as fault rock is by definition nonintact. The fault properties shown here were therefore derived from geophysical log measurements. The standard geophysical log measuring increment of 6 inches (15.24cm) means that statistically valid samples are hard to obtain from faults with limited borehole intersections. Therefore properties were only derived for faults with borehole intersections greater than 50 centimetres. The core sample seismic velocities are seen to be much lower (interquartile range for all

THE NIREX INVESTIGATION OF THE SELLAFIELD REGION NUMBER OF POINTS

NUMBER OF POINTS

103

NUMBER (')F" POIN'IS

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LOWER QUARTILE

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BULK PROPERTIES

CORE PROPERTIES

Fig. 5. Key to the symbols used on a box and whisker plot.

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Fig. 6. Box and whisker plot of compressional velocity for the St Bees Sandstone Group. boreholes 3.4 to 3.8 k m s -1) than those of the bulk rock as they were tested on dry samples (see above). The fault data are also somewhat lower (interquartile range for all boreholes 3.3 to 3.9 k m s 1). Whilst the interpretation of these two

datasets was hampered by the small number of sample points and the sampling bias, it is clear that the properties of fault rock are significantly different to the bulk rock. This can be seen in the 'total column' which includes the summed data

104

A. KINGDON E T AL. 7.0

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Fig. 7. Box and whisker plot of compressional velocity for the Borrowdale Volcanic Group.

Fig. 8. Cross-plot of density against neutron porosity fo the St Bees Sandstone Group showing depth as the z-axis.

THE NIREX INVESTIGATION OF THE SELLAFIELD REGION for all boreholes. As can be seen in Figure 7 most boreholes in the PRZ area shows a consistent set of bulk rock velocity (interquartile ranges between 5.0 to 6.1 kms-1). Only borehole PRZ3 displays significantly different properties with lower values (interquartile range 4.5 to 5.0 kms-l). Comparison of the three measurements for each borehole provided important evidence to the controlling mechanism for bulk rock properties. Whilst core derived intact rock properties showed somewhat higher velocities than the bulk rock properties in almost all cases, the fault derived velocity values were significantly lower than the equivalent bulk rock properties and showed greater variability. The difference between bulk rock and intact rock is, by definition, the discontinuities of which fault rock was the only measurable example. Hence the discontinuities must be the dominant controlling factor on the bulk rock properties of the BVG. In the case of borehole PRZ3 the velocity measurements showed a very localized anomaly, consistent with the targeting of the borehole into a faulted zone.

Causes of variataions of rock properties in the St Bees Sandstone The compressional velocity distribution of the boreholes described above shows that the

105

velocity profile of the St Bees Sandstone largely reflects the depth of burial and there is therefore an increase in average velocity to the south-west where the midpoint depth of the formation is greater. Figure 8 shows a typical cross plot of percentage porosity against density of the St Bees Sandstone from Borehole 2. The depth of each point is displayed as the z-axis (the colouration of the points in Fig. 8). The diagram shows that most of the data plots along a line, with density increasing as porosity decreases and depth increases. This distribution is caused by compaction (due to increasing overburden pressure) leading to decreasing porosity with depth. Some variations from this simple distribution are seen. In order to understand the anomalous points, the effects of variations in lithology had to be quantified. Figure 9 shows the densityporosity cross plot for the same data but with the z-axis now showing the gamma-ray count from each point. This clearly shows the effects of lithological change as higher gamma counts occur in those parts of the distribution which do not follow the simple trend. This is because these zones are not clean sandstone but contain a significant proportion of claystone. These two diagrams therefore indicate that the dominant control on the bulk rock properties of the St Bees Sandstone was depth of burial and lithological variation.

Fig. 9. Cross plot of density against neutron porosity for the St Bees Sandstone Group showing gamma ray as the z-axis.

106

A. KINGDON E T AL.

Fig. 10. Cross plot of shallow resistivity against neutron porosity for the Borrowdale Volcanic Group.

Fig. 11. Box and whisker plot of gamma ray for the Borrowdale Volcanic Group.

THE NIREX INVESTIGATION OF THE SELLAFIELD REGION

107

Causes of variations of r o c k properties in the Borrowdale Volcanic Group Figure 8 showed that whilst the BVG showed fairly homogeneous properties between boreholes across most of the P R Z area, there is significant variation of properties within each borehole. The nature of these variations was therefore investigated further using an example borehole. Figure 10 shows a neutron porosity against shallow resistivity cross plot with gamma ray counts as the z-axis. Two main clusters are seen on this graph. The bulk of data points are shown to be highly resistive, low porosity with a low gamma ray count. These values represent the properties of the intact rock. In addition a smaller cluster of points with low resistivity, higher porosity and high gamma ray counts are also seen. The higher gamma ray counts are probably caused by the alteration minerals found around discontinuities within the rock mass. This data acted as a further indication that the dominant control over the bulk rock properties of the BVG are the nature of the discontinuities within the rock mass rather than the intact properties of the rock itself. In an attempt to quantify the effects of the properties of the faults on the bulk rock properties a box and whisker plot of the gamma ray counts for the BVG of the P R Z boreholes was produced (Fig. 11). In most of the boreholes with a statistically significant sample of fault rock, the gamma ray response is approximately 5 API higher in the fault rock than in the bulk rock. This can be seen most clearly in the total (all boreholes) column on the right-hand side of the diagram. This is not an ideal presentational medium because a fault in an already low gamma ray formation may have a lower count than high gamma background elsewhere in the borehole. Variations in the condition of the boreholes will also significantly affect the results. The data for Borehole RCM3 is dominated by a single large fault close to the top of the BVG where low gamma is recorded because of poor hole conditions (the caliper increases from 6 to 16 inches through this fault). Despite these problems the diagram does clearly show that gamma ray counts are higher in fault zones than in bulk rock.

Data scales and up-scaling Relating physical properties derived from one scale to another represents a significant problem. Prediction of the rock properties through a given

Fig. 12. A diagrammatic fractal distribution: the Sierpinski Gasket.

volume, such as those sampled by the trial 3-D seismic survey are important for successful engineering design of tunnels, shafts etc. The P R Z area has eleven deep boreholes drilled by Nirex, and several drilled previously by British Steel and its precursors, and yet less than one five millionths of the total volume of the P R Z has been directly sampled by coring. In order to scientifically justify the up-scaling of known properties (derived from either direct measurements on core or indirect measurements from geophysical logging tools) it is important to demonstrate that at least over a small but significant part of the scale, properties are comparable. If this can be done then 'up-scaling' of data can be seen as a legitimate concept, although it should be treated with caution.

Fractals A true fractal relationship is a relationship between two variables that does not change with scale. Whilst it is unlikely that any relationship is truly fractal in a natural system, if a relationship could be demonstrated over a number of orders of magnitude then this could be used to justify up-scaling of data from one scale to another. Figure 12 shows a diagrammatic fractal relationship, the Sierpinski Gasket. Each size of triangles is related to the next largest and next smallest size of triangles by the same scale and geometric relationships, up to the limits of page size in one extreme and print resolution in the other. An attempt was made to study discontinuities in the Sellafield area at two separate scales: distances between individual discontinuities measured directly from the core and distances between seismically resolved faults. This was

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Fig. 13. Log-log plot showing fractal distribution of borehole discontinuities in borehole 8B.

done firstly to assess whether fracture distributions were fractal at each scale and then to see whether any link between data at the two scales could be established.

Discontinuity separation Various techniques have been derived to study the fractal dimension for a distribution of naturally occurring phenomena. This study was carried out using the Spacing Population Technique (after Harris et al. 1991) which is both straight forward and applicable to the type of data to be examined. The basic dataset for this study was the borehole discontinuity log, produced for Nirex by Gibb Deep Geology Group (GDGG) from direct measurement of the core. This lists, for each borehole, all the occurrences of faults, veins, joints and other discontinuities, ordered by depth. All discontinuities with a non-structural origin were removed, such as bedding features, stylolites in the Carboniferous Limestone and those fractures in the core that were induced by the drilling process.

Methodology The Spacing Population Technique is based on cumulative frequency distributions derived incrementally from large (infrequent) to small

(frequent) events. In this case, classes of 0.1 m to 100m were used as applicable to borehole discontinuity spacing, covering three full log cycles. In order to analyse the data an approximate geometric progression was used to divide the data up into frequency classes suitable for log-log output. The cumulative frequency data wwere plotted as a log-log plot. To be considered fractal the distribution had to plot as a straight line (showing that the relationship is scale invariant and conforms to the following function):

y=ax - D where: y = probability (cumulative frequency); a = a prefactor; x = the discontinuity spacing; D = t h e line gradient (i.e. the fractal dimension). For a distribution to be considered fractal the data should be linear across at least one order of magnitude.

Results of fractal analysis Figure 13 shows a cumulative frequency log-log plot for Borehole 8B, which gives the results of the fractal analysis of borehole discontinuities in this borehole. Some of the limitations of the fractal technique are demonstrated by this dataset. Whilst natural fractals should be proved

THE NIREX INVESTIGATION OF THE SELLAFIELD REGION

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Fig. 14. Log-log plot showing fractal modelling of seismic scale faults for the base Carboniferous.

to exist in any relationship over several orders of magnitude, any single measurement technique may only characterize a subset of this total range. The limits for identification of fractal patterns are often therefore controlled by limitations in the sampling method. In this study for instance the core fracture dataset was reliant on the human eye to identify individual fractures. Very closely spaced fractures lead inevitably to highly broken core and poor core recovery, so that such zones are preferentially undersampled. Rock, heavily fractured by localized, closely spaced events is essentially indistinguishable from large fractures and will behave in a similar way. Major fault systems have not been sufficiently sampled by boreholes to permit fractal analysis at this scale, whereas the threedimensional seismic survey only identifies faults either by 'significant' offset of marker lithological contacts or directly where the thickness of fault-rock is sufficient to cause a velocity contrast. Also the Sellafield Site boreholes cannot be labelled a random unbiased dataset, as some of the boreholes were specifically targeted at some of the major faults in the region. The borehole 8B fracture set shows a clear fractal relationship over two orders of magnitude, i.e. at fracture spacings from 20cm to 10m, with a regression coefficient of R 2= 0.9996, very close to a perfect straight line.

Fractal events at the seismic scale Large events, such as major faults, although

sampled by boreholes, occur only infrequently and the dataset is statistically insignificant in any one borehole. Therefore another measurement technique must be used to sample the larger faults. An obvious alternative method is to examine faults identified from a two-dimensional seismic grid. This dataset was used in this study for a comparison with the borehole derived results. It was important to make clear at this stage that these were not identical datasets simply measured at different scales. Seismic reflection profiles (and in particular widely spaced two-dimensional seismic data) tend to resolve only large scale fault zones rather than distinguishing individual minor fault strands such as those which would be delineated from borehole core.

Methodology In order to get an acceptable level of coverage of fault features with a common resolution, offshore seismic reflection data from near the Sellafield site were used for this study. Unlike boreholes, which essentially sample a onedimensional environment, the interpreted seismic fault maps used in this part of the study were two-dimensional in character. The dataset in this case were fault maps stored in a database of faults derived from V U L C A N software modelling of the regional structure. A different sampling technique was used in order to develop cumulative frequency data. In this case a two-dimensional 'box counting' method was applied. This was done by overlaying the maps to be studied with grids of

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Fig. 15. Log-log plot showing fractal modelling of seismic scale faults for the base permo-trials.

square boxes. These boxes each had sides of length d and the number of boxes containing fault features was counted (given as Nd). The exercise was repeated several times with boxes of progressively shorter side length d (i.e. the grids become finer). The number of filled boxes (Na) was plotted on a log-log plot against box size dimension (d). If the relationship between the two variables is fractal it produces a straight line of gradient - D , which should be in the range 1.0 < D < 2.0 (Hirata 1989)

Results of fractal modelling of seismic scale

faults Two different seismic base maps of the East Irish Sea basin were studied (Nirex 1995b); The base Carboniferous (Fig. 14) and Base Permo-Triassic (Figure 15). Both showed very clear fractal patterns over the scale range from 200m to 1 km, with regression coefficients of R 2= 0.999 in both cases. Fractal patterns have been demonstrated over two different scale ranges for discontinuity spacing events. The regression coefficient for both sets of events was very close to one (i.e. a completely fractal pattern). However the scaling exponents, sometimes known as the fractal dimension, differ suggesting that fracture spacing is a scale dependent parameter. These relationships provide evidence that up-scaling of discontinuity events from features measured in core and at the seismic scale are valid within

Fig. 16. An idealized artifical neural network.

their respective scale ranges. It is also possible to debate that if up-scaling is valid over both these ranges there may be the possibility of establishing a relationship between the different fractal equations and thus defining an up-scaling function all the way from micro fracture scale to major faulting.

Neural network modelling Artificial Neural Networks (ANNs) are a computer modelling technique that work in a manner analogous to the processes of a mammalian brain. They are based on simple linear processing elements which interact to form

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111

Fig. 17. Results of neural network modelling for RCFl: zonation of fracturing from actual and predicted fracture frequency.

Fig. 18. Results of neural networks modelling for RCF2: zonation of fracturing from actual and predicted fracture frequency.

complex non-linear behaviour. A N N s can 'learn' to recognize patterns in data and develop their own generalizations. A diagrammatic model of an idealized artificial neural network is shown in Fig. 16. Fracture frequency measured from borehole core was not easy to predict with any degree of accuracy from conventional geophysical log measurements. Whilst borehole imaging tools go some way to addressing this issue, it was not always possible to distinguish between features such as bedding features and discontinuities.

Neural networks may allow another approach in identifying fracture frequency.

Multi-layer preceptron This study used a type of A N N called a multilayer preceptron (MLP) to model the relationship between core derived fracture frequency and geophysical log measurements. The MLP consists of a series of simple processing elements (nodes) connected to one another. In operation the node receives several

ll2

A. KINGDON ET AL.

inputs which it sums. The strength of the node's response is proportional to the sum of the inputs. Nodes are placed in layers such that each node from one layer is connected to every node in the next layer. These connections are weighted and weights are changed according to the relative importance accorded to each layer. Input data are fed through the network and compared with the output data. Discrepancies between the input and output datasets result in changes in the weighting in connections. Over a number of iterations the network 'learns' which inputs have the greatest effect on output. This type of ANN, where data are fed through the network and error fed back, is known as a feed forward back-propagating network.

Use of artificial neural networks in this study Artificial neural networks were used in this study to attempt to model fracture frequency from conventional geophysical log inputs. Fracture frequency was derived from the borehole discontinuity logging file, binned at intervals of one metre. The two datasets were not immediately analogous because fracture frequency had been calculated per metre whereas geophysical logs conventionally sample at 0.1524m (6 inches). Geophysical logs were filtered using a twelve point moving average filter using BGS WELLOG software. Data were then interpolated to a one metre value and extracted in EXCEL 5.0 for manipulation. Six geophysical logs were used as input for this study: density, neutron porosity, gamma ray, shallow resistivity, compressional velocity and shear velocity. The fracture frequency data were also subject to some biasing and were therefore subjected to a five point moving average filter. These were also exported from BGS WELLOG software to EXCEL 5.0. Neural network modelling for this study used Neural Connections V1.0 software from SPSS. Various network topologies and statistical test were applied to validate the results. The software selected the network topology, usually the X-3-1 layout (X nodes in the input layer, three hidden layer nodes and a single output node).

Network training Training was highly important to the performance of a neural network. Although it was possible for ANNs to generalize and infer noise obscured properties, the network response was better where it has been trained by high quality data. In this exercise data from boreholes in the PRZ area were used to model the fracture

Fig. 19. Comparison of RMR and wireline logs for borehole RCF2.

frequency values for BVG sections in boreholes RCF1 and RCF2. The training dataset consisted of the following data segments 840-1015 mbRT (metres below the rotary table datum) from RCF1, 525.5-750 mbRT from RCF2 and 732.5-932.5 mbRT from RCF3. The data were run through a network in its natural order and then randomized to compare the performance of the network.

Results of neural network analysis Figures 17 and 18 show the results of the neural network analysis for boreholes RCF1 and RCF2. This dataset shows clearly both the uses and the limitations of using ANNs for modelling. Whilst in some parts of both boreholes, the actual fracture frequency had been modelled with some accuracy, in others the modelling had not adequately resolved the distribution. The actual fracture frequency was shown at the top of both diagrams with zones of similar levels of general fracturing marked by a black line. The bottom diagram shows the A N N predicted results, again with a black line marking the zones of similarity. Comparison of both boreholes shows that the models were good at distinguishing the background level of fracturing in the boreholes. The biggest problems in the models were at the data extremities. Although this technique was not perfect it does again give clear indications that the fracture frequency data

THE NIREX INVESTIGATION OF THE SELLAFIELD REGION derived from core can be up-scaled to be modelled by geophysical logs.

Comparison of RMR and conventional wireline logs The rock mass rating (RMR) is an industry standard rock quality and strength index derived from direct measurement of the physical attributes of the core and is completely independent of wireline log measurements. This measurement allows an accurate assessment of rock strength, but is labour intensive and therefore expensive to collect. R M R is calculated for whole and partial core runs and is reported for Boreholes RCF1, RCF2 and RCF3 in 3 m intervals. Figure 19 shows the results of a comparison of R M R with two conventional wireline log measurements from the same borehole over a given interval. The degree of correlation between these two sets of measurements is high despite the wholly different derivation and supports upscaling of the wireline log data from a measurement scale of 15cm to at least 3 m by simple arithmetic averaging.

Conclusion Whilst neither the fractals nor the artificial neural network derived models showed exact matches with the core derived data from which they were extrapolated, both showed that there was considerable scope for the belief that using the correct criteria, it is possible to up-scale data to match both wireline and seismic scale data. The Rock Characterization Facility (RCF) proposed at Sellafield requires detailed rock properties to be derived from boreholes and extrapolated across a wider area to allow for prediction of the likely tunnelling parameters. Where three-dimensional seismic survey data are available across an area, it should be possible to derive rock properties at a borehole scale and extrapolate them across a three-dimensional volume to give an accurate prediction of the nature of the RCF site. This was dependent upon a detailed knowledge of the rock properties and accurate correlation of core and seismic properties. The concept of up-scaling parameters derived at one scale to another may be feasible but needs

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considerable more research to prove valid. If suitable algorithms can be derived, then extrapolation of geophysical parameters derived from geophysical logs or cores, across a three-dimensional seismic grid, should allow detailed prediction of the properties for that volume of the rock mass.

References BARNES, R. P., AMBROSE, K., HOLLIDAY, D. W. & JONES, N. S. 1994. Lithostratigraphic subdivision of the Triassic Sherwood Sandstone Group in West Cumbria. Proceedings of the Yorkshire Geological Society, 50, 51-61. BIRCH, F. 1960. The velocity of compressional waves in rocks to l0 kilobars, Part 1. Journal of Geophysical Research, 65, 1083-1102. CHADWICK,R. A., KIRBY, G. A. & BAILY,H. E. 1994. The post-Triassic structural evolution of northwest England and adjoining parts of the East Irish Sea. Proceedings of the Yorkshire Geological Society, 50, 91-103. CHAeLOW, R. 1996. The geology and hydrogeology of Sellafield : an overview. Proceedings of the NIREX seminar, 11 May 1994. Quarterly Journal of Engineering Geology, 29, Supplement 1. HARRIS, C., FRANSSEN, R. & LOOSVELD, R. 1991. Fractal analysis of fractures in rocks: the Cantors dust method-comment, Tectonophysics, 198, 189197. HIRATA,T. 1989. Fractal Dimension of fault systems in Japan: fractal structure in rock fracture geometry at various scales. Journal of Geophysical Research. 94, 7507-7514 JACKSON,D. I., JACKSON,A. A., EVANS,D., WINGEIELD, R. T. R., BARNES,R. P. & ARTHUR, M. J. 1995. United Kingdom offshore regional report. the geology of the Irish Sea. British Geological Survey. MILLWARD, O., BEDDOE-StEPHENS, B., WILLIAMSON, I. T., YOUNG, S. R. & PETTERSON, M. G. 1994. Lithostratigraphy of a concealed caldera-related ignimbrite sequence within the Borrowdale Volcanic Group of west Cumbria, Proceedings of the Yorkshire Geological Society, 50, 25-36. NIREX, 1993. The Geology and hydrogeology of the Sellafield area, Volume 1: The Geology. Nirex report 524. NIREX, 1995a. The Geology of the Sellafield Boreholes Nos. 8A and 8B. Nirex report 638. NIREX, 1995b. Sellafield geological and hydrogeological investigations. Factual report-compilation of maps and drawings, Volume 1 of 2. Nirex report SA/95/ 02.

Forward modelling of the physical properties of oceanic sediments: constraints from core and logs, with palaeoclimatic implications C. LAUER-LEREDDE, 1'2, P. A. PEZARD, 1'3, F. TOURON 4 & I. D E K E Y S E R 2 t Laboratoire de Mesures en Forage (ODP), IMT, 13451 Marseille cedex 20, France 2 Centre d'Oc~anologie de Marseille, CNRS (URA 41), Universitd d'Aix-Marseille H, 13288 Marseille cedex 09, France 3 Laboratoire de POtrologie Magmatique, CNRS (UPRES A 6018), CEREGE, 13545 Aixen-Provence cedex 04, France 4 Gafa Entreprises, 16 Boulevard Notre-Dame, 13006 Marseille, France

Abstract: A new methodological approach based on the analysis of core data, logs and highresolution electrical images of borehole surfaces (FMS) is developed in order to improve the study of oceanic sediments from physical properties. This approach is tested on data obtained in the context of the Ocean Drilling Program (Japan Sea, Leg 128, Hole 798B). The downhole measurements and FMS images exhibit a cyclic pattern reflecting variations in oceanic surface productivity combined with continental aeolian supply due to palaeoclimatic changes. On the basis of m-scale physical measurements, cm-scale FMS images and measurements on core, the objective is to deconvolve the variations in sedimentary supply of oceanic and continental components through time and to compute the intrinsic formation factor versus depth. The latter topic is approached in two ways: first by conventional log analysis, then with a new iterative forward modelling method. In the second case, the low frequency electrical resistivity log (SFL) is modelled using a numerical modelling code (Resmod2D e:) in order to obtain an accurate formation electrical resistivity model (Rt), where individual beds are derived from FMS images. An analytical routine is also used to model the natural gamma-ray measurement (CGR). While the conventional log analysis allows deconvolution of the sedimentary supplies, the forward modelling leads to a greater resolution and accuracy in more precise sediment characterization, such as that obtained from the derivation of the formation factor.

Unlike the core material, downhole logs provide continuous high resolution records. The logs reflect the physical and chemical variability of the drilled sequence. Several logging tools based on widely varying physical principles (electric, acoustic, nuclear . . . . ) are used. The logs offer different perspectives about changes in sediment composition. Hence, extracting sediment characteristics or palaeoclimatic information from downhole logs appears as a promising field of application. The principal limitation on integrating logging data for sedimentary and palaeoclimatic studies is the absence of a generally applicable method to transform logging data into reliable sediment physical properties or palaeoclimatic data. The major objective of this study is to develop a new methodological a p p r o a c h using, in combination, logging, coring data and highresolution electrical images (FMS) to derive the detailed structure of near sea-floor sediments. The method is tested on data obtained in the

context of the Ocean Drilling Program (ODP) at Oki Ridge in the Japan Sea (Leg 128 Hole 798B). Core data are first used to construct a mineralogical model of the sedimentary formation at Site 798. Classical log analysis is then applied to deconvolve the different sedimentary inputs and to compute a continuous formation factor (FF), which offers a tool to describe the sediment pore structure. However, this approach is limited by the vertical resolution of each sensor, that is half a metre on average for traditional downhole measurements (e.g. logs). An original approach combining analytical and numerical modelling is proposed here to perform a small scale analysis of downhole logs through the computation of the formation factor.

Geological setting at Site 798 Site 798 (37~ 134~ is located in the southeastern Japan Sea, about 160km north of the western coast of Honshu. The site is

115 LAUER-LEREDDE,C., PEZARD,P. A., TOURON,F. & DEKEYSER,1. 1998. Forward modelling of the physical properties of oceanic sediments: constraints from core and logs, with palaeoclimatic implications. In: HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 115-127

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Fig. 1. Location map of the area surrounding Hole 798B.

positioned over a small sediment-filled graben on top of Oki Ridge, in 911.1 m water depth (Fig. 1). A 517m thick sediment sequence of late/early Pliocene to Holocene age was drilled; diatomaceous ooze, diatomaceous clay, silty clay, clay, and siliceous claystone are the predominant sediments. The primary drilling objective at this site was to obtain a complete Neogene sequence of pelagic-hemipelagic sediments deposited above the local carbonate compensation depth (CCD), currently near 1500 m, in order to obtain a detailed description of the sedimentary input at the site. The strategically positioned location and the high avera/~e sediment accumulation rate (about 12 cm ka -~) at Site 798 are ideal to study the local sedimentology in relation to global palaeoclimatology. This site is of great interest for two prevailing sedimentary supplies are defined from smear slides observations (Ingle et al. 1990) and FMS images (Fig. 2). The upper 300 m of FMS images (late Pliocene/Pleistocene) are characterized by rythmic changes between dark, laminated, diatom- and organic carbon-rich conductive intervals, and light-coloured, nonbioturbated to bioturbated, clay-rich, resistive intervals (F611mi et al. 1992). To investigate the sedimentary origin of these cycles, Dunbar et al. (1992) analysed a total of 913 samples for biogenic opal content (Fig. 3): major features of the opal record are a general trend of increasing opal fraction with depth, and cyclic variations between high and low values at a period of approximately 40 ka. The opal content varies between 3 and 43 Wt% in the upper 320m. DeMenocal et al. (1992) also analysed contiguous samples over three intervals located between 100 and 320 mbsf (metres below sea

Fig. 2. Formation MicroScanner (FMS) micro-resistivity images from the ODP Hole 798B (from 200 to 300 mbsf). The images are azimuthal traces of the four pads pressed along the borehole wall. Black represents low resistivity, and white, high resistivity.

FORWARD MODELLING OF OCEANIC SEDIMENT PHYSICAL PROPERTIES Core

SVL (Ohm m)

Opal

recovery _

o~

~o

(wt%) 20 30

117

0.45

40

so

loo

0.55

0.65

100

OPAL (%) II0

10

.

lo

,,0

110

120

120

130

130

100 I

s

140

m

140

8 150 200 i

150

Fig. 4. Correlation between the SFL log and opal percent measured on core in ODP Hole 798B (after DeMenocal et al. 1992).

I

No data 2so

300

Fig. 3. Weight percent biogenic opal versus depth in ODP Hole 798B (after Dunbar et al. 1992).

floor). These samples were analysed for major sediment composition: biogenic opal content varies between 5 and 40%, and terrigenous silts and clays, between 40 and 80%. Core-log correlations were established using ash layers identified in core photographs and Formation MicroScanner T M (FMS) images. High opal values result in low gamma ray, bulk density, grain densities, and resistivity log values. There is a close correspondance between the SFL and the opal data (Fig. 4). Low opal content is balanced by increases in terrigenous sediment, and this is recorded by high gamma ray log values (DeMenocal et al. 1992). Core-log comparisons therefore demonstrate that log cycles reflect variations in terrigenous sediment supply and diatomaceous opal. Diatom tests are

the dominant opaline component throughout the upper 300m; radiolarians and silicoflagellates contribute in a minor way to the opal flux (Ingle et al. 1990). The periodicity of the sedimentary cycles was estimated with standard-power spectral analysis method (Imbrie et al. 1984): the power spectra of the gamma-ray (SGR) time series showed a peak at about 40 ka, probably a climatic expression of the 41ka 'Milankovitch-type' cyclicity (DeMenocal et al. 1992). This suggested that the earth obliquity was the driving factor of climate and sedimentary supply in this region over the last 3 Ma. The diatomaceous sediments of the dark facies, and the terrigenous-rich signature of the light-coloured lithofacies suggested that these cycles also reflect variations in oceanic surface productivity combined with continental aeolian dust from central Asia, as a consequence of palaeoclimatic changes. The terrigenous mineralogy assemblage is similar to that of Chinese loess, a probable up-wind source of the aeolian dust. The Chinese loess deposits may indicate a linkage between glacial climate and Asian aridity (Kukla et al. 1988), so the periodic increases in terrigenous concentration may reflect the downwind propagation of this signal.

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Table 1. Chosen physical properties fop" mah7 components Phase Continental

Component

Densit~r gcm -

PEF ba e 1

CEC meq gq

Illit e Chlorite Kaolinite Smectite Quartz

2.50 2.60 2.42 2.12 2.65

3.5 6.3 1.83 2.04 1.8

0.1-0.4 0.05-0.4 0.03-0.15 0.8-1.5 0

Table 2. Clay composition re[erred to 100 Wt% clav.fi'action Zones* (mbsf) Za (200 220) Zb (220~ 225) Zc (225- 260) Zd (260 280) Ze (280-300)

Illite (%)

Chlorite (%)

Kaolinite (%)

Smectite (%)

85 80 90 85 80

10 10 10 10 5

0 10 0 5 10

5 0 0 0 5

*The studied interval was split into five zones, each with a constant clay mineralogy, on the basis of Dersch & Stein data (1992}.

Core and log data This work is focused on a 100-m-long interval (from 200 to 300 mbsf) because of the wellexpressed cyclicity over this segment (Fig. 2). Mineralogical model

In the following study, the sediment physical properties and the main mineralogical components are used to compute the relative proportions for oceanic and continental supplies, and to determine the formation factor (FF). A first order mineralogical model of Hole 798B is deduced from smear slide observations of dominant lithologies, and from previous sediment composition studies. The oceanic input, essentially diatoms, is associated with opal, on the basis of Ingle et al. (1990), DeMerlocal et al. (1992), and Dunbar et al. (1992) works. The continental one is deduced from Dersch & Stein (1992) core analyses at Site 798. In order to get information about the composition of the terrigenous sediment fraction, Dersch & Stein (1992) determined the average amounts of quartz and clay minerals. The entire sequence is characterized by quartz amount ranging between 5 and 20%. In the upper 413m, the clay fraction is dominated by illite with values between 60 and 88% and chlorite, between 0 and 27%. Calcareous components are either absent or poorly preserved,

and carbonate contents average less than 4% between 200 and 455 mbsf. In this section, volcanic ash layers are thin and scarce. The continental input is therefore assumed to be composed at this site of four clay minerals (illite, chlorite, kaolinite, and smectite) and quartz. Six components are consequently taken into account in this study, and characterized by three physical properties: density (g cm 3), photoelectric-effect (ba e q ) and cation exchange capacity (meq g l). The reference values for each of these components (Table 1) are chosen from the literature (e.g. Grim 1968; Fertl & Frost 1980; Juhasz 1981; Caill6re et al. 1982; Drever 1982; Schlumberger 1994). Our objective is to obtain information on oceanic and continental supplies, rather than on the relative fractions of the main mineral components.The proportions of each element for the continental phase are, however, needed in order to estimate the physical properties of this phase. A short interval from 200 to 300 mbsf (1.7 to 2.5 Ma) was chosen as a first step of this analysis. This interval was divided in five zones characterized by average clay fractions (Table 2), on the basis of previous analyses (Dersch & Stein 1992). This division in zones allowed to simplify the mineralogical model still further, and to reduce the number of unknowns: for example, the continental phase is constituted with only three elements (illite, chlorite, quartz) for zone C (Table 2). The sediment averages

FORWARD MODELLING OF OCEANIC SEDIMENT PHYSICAL PROPERTIES Natural G a m m a Ray CGR (API) 200

20

40

Electrical Resistivity

B u l k density

(fl m)

(g ce-l)

60

0.4

0.5

0.6

0.7

1.4

1.6

Photoelectric effect (ba/e') 1.9 2.2

2,6

119

N e u t r o n Porosity

(%)

I

6O

I

70

I 'T'" I

8O

I~l

II

220

240

g

260

280

41 Fig. 5. Downhole measurements from 200 to 300 mbsf in ODP Hole 798B.

10% quartz for the whole interval, 50% clay between 200 and 260 mbsf and 60% clay between 260 and 300 mbsf (Ingle et al. 1990, Dersch & Stein 1992). The percentages of the continental phase are then deduced for each zone.

Downhole measurements

At the completion of coring operations at Hole 798B, four logging runs were completed from 70 to 518 mbsf. During the first phase, the phaser dual induction tool (DIT), the long-spacing digital sonic tool (SDT), and the natural gamma-ray spectrometry tool (NGT) were run (seismic stratigraphic tool string). The second phase consisted of the lithoporosity combination tool string including the lithodensity (LDT), compensated neutron (CNT-G) porosity, and natural gamma-ray spectrometry (NGT) tools. After the Formation MicroScanner T M (FMS) had been lowered downhole, the geochemical tool string, including an induced gamma-ray spectroscopy tool (GST), an aluminium clay

tool (ACT) and the NGT, was run (Ingle et al. 1990). The depths of investigation are sensordependant, and data are typically recorded at intervals of 15cm. The quality of the logs obtained is generally excellent: most of the logs reflect variations in biogenic opal production (diatomaceous) resulting from glacial-interglacial changes in surface productivity (Matoba 1984; Zheng 1984; Morley et al. 1986). Downhole geophysical logs (m-scale). The following analysis focuses on the resistivity (SFL) and natural gamma ray (CGR) logs from 200 to 300 mbsf in Hole 798B. These logs were selected because the resistivity is influenced both by the clay fraction and diatoms (oceanic productivity input), whereas the natural gamma ray is mainly sensitive to the clay fraction (aeolian continental input in this case). The CGR is often used to indicate downhole variations in clay minerals content, because it reflects gamma-ray radioactivity from the decay of potassium and thorium which are common elements in clay mineral structures (Hassan et al. 1976). The

120

C. L A U E R - L E R E D D E E T AL.

CGR may then serve as a proxy of variations in terrigenous aeolian component.

Deconvolution o f cont&ental and oceanic inputs

Throughout the studied depth interval, the highest CGR value corresponds to the highest bulk density values (Pb), the highest resistivity values (R0), and the lowest porosity values (~b) (Fig. 5), reflecting a high terrigenous content relative to the biogenic supply. Terrigenous clays have high K and Th contents and relatively higher density and lower porosity than sediment with higher opal content: the clay particles filling the pores induce a lower porosity, hence a higher resistivity. Porosity is, to a first order, proportional to the inverse square-root of resistivity (Archie 1942). The sediments rich in diatomaceous opal commonly have high porosities because of the intrinsically high porosity of diatoms themselves. These cycles are also apparent in the recovered sediment record: the high gamma-ray, high density, high resistivity, and low porosity levels correspond to the massive clay-rich intervals, whereas the low density, low gamma-ray, low resistivity, and high porosity units correspond to the darker, diatom-rich intervals.

The aim is to analyse the downhole measurements in order to deconvolve both oceanic and continental inputs. The model of the formation consists of only two known inputs in unknown proportions. Bulk and matrix densities (Pb, Pma, g CC-1) and photoelectric effect (Pef, ba e-1) were chosen to define proportions of the two components. Whereas Pb responds primarily to porosity, the Per responds primarily to rock matrix (lithology). The combination of Pb and the Pef, the photoelectric absorption cross-section (Schlumberger 1994), is:

High-resolution (cm-scale) electrical images. The Formation MicroScanner TM (FMS) creates a picture of the borehole wall by mapping its electrical conductance using an array of 16 small and pad-mounted electrodes on each of four pads (Ekstrom et al. 1986; Luthi & Banavar 1988; Pezard et al. 1990). FMS data are recorded each 2.5 mm as the tool moves up the borehole. The vertical resolution of individual features is about a centimetre. The tool can, however, detect thinner features, provided they have sufficient resistivity contrast to the surrounding matrix. The images registered with the FMS show qualitative conductivity changes, particularly due to the different physical properties of the beds (for example porosity, resistivity of pore fluid or the presence of clays). The electrical images obtained at Hole 798B (Fig. 2) resolve the cyclicity of sedimentary processes at the site extremely well. Light (dark, respectively) colour is related to the continental (oceanic) input.

Log analysis The downhole logs and core measurements, associated with the proposed mineralogical model, are used here to determine variations in sedimentary inputs, and to compute the formation factor.

U = Pef x Pb

(1)

and obeys a linear mixing law such as: U=~

Uf-~-(1--(~) Uma

(2)

where U, Uf, and Uma are for example the photoelectric absorption cross-sections of the media, pore fluid and matrix, respectively. As our matrix consists of a mixture of two inputs (oceanic and continental) with relative weight fraction (#o and #c) and photoelectric absorption coefficients Uo and Uc: Uma =/to Uo + #c U~= Pef • Pma

(3)

The relation necessary to solve for these two unknowns is the closure relation of partial fractions: l=#o+#c

(4)

The solution can most easily be seen in terms of the matrix representation of the set of simultaneous equations: A=R Y

(5)

where A is the vector of measurements, R is the matrix of known coefficients, and Y is the vector of unknown volumes. The porosity and density logs are first used to compute the matrix grain density Pma. The wet bulk density Pb is related to the porosity through a simple mixing law: Pb = Pwqb + Pma (1 -- qb)

(6)

where Pw is the density of seawater. On the basis of our preliminary mineralogical model which consists of six major components (opal, illite, chlorite, kaolinite, smectite, quartz),

FORWARD MODELLING OF OCEANIC SEDIMENT PHYSICAL PROPERTIES Uo and Uc are computed for the five zones previously determined (Table 2) as follows: Uo = Pef(opal) • Pma(opa,)

u~ = L #i • Pefi • Pmai

(7)

(8)

i=1,5

with ~i as weight percentage of the component i in the continental phase. The system resolution leads to the weight fraction of both oceanic and continental phases.

Computation o f the f o r m a t i o n f ac t or The electrical resistivity of saturated sediments is usually quoted in terms of a formation factor (FF) to remove the effect of the pore-fluid resistivity, because the grains themselves are considered as insulators (Archie 1942): FF = Ro Rw-1 = Cw Co 1

(9)

where Ro (respectively Co) is the resistivity in 12 m (conductivity in S m -1) of the porous medium, and Rw (respectively Cw), the resistivity (conductivity) of the pore-fluid. The formation factor of the porous medium depends on the intrinsic geometry of the pore channels, and therefore describes the manner in which the grains are arranged in a sedimentary formation (Winsauer et al. 1952). Archie's equation is generally considered to apply satisfactorily to clean sands. The presence of clay minerals, however, has a detrimental effect on Co computations: the capacity of a clay to exchange cations at the pore-mineral interface induces the presence of a surface conductivity term (Waxman & Smits 1968). A resistivity model taking into account the effects of dispersed clays was proposed by Waxman & Smits (1968) and Waxman & Thomas (1974): Co = (Cw+ BQv ) FF -1

(10)

Qv=pma CEC (1-~)qb -1

(11)

where B represents the equivalent conductance of clay-exchange cations (S m 2 meq-1), as a function of salinity and temperature, Qv describes the cation exchange capacity or CEC (meq g-a) per unit pore volume (meq cm 3), and Pma (g cm-3) is the matrix grain density of the sediment. In the following, the successive stages of the computation of the formation factor are detailed.

121

Cation exchange capacity. Values of CEC can be measured directly on rock samples, but not directly in situ. Several attempts have consequently been made to derive CEC from existing logs. Previously developed CEC or Qv estimates from well-logs based on the spontaneous potential curve (Smits 1968; Johnson 1978), dielectric constant (Kern et al. 1976), reservoir porosity (Lavers et al. 1974; Kern et al. 1976; Neuman 1980) and gamma ray (Koerperich 1975; Clavier et al. 1977; Johnson 1978) have been discussed. For this study, the correlation between the natural radioactivity from K and Th elements (CGR) and the CEC was selected. Most shale are radioactive due to the presence of K 4~ in the potassium-bearing clay mineral illite. A correlation between gamma ray counts and CEC may then be expected. Johnson (1978) showed such a correlation for formation containing largely illite and kaolinite where the relatively high gammaray count of the illite corresponded to high potassium content thus making it an excellent shaliness indicator. Scala (see Clavier et al. 1977) found a strong correlation between gamma ray count rate divided by the porosity and Qv. In other words, gamma ray log can be used in some cases and after calibration on core as a substitute of the CEC measurement. On the basis of Scala data, we estimated the proportionality constant between the two quantities as follows: CEC = (0.005) CGR

(12)

The computed values of CEC are then converted into Qv using (11). To check the validity of (12), an analytical maximum and minimum CEC are estimated with CEC values from Table 1 in each zone (Table 2). Using the relative proportions of each clay mineral, an estimate of the CEC of the clay assemblage can be computed, using a linear summation.

Formation factor. Waxman & Thomas (1974) found that B can be related to an exponential function of the conductivity, and Juhasz (1981) proposed the following expression: B = -(1.28) + (0.225)T-(0.0004059)T 2 (13) 1 + Rw kz3(0.045T--0.27) where T is the temperature in ~ and Rw the fluid resistivity in f~m. The mean value of B obtained for Hole 798B from (13) is 3.8 S m2meq -1. Continuous FF values versus depth may then be evaluated from (10).

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C. LAUER-LEREDDE ET AL.

Fig. 6. Log analysis results. (a) Grain density; from core (solid squares) and computed (solid line) (b) Opal fraction from core (after Dunbar et al. 1992) and computed. (c) Computed continental sedimentary fraction. (d) Computed CEC (derived from CGR) and Qv values. (e) Computed formation factor from definition (dash) and of Waxman & Smits (1968) (solid).

Results Grain density. The computed matrix density (Fig. 6a) exhibits a high degree of variability. The matrix density reflects the varying clay and diatom contents. Diatoms tend to have low densities, sometimes lower than 2.0g cm -3, whereas clay minerals have densities ranging as high as 2.80g cm -3 (Johnson & Olhoeft 1984). The estimated values and the core measurements are in general agreement over the interval, although fine-scale correlations between the two quantities are difficult. This difficulty results mainly from the discrepancy between the core and log measurements themselves. One of the problem with gaseous sediment is that the core recovery is often fragmented and the section is expanded and disturbed, leading to differences between the core and log depth-scales (e.g.

Hagelberg et al. 1992). Hence, measurements on core cannot been compared readily with downhole logs. Oceanic and continental fractions. The agreement between the reconstructed opal fraction curve and core measurements from Dunbar et al. (1992) is very good throughout the section (Fig. 6b), although more measurements in the upper part would be desirable for a better comparison. The major variations are well in phase: for example, Dunbar et al. measured an abrupt decrease of opal content at about 269 and 287 mbsf, and our estimated values present the same feature. Moreover, fine variations appear in the reconstructed opal fraction curve. The amount of computed opal is however overestimated, especially in the upper part (about 20%). This last point might originate in the

FORWARD MODELLING OF OCEANIC SEDIMENT PHYSICAL PROPERTIES mineralogical model: the oceanic input is assumed to be entirely opal, whereas other components are also present. For example, we considered as insignificant the biogenic carbonate component, although oceanic intervals are enriched in foraminiferas, essentially in the upper 250 mbsf. Also, the values of the physical properties chosen for this first-step model are only reference values. The true values for the components at Hole 798B are not known exactly. An additional cause of the difference between core and computed opal might be the methods chosen by Dunbar et al. (1992) to measure the opal content. They used a timeseries dissolution technique and a one-step dissolution method. In general, the results from both techniques are comparable, but the onestep method tends to yield opal contents consistently lower by 5 to 10% in enriched samples. As measurements on core for the continental fraction was not available, a precise comparison to validate our model was not possible. The computed values ranging between 40 and 90% (Fig. 6c) are in agreement with the measurements of Ingle et al. (1990). Moreover, the reconstructed continental fraction curve is characterized by a significant increase in clay content between 278 and 286 mbsf, as suggested by the increase in gamma-ray, bulk density and resistivity (Fig. 5). Cation exchange capacity. The computed CEC and Qv logs (Fig. 6d) follow the variations of clay abundance and are restricted to analytical boundaries. The proposed proportionality constant fits well. Guo (1990) measured the CEC of several loess samples from China. The CEC ranged between 0.07 and 0.28 meq g 1, which is within the range of the present results. Formation .factor. The formation factor derived

from the Archie formula is lower than that derived from Waxman & Smits formula (Fig. 6e), particularly in high resistivity zones. The data set can be represented by a regression similar to that proposed by Winsauer et al. (1952), and such as F F = a ~ .... , with a =1.45 and m = 2.38 (Fig. 7). This result is in the range typical of marine sediment. In a similar approach, Henry (1997) analysed clay-rich sedimentary samples for CEC from the Barbados wedge (ODP Site 948), similar to those from Oki Ridge: the electrical resistivity varies from 0.5 to 0.8 f~ m, the porosity is larger than 50%, and the grain densities measured on samples are close to 2.80 g cm 3. The CEC measured on core by Henry (1997) ranges between 0.2 and 0.5 meq g-i

123

Fig. 7. Formation factor versus porosity plotted on double logarithmic scale. Jackson et al. (1978) and Taylor Smith (1971) results are displayed (A, B, C, D, E; F). The present data (G) define a trend described by: FF = 1.45qb-2'38.

and the relationship F F = 1.24qb 2.31 is close to that from Oki Ridge. Taylor Smith (1971) analysed samples from Mediterranean Sea clays and found a m value close to 2.20 (Fig. 7). The results of Jackson et aI. (1978) show that the exponent m depends entirely on particle shape for unconsolidated sands (Fig. 7). Similar measurements on assemblages of shell fragments, kaolinite particles, and marine illite clays produce similar values of m (close to 2.0), suggesting that the platey nature of the particles within clays controls the relationship between FF and qb. The high value of m derived for Oki Ridge sediments is then in agreement with similar results in formations with large amounts of clays (Jackson et al. 1978), especially illite. The results obtained for Oki Ridge sediments are also typical with regard to the large spread of F F values. This spread reflects the change in shape of particles in relation to the supplies cyclicity. High values of FF correspond to the continental input, i.e. clays, whereas low values of FF correspond to the oceanic input. As a conclusion, the simple mineralogical model used here appears as well adapted to the description of sedimentary formations with high porosities (greater than 60 %) and clay content. These first results also demonstrate that the computation of the oceanic and continental

124

C. LAUER-LEREDDE ET AL. C G R (API) --- computed - - measured

200

30 tl%[

I

50 I

C G R model (API) 30 I

I

~

I

50 i

S F L (f2 m) ... computed - - measured I

II

I 4

0.5 1

]

0.7 I

Rt model (fi m) 0~5 I

[

I

0.7 I

FFs ... from raw logs -- from modelled logs t

II

2.5 ;ll=1

3.5 [

4,5

205

"-~21q

l

(a)

(b)

(c)

(d)

(e)

Fig. 8. Forward modelling results. (a) Computed and measured gamma ray values (CGR). (b) Formation natural gamma ray, from K and Th, model expressed in terms of CGR. (c) Computed and measured electrical resistivity values (SFL). (d) Formation electrical resistivity model (Rt). (e) Formation factor as determined from downhole measurements and the numerical model

fractions using the photoelectric absorption cross section (U) is in agreement with core measurements and, so, might be used to predict the core data. Nevertheless, the vertical resolution of the downhole logs and the computed formation factor is rather poor in some zones, especially in the upper part of the section. A new forward modelling method is therefore proposed in the following to improve the vertical resolution and derive more accurate Rt, CGR and FF profiles. Forward

model

The aim is to obtain an accurate formation resistivity model (Rt) from the numerical modelling of the electrical resistivity log (SFL), constrained by the high-resolution electrical images of borehole surfaces (FMS). A statistical

method is also used to model the natural gamma ray data (CGR). This study is restricted to a 20 m-long interval (200-220 mbsf/1.6 to 1.9 Ma).

Numerical modelling Modelling code. Resmod2D ~ is a newly developed two-dimensional finite element numerical code. It allows modelling of the response of electrical resistivity downhole probes, such as the Spherically Focused tool (SFL). In brief, a formation resistivity model composed of horizontal sedimentary beds (layers) with fixed thicknesses and resistivities, is entered in the code in order to compute the response of the probe in front of this formation. The resistivities of the model are referred to as 'true', whereas the computed resistivities (so the simulated response of the tool) are referred to as 'apparent' because

FORWARD MODELLING OF OCEANIC SEDIMENT PHYSICAL PROPERTIES in an inhomogeneous formation, it depends on the resistivity of the bed next to the probe and also that of the adjacent formations. The measure of the resistivity by the SFL is therefore lower than the true resistivity. Res&tivity modelling. Using Resmod2D :t:, a formation resistivity model (Rt) is created on the basis of cm-scale electrical images (FMS) for bed thickness and m-scale electrical log (SFL) for individual bed resistivity. The layer boundaries are determined from FMS images by distinct colour contrasts; the gradual transitions are disregarded here. This initial model is then processed with Resmod2D ~ to simulate the SFL tool and compute a theoretical resistivity log (SFLc). By comparison between SFLc and SFLm, the formation model is modified step by step (resistivity values and frame). Thickness changes as well as layer additions in the iterations are constrained by FMS images. This process is iterated until the best fit between SFLc and SFLm is obtained. Each processing lasts about four hours for an evaluation every 5 cm and over a 20 m-long interval (from 200 to 220 mbsf). Natural gamma ray modelling. A statistical method is used to model the natural gammaray tool in an analytical manner. The aim is to determine the natural gamma activity of each layer defined in the resistivity model. This simple method is based on an exponential attenuation of the gamma ray flux versus depth (Ellis 1987). Using the lithological frame established for the resistivity model, and the natural gamma ray measurements (CGRm), an initial model is established to estimate the C G R (CGRc). By comparison between CGRc and CGRm, the model is modified step by step and the chosen model corresponds to the best fit between CGRc and CGRm. Results Formation electrical resistivity and gamma ray models. The chosen models, obtained after about 80 iterations, correspond here to a near-perfect fit between the measured and computed values (Figs 8a, 8c). The initial Rt model took into account the major layers seen on the FMS images, not the discrete ones. The SFLc then presented the major variations of the SFLm but not the small ones. In order to reproduce these small events and to take the gradual transitions into account, the basic frame was refined by adding small layers. Whereas the initial models were composed of 47 layers, the chosen ones are

125

made of 76 layers, the smallest one measuring 8 cm and the greatest, 90cm. In order to validate the models, several precision and robustness tests were run. For example, the error E between the downhole logs and the computed theoretical logs was computed using Whitman (1989) method: E(%) + 100 (10 El~

E l o g = ~l Z i=l,U

1)

(log(mi) -- log(ci)) 2

(14)

(15)

N

where N is the number of records for the chosen downhole log, and mi (respectively ci) is the value of the record i for the downhole log (for the computed log). This estimated error is on the order of 1 % for the SFL, and 3 % for the CGR. Due to the integration of high-resolution FMS results, the models (Figs 8b, 8d) have a much better resolution than the raw logs (Figs 8a, 8c). Whereas the logs show essentially three continental events between 200 and 220 mbsf, the models show the same main three events, but also several small ones. For example, while the SFL seems rather linear over the first five metres, the Rt model brings out an alternance of minor troughs and peaks suggesting little changes in lithology, such as clay content decreases corresponding to the troughs. Whereas other lowresistivity units seem to be massive on the SFL, they are composed of several fine layers in the model. The high-resistivity units, related to colder periods, are characterized in the raw log by two or three regular peaks, whereas the model displays a much more irregular profile. The major peaks are more pronounced and the contrast is greater: the peak at about 211.15 mbsf has a value of 0.58 f~ m for the raw log, and 0.80 f2 m for the model. Moreover, the transition from the low-resistivity interval upward into the high-resistivity one is rather gradual for the raw log, whereas the model seems to show that a sharp boundary is present at the base of the high-resistivity unit, suggesting an abrupt initiation of each glacial period. All these features revealed by the models are confirmed by core observations (Ingle et al. 1990): the vertical lithologic variations within the dark/light cycles are remarkably constant. The dark-coloured intervals are either thinly to thickly laminated and finely bedded. These intervals generally possess a well-defined and sharp base that grades upward into light-coloured/high-resistivity sublayer (Ingle et al. 1990); the lower portion of the light-coloured intervals is commonly gradational

126

C. LAUER-LEREDDE ET AL.

and mostly obliterated by bioturbation. The determination of the boundaries in the model seems therefore to be relatively accurate. Formation factor, The formation factor is computed for the modelled logs (Rt and CGR) on the basis of the method presented in the log analysis section. The results for the F F model are similar to those for the Rt model. The F F model has a better resolution than the F F computed from logs, as the former displays small events not detected by the latter (Fig. 8e). The major peaks are also more pronounced: the peak at about 211.15 mbsf has a value of 3.48 for the raw log, and 4.62 for the model. Changes between glacial and interglacial periods also present the same characteristics as the Rt model (sharp transition from dark laminated interval upward into lightcoloured interval, and gradual transition from light interval upward into dark one).

Conclusions The first results of the method using the raw logs show that the proposed mineralogical model is well representative of the sediment from ODP Hole 798B. The photoelectric absorption crosssection (U) allows differentiation and computation of the oceanic and continental fractions. The vertical resolution of the raw logs and of the computed formation factor curve being however poor in some zones, a new forward modelling method is proposed in this paper to improve this study. The m-scale electrical log (SFL) and the natural gamma ray log (CGR) are modelled using cm-scale electrical images (FMS) to define and map high-resolution layers, The first results show that the Rt, C G R and F F models are more precise than the Rm, C G R and FF obtained from log analysis, insofar as the models bring out small layers not detected by the raw logs. The modelling approach allows the study of changes from oceanic to continental supply hence from interglacial to glacial periods: the continental input tends to increase abruptly from warm periods to colder ones suggesting abrupt initiation of glacial cycle, whereas it seems to decrease gradually from cold to warmer periods. This study provides a continuous description of changes in intensity of the different sedimentary sources within the analysed interval. While FMS images reveal the presence not only of large-scale layers but also of thin (short) events, the numerical modelling enhances the nonlinearity of m-scale logging devices, stressing

for example the importance of cm-scale resistive beds on the response of m-scale logs. This case study, which now requires additional measurements on core to further improve the precision of the method, could become a parallel method to obtain meaningful physical properties and palaeoclimatic data from future sites. This work was carried out by the main author with financial support from the French 'Minist~re de la Recherche et de l'Enseignement Sup6rieur'. The authors wish to acknowledge the contribution of GaYa Entreprises (Marseille) for the use of the forward modelling code Resmod2D". They also wish to thank S. Brower (LDEO) for providing the logging data, C. Robert (COM, Marseille) and the two anonymous reviewers for their helpful comments.

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Centre-Ouest de la Chine: approache micromorphologique. PhD Thesis, University of Paris 6. HAGELBERG, T., SHACKELTON, N., PISlAS, N & The Shipboard Scientific Party 1992. Development of composite depth sections for Sites 844 through 854. Proceedings of the Ocean Drilling Program, Initial Reports, 138, 79-85. HASSAN, M., HOSSIN, A. & COMBAZ, A. 1976. Fundamentals of the differential gamma-ray log. Transa c t i o n S P W L A , 17th A n n u a l L o g g i n g Symposium, Paper H, 1-18. HENRY, P. 1997. Relationship between porosity, electrical conductivity and cation exchange capacity in Barbados Wedge sediments. Proceedings of

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CRC Handbook of Physical Properties of Rocks. Boca Raton, FL (CRC Press, Inc.), 3, 1-38. JUHASZ, I. 1981. Normalised Qv - - the key to shaly sand evaluation using the Waxman-Smits equation in the absence of core data. Transaction SPWLA 22nd Annual Logging Symposium, Paper Z. KERN, J. W., HOYER, W. A. & SPANN, M. M. 1976. Low porosity gas sand analysis using cation exchange and dielectric constant data. Transaction of the Society of Professional Well Log Analysts, 17th Annual Logging Symposium, Paper PP, 17 p. KOERPERICH,E. A. 1975. Utilization of Waxman-Smits equations for determining oil saturation in a low-

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salinity, shaly sand reservoir. Journal of Petroleum Technology, 27, 1204-1208. KUKLA, G., HEELER, L., MING, X., CHUN, X. T., SHENG, L. T. & SHENG, A. Z. 1988. Pleistocene climates in China dated by magnetic susceptibility. Geology, 16, 811-814. LAVERS,B. A., SMITS,L. J. M. & VANBAAREN,C. 1974. Some fundamental problems of formation evaluation in the North Sea. The Log Analyst, 12, 1-10. LiiTH1, S. M. & BANAVAR,J. R. 1988. Application of borehole images to three-dimensional geometric modelling of eolian sandstone reservoirs, Permian Rotliegende, North Sea, American Association of Petroleum Geologists Bulletin, 72, 1074-1089. MATOBA, Y. 1984 Paleoenvironment of the Sea of Japan. In: OERTLI, H. J. (ed.) Benthos '83 2nd

International Symposium on Benthic Foraminifera, 409-414. MORLEY, J., HEUSSER, L. & SARRO, T. 1986. Latest Pleistocene and Holocene paleoenvironmnet of Japan and its marginal sea. Palaeogeopraphy, Palaeoclimatology, Palaeoecology, 53, 349-358. NEUMAN, C. H. 1980. Log and core measurements of oil in place. Journal of Petroleum Technology, 32, 1309-1315. PEZARD, P. A., LOVELL, M & The Ocean Drilling Program Leg 126 Shipboard Scientific Party 1990. Downhole images: electrical scanning reveals the natur of subsurface oceanic crust. EOS, 71, 709 SCHLUMBERGER 1994. Log interpretation charts. Schlumberger Wireline & Testing, Houston. SMITS, L. J. M. 1968. SP log interpretation in shaly sands. Society of Petroleum Engineers Journal, 8, 123-136, Transaction of AIME, 243. TAYLOR SMITH,D. 1971. Acoustic and electric techniques for sea-floor sediment identification. Proceeding Symposium on engineering properties of sea-floor soils and their geophysical identification, Seattle, Washington. WAXMAN, M. H. & SMITS, L. J. M. 1968. Electrical conductivities in oil-bearing shaly sands. Society of Petroleum Engineers Journal, 8, 107-122. - & Thomas, E. C. 1974. Electrical conductivities in shaly sands--I. The relation between hydrocarbon saturation and resistivity index; II. The temperature coefficient of electrical conductivity. Journal of Petroleum Technology, 6, 213-225. WINSAUER,W. O., SHEARtN,H. M., MASSON, P. H. & WILLIAMS, M. 1952. Resistivity of brine-saturated sands in relation to pore geometry. Bulletin of the American Association of Petroleum Geologists, 36, 253-277. WHITMAN, W. 1989. Inversion of normal and lateral well logs. The Log Analyst, 30, 1-11. ZHENt, H. H. 1984. Paleoclimatic events recorded in clay minerals in loess of China. In."PECSI, M. (ed.)

Lithology and Stratigraphy of Loess and Paleosols. Geographic Research Institute.

Lithological classification within ODP holes using neural networks trained from integrated core-log data G. W A D G E 1, D. B E N A O U D A 1, G. FERRIER l, R. B. WHITMARSH 2, R. G. ROTHWELL

2 & C. M A C L E O D

3

1Environmental Systems Science Centre, University of Reading, PO Box 238, Reading RG6 6AL, UK 2 Southampton Oceanography Centre, University of Southampton, Empress Dock, European Way, Southampton S014 3ZH, UK 3Department of Earth Sciences, University of Wales College of Cardiff, PO Box 914, Cardiff CF1 3 YE, UK Abstract: Neural networks offer an attractive way of using downhole logging data to infer the lithologies of those sections of ODP holes from which there is no core recovery. This is best done within a computer program that enables the user to explore the dimensionality of the log data, design the structure for the neural network appropriate to the particular problem and select and prepare the log- and core-derived data for training, testing and using the neural network as a lithological classifier. Data quality control and the ability to modify lithological classification schemes to particular circumstances are particularly important. We illustrate these issues with reference to a 250 m section of ODP Hole792E drilled through a sequence of island arc turbidites of early Oligocene age. Applying a threshold of > 90% recovery per 9.7 m core section, we have available about 50% of the cored interval that is sufficiently well depth-matched for use as training data for the neural network classifier. The most useful logs available are from resistivity, natural gamma, sonic and geochemistry tools, a total of 15. In general, the more logs available to the neural network the better its performance, but the optimum number of nodes on a single 'hidden' layer in the network has to be determined by experimentation. A classification scheme, with 3 classes (claystone, sandstone and conglomerate) derived from shipboard observation of core, gives a success rate of about 76% when tested with independent data. This improves to about 90% when the conglomerate class is split into two, based on the relative abundance of claystone versus volcanic clasts.

Within the Ocean Drilling Program (ODP), one c o m m o n application of d o w n h o l e logs is to c o m p l e m e n t and calibrate measurements m a d e on cores and to fill in the gaps left by incomplete core recovery. Below the range of the Advanced Piston Corer (typically about 200 m sub-bottom) core recovery is rarely m o r e than about 50% for sediments and 40% for basement rocks ( O D P 1990). D o w n h o l e logs make measurements at ambient temperatures and pressures and sense a volume a r o u n d the borehole greater than the core itself. Such measurements provide a continuous stream of in situ data on the wall rocks and borehole fluids. The above figures for typical core recovery are aggregate values. Our knowledge of the exact depths of recovered core samples is, in general, worse than these figure imply. In the ODP, coring advances in steps of about 9 . 7 m (the length of individual core barrels). Unless core recovery for this 9.7 m interval is complete we do

not k n o w the exact sub-bottom depth of the core samples that are recovered, though we may assume their relative original positions are preserved. Thus, at the limit, a continuous 1.5 m section recovered from a 9.7 m core may have originally come from the top or the b o t t o m 1 . 5 m interval. A g r i n i e r & A g r i n i e r (1994) showed that the best estimate of the position within finite limits of any arbitrary length of core sample is given by Euler's Beta distribution. This can be given as a probability density function of position in terms of the lengths of core, section and the n u m b e r and positional order of core sample. Therefore, d o w n h o l e logs play an even more important part in filling the gaps in our knowledge of rock sequences for which there is incomplete core recovery. If we can identify the characteristic ranges of combined log values that correspond to different lithologies penetrated by the hole then we have a means of assigning lithological class labels to the

WADGE,G., BENAOUDA,D., FERRIER,G., WHITMARSH,R. B., ROTHWELL,R. G. & MACLEOD,C. 1998. Lithological classification within ODP holes using neural networks trained from integrated core-log data. In: HARVEY,P. K. t~ LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 129-140

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total interval from the downhole logs. We show how this can be done for ODP data when we can assign the lithological classes from those sections where we do have 'complete' core recovery and extend the classification to the full hole. Our approach has been to develop software that supports a general user in completing this task. There are a number of methods by which such a classification scheme can be driven (e.g. discriminant functions, principal components analysis and cluster analysis; Doveton 1994). We have chosen to use classifiers based on artificial neural networks, principally because of their ability to cope with complex non-linear problems. Also, neural network classifiers have been shown to be of value for lithological classification of downhole logs in hydrocarbon exploration wells, in many cases with superior results to other techniques such as discriminant analysis (Baldwin et al. 1990; Rogers et al. 1992; Wong et al. 1995). Goncalves (1995) reports similar findings for ODP data. This paper has three main sections. Firstly we present the main factors involved in the classification task. These are how the problem is defined, the constraints imposed by the data available and how to validate the results and assess performance. Next we describe how we have implemented the neural network method on our computer system. Finally, we present classification results from ODP Hole 792E. The succession represented in this hole is a complex sequence of island arc lithologies that is a good test of the general usefulness of the technique.

ODP lithological classification The lithologies encountered in ODP holes are usually deep-sea sediments and oceanic crustal rocks and are generally distinct from those encountered during drilling in sedimentary basins underlain by continental crust. Deep sea sediments are often relatively unconsolidated and rich in carbonates and/or silica or composed of terrigenous or volcanic detritus; they may be underlain by a basaltic basement. Porosity is typically high, with ubiquitous saturation by sea-water. The classification framework of Mazzullo et al. (1987) is widely employed for sediments by ODP. The highest-level division is into granular and chemical sediments. Granular sediments are subdivided into pelagic, neritic, siliciclastic, volcaniclastic and mixed sediments and chemical sediments into carbonaceous, evaporites, silicates/carbonates and metalliferous sediments. Below this level classes are based on principal names (e.g. ooze, chalk) together with major or minor modifiers (e.g. nannofossil,

foraminiferal). This nomenclature is applied by the shipboard petrologists when the cores are split and each 1.5m section of recovery is described individually on the Visual Core Description sheets in freehand. This description may show some variation from one petrologist to another. From this initial detailed visual description a more generalised sequence of graphical codes (from a total of about 50) are assigned to each interval of core to denote a lithological class label (e.g. T6=sandstone; C34= foraminiferal chalk) so that a composite graphical log (Barrel Sheet) of all the core recovery can be drawn for publication in the Initial Report series of ODP publications. Beyond this the shipboard scientists can, and sometimes do, erect other, non-standard but complementary, classification schemes, perhaps based on local variation of the sediment. Within the ODP there is no official archived digital version of the lithological classification of core. Hence working on core classification off the ship requires digital recoding of the shipboard scheme(s). The neural network approach is a supervised classification scheme. It requires that a sufficient number of training examples of the logs from each separate lithological class be made available for the algorithm to learn the character of that class. Two general rules apply here. First, if there are too few samples within a class then those samples will not fully represent the distribution of values that the classifier might meet through the whole borehole. Second, the number of samples presented to the classifier from each class should be approximately the same. If the number of samples from one lithology presented to a neural network classifier is much greater than for the other classes then the network will tend to bias its classification in favour of this class. This problem becomes serious whenever the logs do not provide a clear separation of the lithological classes. The quality of log samples used to train the classifier is also important. In addition to checking for spurious outliers we use three logs for quality control. Samples exceeding any of the threshold values for the caliper, density correction and geochemical factor logs are not used for supervising or testing the classifier. Classification choice

The choice of what classification to attempt is of vital importance. The ideal is to have a set of lithological classes that best represents the geological information required from the hole and which produces distinctive responses in the

ODP LITHOLOGY USING NEURAL NETWORKS suite of available downhole logs. The usual starting point will be the principal-names level of shipboard classification of the core. Classes with very few member samples may need to be amalgamated with other classes. The lithological information required from the hole may not be the most obvious. For example, a hole may penetrate a succession of oozes above siliciclastic rocks lying on a basaltic basement. Classifying such a tripartite division should be trivially easy and the real problem of interest may be in the second-order variability, say, distinguishing volcanic from non-volcanic rocks in the siliciclastic sequence. In this case the classification task can be constrained by choice of depth interval. Alternatively, the need to change the class labelling given to the core samples to best fit the problem may only become apparent after an initial attempt at classification. Merging and splitting of classes may be required. There is a clear general need for more than one classification scheme to be tested and for the editing facilities to support that need. There is no guarantee that the recovered core, and hence any classification scheme based on it, is fully representative of the lithologies in the missing intervals. Examples of preferential recovery of, say, clays relative to sands are well-known. At the extreme a relatively common lithology may not be recovered at all. It is more likely that a lithology only ever exhibits low recovery and hence cannot be matched to specific depth intervals and used with confidence to train the classifier. This problem of a missing class(es) can be partly addressed using exploratory data analysis of the logs themselves. If it is clear that some populated area of log-space is not represented by the current classes then a search can be made to identify the missing class.

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sub-populations are used to train and test the performance of the classifier (e.g. discriminant analysis) and the classification rates of the two classifiers can now be compared.

Implementation of a neural network method

System design The quite complex processing chain implicit in the above discussion is best handled by a computer system designed for the job. We have designed such a system, the essential elements of which are shown in Fig. 1. The computing platform is a Sun Sparcstation and the graphical user interface is designed using PV-WAVE visualization software. There is a separate development environment for designing the neural networks that the user does not see, but which can create portable networks (as C code) that can be retrained. The user must define the problem by choosing appropriate depth intervals, lithological classes, logs and a neural network. The results of running the network are displayed graphically and in terms of relative performance of the classification rate. There are three main functional components to the system. These are shown in Fig. 2 and are described in detail in the following sections.

Performance measures Having trained a neural network classifier, some way of assessing its performance is required. The standard way to do this is to take a separate subpopulation of core-classified samples from the same general population and classify it independently with the network. The goodness of fit of the two classifications (classification rate) gives a measure of how well the network classifier performs relative to the visual description classification. If this performance is thought satisfactory then the network can be run on the full problem interval. What is 'satisfactory' in this context is best left to the geologist. One, albeit relative, benchmark by which to judge satisfactory performance is to compare with another classification technique. Again, the same

Fig. 1. Schematic structure of our computer system to derive lithological logs from ODP core-log data.

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Fig. 2. Functional schematic of the way data is explored, selected and classified in our method.

Log data exploration The purpose of log data exploration is to enable the user to become familiar with the data before making explicit choices. The ability to plot logs against depth and cross plots of one log against another is standard. In our system there is also the ability to display simultaneously, selected sample populations both in terms of their depth and their position in log space. This is done by manipulating a graphical cursor. It is a valuable facility for deciding whether some 'extreme' values in a cross plot, say, correlate with a specific bed or are scattered throughout a sequence. Principal components analysis and unsupervised cluster analysis are also available for any selection of samples and logs. These exploratory analytical functions help decide the following: (1) what is the effective dimensionality of the log data (i.e. how many distinct classes will the data support)?; (2) at what depth intervals do the most representative samples lie?; (3) are any samples obviously not represented by core intervals with good depth matching?

Data selection Explicit selections of depth interval, logs and classes must be made. As we show later, the

neural networks tend to perform better with as many 'useful' logs as possible and hence the default is to use all logs. However, some logs may only be available for restricted depth ranges. Hence the choice would be between fewer logs or more logs for a reduced interval. Class selection is a more complex issue. There are two main requirements: to be able to edit a classification, to create a new classification scheme and to give each such scheme source information; who created it, when and how. Editing can involve merging and splitting classes and assigning new labels. Thus a library of classifications can be created. Class labels are assigned at each log sampling interval, nominally every 15 cm. The shipboard petrologists also log sedimentary and structural discontinuities some of which form class boundaries. In one ideal situation, each thick (> > 15 cm) sedimentary bed would be of uniform lithological character with sharp boundaries and have contrasting neighbours giving the logs the character of step functions across the boundaries. This ideal is the basis of log segmentation algorithms (e.g. Vermeer & Alkemade 1992) which seek to segment the borehole into uniform intervals to which single (lithological) labels can be attached. This can be helpful in constraining classes in intervals of incomplete core recovery. The data selection process creates a Log-Class File (Table 1), whose values are used directly by the neural network classifier.

Neural network classification Samples from the Log-Class File are separated into class populations and counted. The total of the smallest population is then used to select samples for training and testing. Classes with larger p o p u l a t i o n s are subsampled evenly throughout their range to give a total equal to that of the smallest class. Each same-size class population is then split into two sub-populations by alternate sampling to give training and testing sample populations for each class. The log values of these populations are then examined graphically to check that; the distributions of the training and testing samples are similar, and that the distributions of the core-classified samples are representative of the whole interval under investigation. If these conditions are not met then other data selections must be made. The neural network used is the feed-forward back-propagation type which is standard for classification problems. The selection of logs and classes constrains the structure of the network. The input layer consists of one node for each log and the output layer consists of one node for

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Table 1. Representative part of a L O G - C L A S S file with 4 classes. Only 3 o f the logs are shown. There is no sample classified as Conglomerate 2 in this selection shown

SGR

CGR

A1203

26.6793 26.8207 26.6378 12.6669 12.1269 11.7988 12.1286 11.8547 11.7594

24.7285 25.1096 25.287 9.3789 8.8892 8.7609 8.1325 7.9513 7.9992

21.4907 20.9049 20.0691 23.0037 21.9625 21.4303 19.5016 18.8318 17.9526

-

each class. There is at least one other, 'hidden', layer of nodes between the input and output layers with weighted connections between nodes of different layers. The number of hidden nodes is not externally constrained and can be changed to suit a particular problem. The other network parameters are rnainly related to the weightings applied to the connections. These can be tuned to improve performance, but detailed optimization of neural networks is a complex issue. Our strategy is to make available a limited number of default networks initially and optimize individually applied networks later. Once the network has been selected it begins training with the prepared training data by selecting samples, at random, and presenting their log values to the input layer of the network. The effect of these values propagates through to the output layer where the 'error' between the network node values and the 'correct' values is then propagated back through the network, thereby changing network weightings. In this way the network, after hundreds to thousands of learning cycles, improves its ability to recognize classes until no further improvement is achieved and the network is said to be trained. The trained network can now be tested by presenting each of the test samples to the network once, and recording how close the network result is to the correct classes. The network gives proportional values for each class within the range 0-1, whereas the core-derived class labels are binary (0 or 1). The network results are thus essentially probabilistic. We express the test result as a thresholded classification rate. For example, at a user-defined threshold of 70% probability, a test result of 0.76 sandstone, 0.20 siltstone and 0.04 claystone for a sample with class values of 1,0,0 would count as a correct classification, though not at a threshold of 80%. 92 such correct results out of 100 test samples, for example, would give a classification rate of 92%. If the

Clay

Sst

Cong 1

Cong2

1 1 1 0 0 0 0 0 0

0 0 0 1 1 1 0 0 0

0 0 0 0 0 0 1 1 1

0 0 0 0 0 0 0 0 0

outcome of the training and testing cycle is considered satisfactory then the network can be applied to the full interval under consideration.

Application to Hole 792E Hole 792E of the ODP was drilled in the IzuBonin foreare sedimentary basin in 1989 (Taylor et al. 1990). The primary objective of drilling at Site 792 was to understand the stratigraphy of the forearc and the temporal variations in sedimentation and volcanism that controlled it. About 800m of sediment were drilled above volcanic (andesite) basement, including rocks of Pleistocene, !ate Pliocene, Miocene and early Oligocene age. The lower part of this section is a more volcaniclastic-rich sequence. The interval between 482-732 m below sea floor (mbsf) is the focus of this study and comprises a large part of Unit IV, a sedimentary succession of early Oligocene age (Fig. 3). Overall, Unit IV is composed of vitric sandstone (58%), sandy pebble-granule conglomerate ( 10%), silty claystone (9%), nannofossil-rich silty claystone (5%), claystone (4%), siltstone (4%), nannofossil c l a y s t o n e ( l % ) , clayey siltstone (1%), sandy mudstone (1%) and sandy siltstone (1%). This unit is interpreted by Taylor et al. (1990) as a rapidly deposited turbidite blanket in an oversupplied basin or distal fan. The pumice clasts of some of the coarse sandstones and conglomerates indicates contemporary volcanism but most of the andesite and dacite clasts are probably the result of erosion from the arc volcanoes. Core recovery

Hole 792E had a recovery of 48.2% of total possible core length. Recovery for the 482-732 mbsf interval was 79.1%. However, as discussed

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Fig. 3. Summary graphical log of the stratigraphy of the 482-732 mbsf interval of ODP Hole 792E (after Taylor et al. 1990). The symbols represent: Inverted 'T'= nannofossil ooze, dashes=claystone-siltstone, check = sandstone, dot-ellipse=conglomerate. Grain sizes are represented by c=claystone, s=siltstone, fs = fine sandstone, cs = coarse sandstone and g = gravel/conglomerate.

earlier, many individual cores contained only a fraction of the full core length and hence cannot be matched to the logs with confidence. We have used only individual cores that have greater than 90% recovery to form the basis of our classification. These 13 out of 26 cores (Fig. 3) therefore represent a useable recovery of 50%. Each of these cored intervals have had their depth assignments normalized to 100% recovery after closing any gaps between core sections. Notice that we have no matched core for the interval 617-655 mbsf. Five lithological classes based on shipboard visual description were used in the 482-732 mbsf interval: claystone, silty/sandy claystone, muddy siltstone/sandstone, siltstone/sandstone and gravel/conglomerate. One of these five classes was initially assigned to each (0.15m) log interval. A number of thin (< 0.15 m) , mainly claystone, beds were ignored in this process. However, the claystone and muddy siltstone/ sandstone classes had so few samples (8 and 37, respectively) that they were both merged with the silt/sandy claystone class. For simplicity the remaining three classes were called claystone, sandstone and conglomerate.

measurements

The sediments in Hole 792E are well-indurated. The borehole was close to cylindrical for much of its depth with a diameter < 3 0 c m , and conditions made for good-quality logs. There is reasonable correlation between the log values and shipboard core measurements except for SiO2 content. The tools (sensors) used that are relevant to this study were Resistivity (DEL), Sonic (LSS), Natural Gamma (NGT), Geochemistry (GST and ACT) and Lithodensity (HLDT) and they acquired data in 4 logging runs, that were then depth-matched. Seventeen log parameters were considered for use in the classification: spectral gamma, computed gamma, radioactive potassium, thorium and uranium, deep, medium and shallow resistivity, density, photoelectric effect, sonic velocity and the oxide contents of calcium, silicon, iron, titanium, potassium and aluminium. Unfortunately, no density correction and photoelectric effect measurements are available below about 550 mbsf. We removed low-quality log data that exceeded any of the following thresholds for the three quality-control logs: Caliper (29.5cm Density Correction (0.1 gm cc l) and Geochemical Factor (800). These thresholds were determined empirically by e x a m i n i n g the log distributions. This reduced the number of samples available by about 3%. Within the 482-732 mbsf interval Taylor et al. (1990) and Pratson et al. (1992) observed the following relationships between log and core: (1) Natural gamma spectrometry shows that potassium is the dominant radioactive source mineral and is inversely related to values of resistivity, velocity and density, and in some cases, grain size (482-500 and 555-585 mbsf). Uranium and potassium contents are generally negatively correlated. Beds with high mud contents have high values of potassium content and natural gamma. (2) Above about 515 mbsf the sequence has a bimodal character with high resistivity/ velocity/density - low natural gamma beds alternating with beds of opposite character. Below this depth the logs lose their high frequency nature and generally have raised resistivity/velocity/density values. (3) There is correlation between upward-fining sandstone/conglomerate beds and a sawtooth response of resistivity in the 540-590 mbsf interval. Of particular importance is the reduction in resistivity at 587 mbsf, where the base of a large conglomerate bed

ODP LITHOLOGY USING NEURAL NETWORKS Table 2.

Networkperformance results

Network Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

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Network Structure 1-717-3 15-15-3 15-15-3 15-15-4 4-8-4 5-15-4 6-15-4 11-10-4 15-02-4 15-04-4 15-06-4 15-08-4 15-10-4 15-12-4 15-14-4 15-16-4 15-18-4 15-20-4 15-25-3 15-30-3 15-25-10-3 15-25-20-3

Depth Interval (mbsf)

Classification Total

Clay

Sst

Cong (1)

482-550 482-550 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 482-732 483-732 482-732 482-732

88.4 85.5 75.8 90.0 78.1 80.0 81.9 86.3 71.9 82.5 83.1 86.1 86.9 86.9 88.1 86.9 89.4 88.1 78.0 76.6 80.2 78.8

87.0 87.0 83.5 92.0 85.0 82.5 87.5 85.0 87.5 90.0 85.0 87.5 90.0 90.0 85.0 92.5 92.5 90.0 86.8 79.1 84.6 82.4

78.3 73.9 59.3 80.0 47.5 52.5 67.5 65.0 92.5 47.5 60.0 85.5 62.5 72.5 72.5 70.0 72.0 77.5 57.1 69.2 72.5 65.9

100 95.7 84.6 87.5 82.5 85.0 72.5 95.0 12.5 92.5 87.5 75.0 95.0 85.0 95.0 85.0 95.0 85.0 90.1 81.3 83.5 87.9

also marks the d o w n h o l e increase in smectite concentration and magnetic susceptibility.

Performance of different neural networks There is no one single neural network that will work for all classification problems. The basic type used here, whose structural variants we now discuss, is a back-propagation network with a single hidden layer and a sigmoidal activation function at each node. As part of the preprocessing for neural network training the data from each log that are input to the network are normalized to the range 0-1. Logs with large potential ranges, such as the resistivity tools, may be best converted to a logarithmic scale first. However, the resistivity values of Hole 792E did not show a very great range and this was not done. As was discussed earlier, the number of nodes in the input and output layers is at least partly determined by the nature of the data and the problem to be solved; the number of nodes in the hidden layer is chosen to optimize performance once the input and output layers are fixed. In the performance results reported in Table 2 we use a convention in which a 15-10-4 network means 15 input nodes (downhole logs), 10 hidden nodes and 4 output nodes (lithological classes).

Rate(%) Cong(2)

100 97.5 100 100 100 95.5 100 100 100 100 100 100 100 97.5 100

There are 17 logs available for the interval 482-550 mbsf, but only 15 (no density and photoelectric logs) were considered for the full interval between 482-732 mbsf. Using the basic rock type classification, the 482-550 mbsf interval has 98 claystone, 164 sandstone and 46 conglomerate samples available for training and testing. Network 1 (Table 2) then, is a 17-17-3 network used for 482-550 mbsf that gives a total classification rate of 88.4%. Using only 15 logs for the same interval in network 2 gives a reduced rate of 85.5%. Hence density and photoelectric effect logs do have some extra capability to discriminate between these rock classes in addition to that present in the other 15 logs. However, when we use the same type of network for the 482-732 mbsf interval, but trained using the samples from a 183 claystone, 407 sandstone and 185 conglomerate pool of samples (network 3), the classification rate falls to 75.8%. This means that the claystones a n d s t o n e - c o n g l o m e r a t e classification that worked well from 482-550 mbsf is much less appropriate below 550 mbsf. There is a distinct fall in the capability of the network to recognize the conglomerate and sandstone class samples. Exploring the log data over this wider interval suggests that core samples classed as conglomerate may be usefully split into more than one type. For example, principal components analy-

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G. WADGE E T AL. 100

60 x

9

90

~

50

80

o

40 x

x

=

O

70

i

30

60

2

9

Classification Rate

x

Computing Time

20

!

n

!

i

I

l

4

6

8

10

12

14

16

Number of Logs Fig. 4. Plot of classification rate performance and elapsed computing time versus increasing numbers of geophysical logs as input to the neural networks (networks 4-8). B

90 ra

88

9

m

ra

86 84 82 o

80 78

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i

12

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i

9

14

1

16

9

i

18

9

i

20

Number of Hidden Nodes

Fig. 5. Plot of classification rate performance versus the number of nodes in the hidden layer (networks 9-18). sis of shallow resistivity, sonic velocity, spectral gamma, computed gamma and potassium oxide logs shows that some of the conglomerate class samples give much lower values of principal component 3 and higher values of principal component 4 than the other conglomerate samples. Thus we have created a second classification scheme with four classes: claystone, sandstone, conglomerate 1 and conglom-

erate 2. Conglomerate 1 includes those samples classed as conglomerate in networks 1 and 2; conglomerate 2 (the samples described above) corresponds to core which is conglomeratic but has higher proportions of large ( > 5 cm) claystone clasts than conglomerate 1. In the logs, conglomerate 1 has distinctly lower spectral gamma and potassium values than those of conglomerate 2. Sonic values are also lower in

ODP LITHOLOGY USING NEURAL NETWORKS

137

Fig. 6. Component lithological classification using neural network 4. The four columns display the component contribution of the rocks types (claystone, sandstone, conglomerate 1 and conglomerate 2) for each sampling interval of the logged hole. The depth in mbsf is shown on the left and the core number on the right of each column. The same colours are also used to display the classification in the visual core descriptions in the narrow column to the left of the core numbers. conglomerate 1 but the separation is less distinct. With four classes, network 4, otherwise equivalent to network 3, gives a much improved classification rate for the whole 482-732 mbsf interval of 90.0%. We saw an example of improvement in performance with increased numbers of logs (from network 1 to 2) for the 482-550 mbsf interval. For the full interval with 4 classes it is also generally true that the more logs input to the network the better the performance (networks 4, 5 to 8 inclusive; Fig. 4). There is a penalty to pay in terms of increased computing time (Fig. 4) but this is not too great a burden. Hence it makes sense to use as many useful logs as are available at the outset. Network 4 has a 15-15-4 structure. The

justification for the 15 nodes in the hidden layer is provided by a systematic test of performance in a series of networks with variable numbers of hidden nodes (networks 4, 9 to 18 inclusive; Fig. 5). The optimum configuration of the hidden layer of 15 nodes is given by the maximum performance value. In this case the number of hidden nodes equals the number of input nodes. This would be a useful rule-of-thumb for initial network configuration but it does not guarantee the optimum solution. For instance, as can be seen from networks 3 and 19 to 22 inclusive, greater numbers of hidden nodes (and even a second hidden layer) can give improved performance, but at the cost of increased computing time.

138

G. WADGE ETAL.

Computed lithological log results Figure 6 shows the output of network 4 when applied to the whole 250 m interval of downhole logs from 482-732 mbsf. The aggregate thicknesses of the lithologies according to this classification are: c l a y s t o n e = 4 4 m , sandstone = 130 m, conglomerate 1 = 42 m, conglomerate 2 = 34 m. Some sample intervals are classed as 100% of a particular lithology but many are classed as mixtures, with one dominant lithology and one or more minor components. This is particularly noticeable for mixtures of sandstone and conglomerate 1, but much less so for mixtures involving the other two lithologies. In Fig. 7 the results from this network have been recast such that the major ( > 5 0 % ) lithology in the output is attributed to that depth interval. This gives a columnar plot that mimics a traditional Lithological log and allows direct comparison with the visual core classification. The change in character of the rocks at about 515 mbsf noted above is apparent but is overshadowed by the decrease in claystone which occurs about 20 m higher. Lower down the hole the occurrence of conglomerate 2 is restricted to 3 zones where there is an apparent association of claystone-conglomerate 2. The base of the shallowest of these zones corresponds to the major break noted at 587 mbsf, though there is no obvious change in general lithological character below this. The log gives the impression of three major cycles of mixed conglomerate and sandstone sitting above conglomerate 2 and claystone (515-587, 587-660, 660-732 mbsf). The second thickest interval classed as conglornerate 2 is from around 640 mbsf, where there is no core recovery. In fact there is no sense of this second cycle in the recovered core. The lowest of the three cycles is richer in sandstone at the expense of conglornerate 1. As discussed earlier, the rocks recovered from only 50% of the full 482-732 mbsf interval were used to train and test the neural network. Figure 7 displays these core (core numbers 37-39, 40, 42-43, 46, 48-50, 56-57, 60, 61) together with the additional recovered core, that comprised 29% of the total interval, that was not used in training and testing the network because of poor depth control (core numbers 39, 41, 44-45, 47, 51-55, 58-59, 61). For many of the intervals used in the training and testing there is a high degree of detailed correspondence between the core and the network classifications (e.g. core numbers 37-38, 50, 62) as we would expect. For a few, the correspondence is weaker (e.g. core 43). For the core intervals with < 90% recovery any corre-

spondence is more difficult to assess. For some cores such as 39 and 55, correspondence could be achieved by appropriate expansion of the core lithology down section to match the network lithological log results. For other intervals no such a c c o m m o d a t i o n can be achieved (e.g. core numbers 45, 52 and 59). The two main possible explanations of this are that the network is 'overtrained' and has lost its ability to generalize when exposed to new data, and that the classification scheme is not optimal. The way ahead in our system would be to explore the second possibility by choosing another classification scheme. Because the sandstone group of samples is the largest and shows the poorest general classification performance figures (Table 2) this is the most likely group for possible splitting into two or more classes. Some of the geochemical logs such as TiO2 and CaO show clear evidence of alternating high and low valued sandstone horizons (e.g. 670--682 mbsf) that could form one of the criteria of such a new classification scheme, though this is not pursued here.

Discussion We have chosen to ignore a number of major issues of core-log-driven classification including graded bedding, the differences in spatial resolving power of the logs and the use of segmentation, in order to emphasize the value of quality control of the data and careful consideration of the optimal structure of the neural network. In particular, we wish to stress the need to have a flexible mechanism for changing the classification scheme of rock types based on the information content in the logs and the shipboard-derived classification scheme. Such an approach is inherently hole-specific. It lies at an intermediate position between a totally empirical approach, driven solely by the log data, and one that might use a universal library of log responses derived from fundamental core components (e.g. sand, carbonate, sea-water etc.). If the data can support it, the refinement of the classification scheme in a hole will be essentially hierarchical. However, there is no guarantee that the way that the log data can be optimally divided will correspond to the classification scheme that the geologist wants or expects. This sort of approach should be of value to ODP scientists both on and off the ship. Results of our work using data from other ODP holes will be presented elsewhere. We also envisage that this technique might be of value for providing rapid lithological analysis of

ODP L I T H O L O G Y U S I N G N E U R A L N E T W O R K S

139

Fig. 7. Majority component lithological log output of the neural network 4. The main column is the network classification, the narrow column to the right is that classified from recovered core.

G. WADGE ET AL.

140

piston cores from which horizontal-track logs have been collected o n - b o a r d ship.

This work is funded by a grant (GST/02/993) to RBW and GW under the NERC Special Topic--UK ODP Science Programme. ESSC work is supported by NERC grant F60/G6/12/02. We are very grateful to our collaborators Drs P. Harvey and H. Grubb, for their help and the Borehole Research Group at LDEO and ODP/TAMU for supplying data.

References AGRINIER, P. & AGRINIER, B. 1994. A propos de la connaissance de la profondeur a laquelle vos echantillons sont collectes dans les forages.

Comptes Rendus de la Academie Sciences de Paris, 318, serie II, 1615-1622. BALDWIN, J. L., BATEMAN,A. R. M. & WHEATLEY,C. L. 1990. Application of neural networks to the problem of mineral identification from well-logs. The Log Analyst, 3, 279-293. DOVETON, J. H. 1994. Geologic log analysis using computer methods. Computer Applications in Geology, 2. American Association of Petroleum Geologists, Tulsa.

GONCALVES, C. A. 1995. Characterisation of formation heterogeneity. PhD Thesis, University of Leicester. MAZULLO, L, MEYER, A. & KIDD, R. B. 1987. A new sediment classification scheme for the Ocean Drilling Program. ODP Technical Note, 8. ODP 1990. Wireline Logging Manual, Ocean Drilling Program. Borehole Research Group, LamontDoherty Geological Observatory. PRATSON, E. L., REYNOLDS, R., LOVELL, M. K., PEZARD, P. A. & BROGLIA,C. 1992. Geochemical well logs in the lzu-Bonin arc-trench system, Sites 791, 792, and 793. Proceedings of the Ocean Drilling Program, Scientific Results, 126, 653-676. ROGERS, S. J., FANG, J. H., KARR, C. L. & STANLEY,D. K. 1992. Determination of lithology from well logs using a neural network. American Association of Petroleum Geologists Bulletin, 76, 731-739. TAYLOR, B., FUROKA, A. & OTHERS 1990. Proceedings of the Ocean Drilling Program, Initial Results, 126. VERMEER, P. L. & ALKEMANDE, J. A. H. 1992. Multiscale segmentation of well logs. Mathematical Geology, 24, 27-43. WONG, P. M., JIAN, F. X. & TAGGART, I. J. 1995. A critical comparison of neural networks and discriminant analysis in lithofacies, porosity and its permeability predictions. Journal of Petroleum Geology, 18, 191-206.

Core-derived acoustic, porosity & permeability correlations for computation pseudo-logs A. C. B A S T O S , L. D. D I L L O N , G. F. V A S Q U E Z & J. A. S O A R E S Petrobras Research Center-SEGEST,

C i d a d e Universitaria - Q . 7 - P r e d i o 20, Ilha do

F u n d a o - R i o de Janeiro, 2 1 9 4 9 - 9 0 0 , B r a z i l

Abstract: In order to improve hydrocarbon production, it is often necessary to obtain more

accurate rock, fluid and petrophysical information. For example, to obtain a reservoir porosity map using seismic data as reference, it is necessary to generate reliable correlations between seismic attributes and petrophysical properties like porosity and permeability. Again, to optimize drilling and/or hydraulic fracturing programs, it is also necessary to estimate better formation static mechanical behaviour from geophysical data. The main goal of this work is to establish for an offshore Brazilian field, relationships between compressional and shear wave velocities and petrophysical properties such as porosity and permeability.The large number of limestone samples (120) gave us a precise empirical relationship between Vs and Vp for limestone. In order to obtain a calibration reference, we also made, with the same samples, simultaneous measurements of dynamic and static elastic constants. Using all these laboratory relationships, it was possible to generate unmeasured pseudo-logs of in situ parameters, which include: shear wave velocity, static and dynamic elastic constants and permeability. The good experimental relationships obtained between k-~b and Vp-~b in this work together with available logs give us an additional method to estimate permeability which is impossible to obtain from in situ measurements.

Indirect generation of unmeasured in situ logs like shear wave velocity (Vs), permeability (k) and elastic constants (Young (E), shear (G) and bulk (K) modulus) have been the subject of various works in geophysics (Wendt et al. 1986; Castagna et al. 1993; Bastos et al. 1995; Tang et al. 1996). In this paper we present, for three Brazilian offshore wells, a generation procedure for Vs, k and elastic constants logs calculated from laboratory data: Vp, Vs, porosity (~), (k) and static and dynamic elastic constants on cores. The importance of the generation of unmeasured in situ logs includes the possibility of obtaining more accurate information about lithology and fluid content in reservoir rocks and, in this way, contributing to generating more reliable AVO and seismic models, and also optimizing drilling and hydraulic fracturing programes. For reservoir development, these kind of data are also helpful for generating correlations between seismic attributes and petrophysical properties and for monitoring subsurface fluid flow. So, our main goal in this work was to: (1) obtain empirical correlations between Vs and Vp from laboratory data in order to generate unmeasured Vs logs from measured Vp logs; (2) generate logs of static and dynamic elastic constants using the simultaneous labora-

tory measurement of static and dynamic elastic constants as a calibration reference; (3) obtain empirical correlations, for each well, between Vp, k and q~, thereby yielding a calculated permeability log.

Methodology Ultrasonic P and S wave velocities were measured in about 120 samples of limestone from an offshore Brazilian field. These samples were retrieved from three vertical wells at depths of about 2350m to 2550m and vertically cut as right cylindrical plugs with diameter 2.5 cm and 3.75 cm and length 3.75 to 5cm. The measurement frequency was 500 kHz for both Vp and Vs and over a range of confining pressure of 1000 psi to 5000 psi at room temperature. The porosity and permeability range were 5% to 35% and 0.1 mD to 1800mD, respectively. The same measurements were made under dry and formation water saturated conditions. However, the results showed only small variations due to saturation, as noted by Bastos et al. (1995). Simultaneous measurements of static and dynamic elastic constants were made on some samples of diameter 5cm and length 12.5cm. These samples were placed in a triaxial cell and subjected to an in situ confining stress of about 5000 psi, and to a deviatoric stress which was increased up to the sample failure. The deforma-

BASTOS, A. C. DILLON, L. D. VASQUEZ,G. F. & SOARES,J. A. 1998. Core-derived acoustic, porosity & permeability correlations for computation pseudo-logs In." HARVEY,P. K. • LOVELL, M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 141-146

141

142

A . C . BASTOS E T A L . 4000

tion related to the increasing deviatoric stress allowed us to determine the static constants. The dynamic constants are obtained simultaneously, by monitoring changes in transit time.

I

Vm - 0 , 5 5 V p + 4 1 , 6 0 c c " 0,96

Procedure and results

Calculated logs o f Vs and Elastic constants

I

F 1000

j

~

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2OOO

,

4O0O

600o

Vp (m/s)

Fig. 1. Vs vs Vp for limestone plugs.

The three wells that are the subject of this work do not have in situ Vs logs. Therefore, a laboratory relationship was obtained between Vs and Vp in order to generate a pseudo Vs log. Figure 1 shows the linear fit to the Vs-Vp cross plot (equation 1). As shown in this figure, an excellent correlation was obtained with a correlation coefficient of 0.96. For the case of these samples, this linear fit was better than the WELL - B

WELL - A Velocity (m/s) 1000 2000 3000 4000 5000 6000 2380

WELL - c

Velocity (m/s)

Velocity (m/s)

1000 2000 3000 4000 5000 6000

1000 2000 3000 4000 5000 6000

2390 2400 2410 242O 2430 2440 A

E

.....

245O

Q 2460 247O 2480 249O 2500 2510 2520

-

-

Vp log

~

Vs log

9 Vp lab

9 V s lab

Fig. 2. Vp and Vs from laboratory data (symbols) and calculated Vp and Vs logs (curves). The three wells show good agreement between laboratory and log data.

CORE-DERIVED COMPUTATION OF PSEUDO-LOGS '

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Fig. 4. Calculated static and dynamic elastic constants logs for weU-C. polynomial fit proposed by Castagna et al. (1993) even for values of Vs close to 1500ms -1. The regression algorithm is: Vs = 0.55 Vp + 41.60

2). There is good agreement between pseudologs and laboratory data. With the Vp, Vs and density (p) logs and the following elastic theory equations:

(1)

Using this relationship and the in situ Vp logs it was possible to calculate Vs pseudo-logs (Fig.

v~ = d / K v

+ 4#/3

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(2)

144

A.C. BASTOS E T AL. WELL - A

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V, =

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(3)

where: K is the bulk modulus, # is the shear modulus and Pb is the Bulk density, pseudo-logs of dynamic elastic constants have been calculated, In fact, to optimize drilling and/or hydraulic fracturing programs, it is often necessary to

obtain logs of the static and not the dynamic elastic constants. For this purpose, we use simultaneous laboratory measurements of static and dynamic constants in order to transform the dynamic to the reference static. Figure 3 shows a cross plot between static and dynamic constants for sedimentary rocks and illustrates strong empirical relationships which are expressed mathematically as follows:

CORE-DERIVED COMPUTATION OF PSEUDO-LOGS WELL- B

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Estat = 0.675 Edyn -- 3.84; correlation coefficient --- 0.95

(4)

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(5)

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(6)

where the subscripts 'stat' and 'dyn' denote static and dynamic moduli, respectively. Figure 4 shows the calculated log of static and dynamic constants obtained from equations (4) to (6) for well C. As expected, the logs of the dynamic elastic constants show higher values than their static equivalents.

Calculated logs of permeability The next step was to calculate permeability logs for the three wells using algorithms based on

laboratory permeability, porosity and velocity data. Figure 5 shows core data, the cross plots of velocity against porosity, and permeability against porosity. From these plots it has been possible to deduce a relationship between Vp, ~b and k. As can be seen in Fig. 5, an exponential fit was the best one obtained for both the Vp-q$ and the k-~b relationships for the three wells. Thus, with the equations obtained (equations (7), (8), (10), (11), (13) and (14)) we can isolate qb from Vp-q) and k-q5 relations and then obtain k-Vp relationships (equations (9), (12) and (15)) which can be used to calculate the k-log shown in Fig. 6. In order to check these relationships we include some points in well A (cross points in Fig. 6) which were not used to obtain equations (7) to (15). Again, it can be seen that there is a good correspondence between these points and the obtained log: Well A Vp = 5868e~~

cc = 0.88

(7)

146

A.C. BASTOS E T AL. k = 0.05e~ k :

cc = 0.90

(9)

Vp 73.e6~

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(8)

cc -- 0.77

k = g p -6"54. e 53"35

(10) (11) (12)

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cc = 0.89

(13)

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cc = 0.93

(14)

k : Vp-184.e152.5

(15)

Figure 6 shows a good correspondence for wells A and C, but not for well B. In this well the good correlation coefficient for the k-qb relationship, 0.77, was lower.

Conclusions (1) The large number of limestone samples gave us a precise empirical relationship between Vs and Vp for limestone, and this differs from the earlier work of Castagna et al. (1993), even for Vs close to 1500 ms -1. (2) Good relationships between static and dynamic elastic constants were obtained for sedimentary rocks, and these have allowed us to generate logs for these constants. As expected, dynamic constants are greater than static ones. (3) The capacity to obtain a relationship between static behaviour of rocks from dynamic properties combines the advantages of both methods in one. Thus, the resultant properties

of this relationship give us the static mechanical behaviour, characteristic of production engineering, but, with the continuous character of geophysical logs. (4) Cross plots between Vp-qb and k-~b indicated good exponential fits for the three wells that formed the subject of this work; (5) The good experimental relationships obtained between k-qb and Vp-qb (see correlation coefficients in equations (7) to (15)), together with available logs give us an additional method to estimate permeability. (6) There is good agreement between laboratory permeability measurements and synthetic permeability logs from velocity data, even for points that were not used in the generation of these pseudo-logs.

References BASTOS, A. C., DILLON, L. D., SOARES, J. A. & VASQUEZ, G. F.. 1995. Estimativa dos perils de

constantes elfisticas em carbonatos pouco permefiveis a partir de dados laboratoriais. 4th International Congress of the Brazilian Geophysical Society and the 1st Latin American Geophysical Conference. Volume II. CASTAGNA, J. P., BATZLE, M. L. 8r KAN, T. K. 1993.

Rock Physics: The link between rock properties and AVO response. In: CASTAGNA, J. P. t~ BACKUS,M. M. (Eds) Offset-dependent reflectivity: SEG, 124-157. TANG, X. • CHENG, C. H. 1996. Fast inversion of

formation permeability from Stoneley wave logs using a simplified Biot-Rosenbaum model. Geophysics, 61, 639-645. WENDT, W. A., SAKURAI, S. t~ NELSON, P. H. 1986.

Permeability prediction from well logs multiple regression. In: LAKE, L. W. & CARROLL, H. B. Jr

(eds) Reservoir characterization. Academic Press, San Diego, California, 181-221.

Effects of water salinity, saturation and clay content on the complex resistivity of sandstone samples P. S. D E N I C O L 1 & X. D. J I N G

Centre for Petroleum Studies, Imperial College of Science, Technology and Medicine, London S W 7 2BP, UK 1Present address." Petrobras S.A., Exploration Department, 27913-350, Macae, R J, Brazil

Abstract: Complex resistivity measurements were made on sandstone samples in the frequency range from l0 Hz to 2 MHz. The main objective was to investigate the frequency response of complex resistivity and phase angle as a function of salinity, water saturation and clay content. The results showed the classical frequency dependence behaviour where the complex resistivity decreases with increasing frequency. The complex impedance behaviour in the intermediate frequency range (10-100 kHz) was used to relate the effect of frequency dispersion with interface polarization and, hence, pore geometry, specific surface area and permeability. Both water saturation and salinity were found to influence the gradient and the relaxation frequency of the complex resistivity versus frequency relationship. A variation in water saturation from full to partial saturation resulted in a dramatic increase in the gradient and a clear shift of the relaxation frequency. Both the saturation and salinity dependence can be attributed to the polarization of both the rock-fluid and fluid-fluid interfaces within the pore space, which depend on the geometry and physical characteristics of the interfacial layers. The results presented in this paper can have important applications in identifying low resistivity and low contrast pay zones. The complex electrical behaviour of a rock results from its conductive and dielectric response in the presence of an electric field; the former is related to the transport of free charge carriers and the latter is due to geometrical, interfacial and electrochemical mechanisms (Sen 1980, 1981). A complex impedance vector (Z*) consists of a real part ( in-phase or resistance, R) and an imaginary part (out-of-phase or reactance, X). Using the rectangular-coordinate form, the complex impedance can be expressed as follows, Z*= R + jX

(1)

where j = v / - 1 is the complex operator. The phase angle (0) by which current and voltage are shifted is given as: 0 = tan I(X/R)

(2)

The complex resistivity p* can be calculated from Z*, p* = Z* A/L = p ' + j p "

(3)

where A is the cross-sectional area of the sample and L is its length, and p' and p" are real and imaginary parts of the complex resistivity, respectively. In some cases, using the reciprocal

of impedance is mathematically expedient. For example, when the real and imaginary components are paralleled, it is better to use admittance (Y*), Y* = 6+ jB

(4)

where G is the conductance and B is the susceptance. The complex conductivity or* can be calculated from Y*, or* = Y* L/A = or'+jcr"

(5)

where a' and or" are the real and imaginary conductivities, respectively.

Background Complex electrical impedance measurement is a non-invasive technique where an electrical current flows through the sample at different frequencies. Experimental measurements of the electrical properties of rocks, when submitted to an alternating electrical field at different frequencies, have shown that both the resistive and reactive components of the complex impedance vary over the frequency spectrum. These two features (complex quantity and dispersion or

DENICOL,P. S. & JING, X. D. 1998. Effects of water salinity, saturation and clay content on the complex resistivity of sandstone samples In: HARVEY,P. K. • LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 147-157

147

148

P.S. DENICOL & X. D. JING

Table 1. List of petrophysieal parameters and chargeability at full and partial water saturation. Sample

Density grams cm-3

Porosity %

Kair mD

Saturation Sw(%)

Chargeability Partial Sat.

Chargeability Full Sat.

Z1 Z3 Z4 Z5 Z7 Z8 Z9

2.66 2.66 2.64 2.65 2.71 2.69 2.64

22.4 14.1 12.2 20.1 25.8 21.5 29.1

1760 38.7 3.51 163 23.1 101 819

33 39 61 47 77 82 44

0.74 0.81 0.68 0.55 0.54 0.52 0.53

0.53 0.53 0.54 0.52 0.53 0.54 0.50

Table 2. List of synthetic shaley samples Sample

Clay Type

Clay Length Content cm

Area cm2

Grain Porosity Density % grams cm-3

Kair mD

SZ1 SZ2 SZ3 SZ4

clean montmorillonite montmorillonite montmorillonite

0 5 10 15

10.75 10.75 10.75 10.46

2.65 2.66 2.66 2.66

337 235 146 105

6.41 6.49 6.53 6.37

frequency dependence) can be used to estimate rock petrophysical properties, such as specific surface area and permeability. The origin of the frequency dependence can be related to geometrical effects of the clay particles (Sen 1980) or electrochemical phenomena at the fluid-grain (Rink & Schopper 1974) and/or fluid-fluid interface (Knight & Endres 1991). The interface region between matrix and the fluid-filled pore space is complicated due to the existence of the ionic double layer. The concept of the electrical double layer forms the theoretical basis for understanding the electrical properties of rocks, especially shaley sandstones. Electrochemical theory suggests that the surface of clay minerals carries excess negative charges as a result of the substitution of certain positive ions by others of lower valence. When the clays are brought in contact with an electrolyte, these negative charges on the clay surface attract positive ions and repulse negative ions present in the solution. As a result, an electrical ionic double layer (or diffuse layer) is generated on the exterior surface of particles. Typical distribution for ionic concentration and electric potential can be predicted by the Guoy (1910) theory. The Gouy theory also predicts that the double-layer thickness (Xd) is reduced as the concentration of the bulk solution increases. The region outside the electric double layer (distance > Xd) is called the free-water region. Ionic double layers exist between rock and fluid interfaces. The perturbation of the double

29.1 28.1 27.4 27.9

layer by an oscillating electrical field is usually accepted (Lima & Sharma 1992) as the main mechanism for the frequency dependence of rocks. Therefore, this interface polarization may provide a link between complex resistivity data and pore-scale attributes, such as pore geometry and specific surface area, which in turn can be related to rock permeability through a KozenyCarman type of relationship ( Borner 1995; Denicol & Jing 1996). Since the frequency dependence is reflecting interface phenomena, salinity of the pore water also influences the dispersion due to the variation in the double layer thickness and ion mobility. Furthermore, fluid saturation also plays a role due to addition of the water/oil interfacial area, an increase in the tortuosity of the brine-phase distribution and the presence of a non-ionic fluid. The main objective of this paper is to investigate, experimentally, the effects of brine salinity, fluid saturation and clay minerals on the complex impedance of different rock samples with varying porosity and permeability. The samples include outcrop cores, oil-field reservoir rocks and synthetic shaley rocks (Tables 1 and 2). A brief geological description of all sandstone samples is given in the Appendix.

Experimental apparatus and procedures Complex impedance measurements were performed using a multi-sample rock testing system. The apparatus can accommodate five

Fig. 1. Schematic representation of the experimental apparatus. samples simultaneously under varying hydrostatic confining pressure, temperature and independently controllable pore pressure. Since all the samples are under the same conditions of pressure and temperature, it eliminates experimental comparison errors due to fluctuations during the period of testing (Jing et al. 1992). The experimental system is shown schematically in Fig.1. Complex impedance measurements were made using the frequency response analyser (QuadTech Model 7600 RCL) in the frequency range of 10 Hz to 2 MHz. The instrument is capable of compensating for the residuals of test fixture and cables based on the open/short circuit compensation technique in the whole frequency range. The instrument is equipped with four coaxial BNC terminals on its front panel which locate its calibration plane. The calibration plane is the position where the instrument measures within its specified accuracy (0.05%). In our experiment, test fixture and cables were used to interconnect the sample to the instrument in a four-terminal configuration (4T). The parasitics related to test fixture, cables and connections are frequency dependent and they were minimized using the 4T configuration and by applying the open/short compensation technique in the whole frequency range similar to the technique used by Taherian et al. (1990). The effect of salinity was investigated for two brine concentrations: 20 g and 50 g of sodium chloride (NaCI) per litre of solution (i.e. 2% and 5% NaC1). The solution is made up of NaC1 dissolved in de-aerated and de-ionized distilled water. Initially, the rock samples were fully saturated with 2% brine solution and loaded in the test cell. The samples were considered fully saturated when the resistance of the samples

measured continuously at 2KHz frequency, showed no significant variation (i.e. < 1% change over a period of 12 h) with brine displacement. The RCL meter was then connected and a frequency sweep performed on each sample. After the frequency measurement, 5% brine solution was injected through the samples to displace the original brine. The resistance was observed continuously. A sharp decrease was observed during the first few pore volumes of displacement, when the more conductive brine became continuous. Then, the decrease was less accentuated and reached equilibrium after about 20 pore volumes of injection. A frequency sweep was then repeated at 5% brine salinity. In order to study the frequency dependence of partially saturated rocks, the desaturation technique using semi-permeable capillary diap h r a g m s has been used f o l l o w i n g the laboratory procedures described by Elashahab et al. (1995). The main advantages of the method are the reduction of capillary end effects and uniform saturation distribution along the core length. These improvements are achieved by using highly hydrophilic ceramic membranes positioned between the sample and the end plate. The resistivity distribution along the core is monitored by six potential electrodes equally spaced along the rock sample so that resistivity measurement can be taken at pairs of electrodes (four-electrode configuration) and also between the top and base current electrodes which give the total resistivity (Fig. 2). The resistivity measurements for saturation monitoring based on the Archie type of equations are taken at a frequency of 2 kHz. The volume of brine produced during the desaturation process was

150

P. S. DENICOL & X. D. JING

Fig. 2. Core sleeve with multiple electrodes.

Fig. 3. General frequency dependence behaviour for sample Zl. carefully measured to allow the calculation of average sample saturation by material balance. The effect of clay minerals on the complex resistivity was investigated using synthetic shaley samples following the method established by Jing et al. (1992). According to this technique, mixtures of sands with different ranges of grain sizes and different clay types and contents can be prepared and consolidated through cycles of loading/unloading and heating/cooling in a high pressure and high temperature cell. The main advantage of the technique is full control of the sample preparation so that the desired variation of clay type, content and distribution can be systematically obtained under laboratory conditions. Five synthetic samples of different clay contents were prepared , namely SZ1 (clay free), SZ2 (5% montmorillonite), SZ3 (10% mon-

tmorillonite) and SZ4 (15% montmorillonite). The sand and clay mixtures were mixed uniformly to achieve homogeneous samples. Table 2 lists the petrophysical characteristics of the synthetic samples. After loading the samples in the high pressure cell, they were saturated with 5% by weight of NaCI brine and the consolidation process was started. Repeated loading and unloading cycles were performed with confining pressures varying from 500 psi to 4000 psi until sample consolidation.

Results and discussion

Frequency effect Figures 3 and 4 show the real component and phase angle versus frequency for two reservoir core samples. This plot of resistivity and phase

THE COMPLEX RESISTIVITY OF SANDSTONE SAMPLES

151

Fig. 4. General frequency dependence behaviour for sample Z3.

Fig. 5. Argand diagram with the critical frequency (fc) separating electrode polarization and bulk sample response for sample Z1.

Fig. 6. Argand diagram with the critical frequency (fc) separating electrode polarization and bulk sample response for sample Z1. angle against frequency can be divided into polarization and sample response regions. The electrode polarization region (e.g. < 10 KHz) is strongly influenced by polarization at the rockelectrode interface and can be identified from the

bulk rock response by plotting the real and imaginary components of the impedance on the complex plane as shown in Figs 5 and 6 (i.e, Argand diagram, Debye 1929). The sample response region (i.e. the Cole-Cole region, Cole & Cole 1941) can be divided into two straightline regions of distinctive frequency dependence: the intermediate frequency range (10-100 kHz) characterized by a small and gradual change in impedance and phase angle followed by the high frequency range (100-2 MHz) characterized by a sharp change in impedance and phase angle. The transition between the intermediate and highfrequency region is characterized by the relaxation frequency of the interface polarization process. Figures 5 and 6 plot the Argand diagrams for samples Z1 and Z3 showing the separation of sample response from electrode effects. The experimental data can be fitted by the classic Cole & Cole (1941) model of a depressed semicircle on the Argand plot. According to Lockner & Byerlee (1985), existing theoretical models are most useful in the analysis of data near the peak loss frequency but they may not be capable of fitting experimental data over the entire frequency range.

Salinity dependence The general frequency behaviour of the complex impedance is shown in Fig. 7 for the reservoir sample Z7 at two different brine concentrations. The effect of increasing the pore electrolyte salinity on the frequency behaviour of the sample can be summarized as follows:

152

P.S. DENICOL & X. D. JING

Fig. 7. General frequency behaviour for sample Z7 at two brine concentrations.

Fig. 8. Normalized impedance at two brine concentrations showing salinity dependence for sample Z7.

(1) The complex impedance decreases as the brine salinity increases in the whole frequency range; (2) The complex impedance decreases with frequency for both brine concentrations; (3) The rate of decrease is more pronounced for the lower brine concentration, that is, the lower the salinity of the brine the higher the frequency dependence. This behaviour is best illustrated when the normalized impedance is plotted against frequency in the range from 10 to 100 kHz (Fig.8); (4) As the salinity of the brine increases, the relaxation frequency increases. The frequency dependence as a function of salinity variations is related to the electrical double layer, the thickness of which varies with the brine concentration. High solution concentrations are associated with the compression of the double layer whilst low concentrations

favour the expansion of the double layer. The frequency dependence, as expressed by the slope taken from the semi-log plot of the normalized impedance in the frequency range from 10 to 100 kHz, is found to increase from 5% to 2 % NaC1. Similar results were reported by Kulenkampff et al (1993) and Kulenkampff & Schopper (1988). This salinity dependence is also related to the relative mobility of the ions in the pore space from the free water to the double layer near the solid surface. In the free water region, the charge carriers are free to move and therefore follow the alternating electrical field. On the other hand, in the double layer region, the movement of ions is partially restricted by the electrostatic potential. The result is a delayed oscillation of the diffuse layer when compared to the free ions of the bulk solution that react promptly to the alternating electrical field. Consequently, a phase lag is established between the input voltage and the corresponding current flowing through the pore

THE COMPLEX RESISTIVITY OF SANDSTONE SAMPLES

Fig. 9. Frequency dependence of resistivity and phase angle at partial saturation for sample Zl.

Fig. 10. Frequency dependence of resistivity and phase angle at full brine saturation for sample ZI.

Fig. 11. Saturation dependence of the resistivity for sample Z1 as characterized by the chargeability (m)

153

154

P.S. DENICOL & X. D. JING

space. If the concentration of NaCl decreases, the double layer thickness increases and the phase lag is more accentuated. Additionally, an increase of the diffuse layer thickness favours the blockage of ions, especially at narrowing pores, with consequent accumulation of charges and local concentration gradients.

080 E_075 ~"070 ~ 065 ="0.60

Z3 Zl

The saturation dependence was studied by comparing the frequency spectrum of the real part of the resistivity at full and partial water saturation. The partial saturation was arrived at by displacing brine with Isopar H which has a dielectric constant of 2.02 at 25 ~ So far, only water-wet samples have been tested. The frequency dependence of the in-phase resistivity and phase angle are shown in Figs 9 and 10 for sample Z1 at 33% and full brine saturation, respectively. The saturation dependence becomes clearer when both resistivity curves are displayed in a log-log plot (Fig. 11). The fully saturated curve is almost flat for the whole frequency range. On the other hand, the partially saturated curve is flat in the low frequency range and shows clear frequency dependency above the relaxation frequency. The frequency effect can be better analysed by the empirical parameter chargeability (m) defined as follows (Siegel 1959): (6)

where R1 and R2 stand for the low and high resistivity asymptotes, respectively. Table 1 summarizes the results obtained for the chargeability of the samples. For a given sample, there is a consistent increase in m when brine is displaced by oil. The correlation between m and the water saturation is shown in Fig. 12 for all the samples. Although a general trend of higher m for lower saturation can be observed, the correlation is weak. Samples Z3 and Z9 showed a more remarkable deviation from the trend, possibly due to the high iron oxide content in the former and dispersed glauconite in the latter. Knight & Nur (1987) also observed that a sample with high iron oxide content (Indiana Dark sandstone) had an anomalous dielectric exponent apparently due to the effect of the magnetic susceptibility on the dielectric response. The interpretation of the saturation dependence upon frequency is difficult due to the intricate geometry of the pore space and its effect on distribution of fluids within the rock. Mineralogical complexity, mainly related to clays and

9

Z4

0.55

0.50 0.3

Saturation dependence

m = R1/(R1 +R2)

0.85

0.4

0.5 0.6 Sw (fraction)

0.7

0,8

0,9

Fig. 12. Correlation between chargeability (m) and water saturation.

metallics, also plays an important role in increasing the complexity of the frequency dispersion. However, the general behaviour of the saturation dependence is characterized by an increase of the frequency effect in response to the oil saturation, as distinguished by the phase angle and chargeability results. It is important to note that in two-phase systems, the frequency dispersion due to the polarization at the solid-liquid interface (poregrain) may be added to by polarization at the liquid-liquid interface (oil-water). As the water saturation decreases, there is an increase in the water-oil interfacial area and an increase in the complexity of the brine phase topology. For any rock-fluid systems, wettability plays a significant role in controlling fluid distribution at the pore scale. Therefore, it might be possible to derive wettability information based on the frequency dispersion measurements of reservoir rock-fluid systems. However, further research is needed in this area.

Clay effects Synthetic shaley samples with controlled clay type, content and distribution were used to investigate the effects of clay minerals on complex impedance measurements. Figure 13 shows the results for the synthetic sample SZ4. The low-frequency region from 10 Hz to ~10 kHz indicates strong dispersion in both impedance and phase angle which is attributed to electrode polarization. In the intermediate frequency range (1 to ~100 kHz) the impedance decreases monotonically while the phase angle reaches a minimum and then starts increasing. The high frequency region is characterized by the relaxation frequency at N800 kHz where the phase angle reaches a maximum and the impedance decreases more drastically. All the synthetic samples present the relaxation frequency at around the same position. However, the value of the phase angle at the relaxation

THE COMPLEX RESISTIVITY OF SANDSTONE SAMPLES

155

Fig. 13. General frequency dependence behaviour of impedance and phase angle for sample SZ4.

Fig. 14. Normalized impedance versus frequency relationships of four synthetic samples containing various amounts of montmorillonite.

Fig. 15. Correlation between clay content and frequency dependence for the synthetic samples.

156

P.S. DENICOL & X. D. JING

frequency increases with the amount of clay, varying from 1 degree for the clay-free sample to 5 degrees for the 15% montmorillonite sample. This observation suggests that a polarizationlike process is being caused by the clay presence although the classical induced polarization effect would be expected at lower frequencies. A possible explanation for this frequency effect may be related to the electro-osmotic coupling due to the accumulation of charges at narrowing pores (Marshal & Madden 1959; Dankhazi 1993). Although its effect is found to be very weak, this type of polarization is expected to increase with a reduction of the sample permeability. Indeed, the synthetic samples show a decrease in permeability with increasing amount of clay (Table 2) that leads to the narrowing and reduction of effective pores and hence the electro-osmotic coupling. The slope of the impedance curve in the range from 10 to 100 kHz is also found to correlate with the clay content of the samples. Figure 14 shows the normalized impedance versus frequency for the samples containing montmorillonite. The graph indicates a consistent increase in the frequency slope from sample Z1 (clayfree) to sample Z4 (15% montmorillonite). A plot of the rate of impedance decrease with frequency versus clay content is shown in Fig. 15, where the clay effect appears to decrease at higher clay contents.

Conclusions The frequency effect in the intermediate frequency range (10-100 kHz) increases when the solution concentration is decreased from 5% to 2% NaC1. This salinity dependence may be explained by variations of the double layer thickness and ion mobility. At high salinity, the double layer is compressed to the pore surface and gradually expands with decreasing brine concentration. As a consequence, the mobility of the ions in the diffuse layer is reduced at high salinity preventing them from following the alternating field as opposed to the free ions in the centre of the pore. Additionally, the expansion of the double layer supports the blockage of ions particularly at the smaller pores with subsequent electro-osmotic polarization due to the accumulation of charges. The frequency effect is found to increase for the whole frequency range when brine is displaced by oil (Isopar H). A variation in water saturation from full to partial saturation resulted in a dramatic increase in the frequency dispersion and a clear shift of the relaxation frequency. This observation may have potential

applications for the evaluation of low resistivity and low contrast pay formations. The frequency dispersion consistently increases with the amount of clay in the sample. This effect is better illustrated when the normalized impedance is plotted in the frequency range from 10 to 100 kHz . The impedance slope is relatively flat for the clay-free sample (SZ1) and increases with the content of montmorillonite for the shaley samples. A plot of the clay content versus the frequency dependency clearly shows a relationship. We would like to thank Petrobras S.A. for sponsoring P.S. Denicol and for providing reservoir rock samples. We also wish to thank M. S. King for many valuable discussions.

References BORNER, F. D. 1995. Estimation of hydraulic conductivity from complex electrical measurement. International Symposium of the Society of Core Analysts, paper 9523. COLE, K. S. & COLE, R. H. 1941. Dispersion and absorption in dielectrics. Journal of Chemistry and Physics, 9, 341. DANKHAZI, G. 1993. A new principle approach to induced polarization in porous rock. The Log Analyst, 34, 54-66. DEBVE, P. 1929. Polar molecules. Chemical Catalogue Co. DENICOL, P. S. & JING, X. D. 1996. Estimating permeability of reservoir rocks from complex resistivity data. Society of Professional Well Log Analysts, 37th Annual Logging Symposium, paper X. ELASHAHAB,B. M., JING, X. D. & ARCHER,J. S. 1995. Resistivity index and capillary pressure hysteresis for rock samples of different wettability characteristics. SPE paper No. 29888, the 9th Middle East Oil Show and Conference, March, Bahrain. Gouv, G. as discussed in HUNTER, R. J. 1988. Zeta Potential in Colloid Science, Academic Press. JING, X. D., ARCHER, J. S. 8r DALTABAN,T. S. 1992. Laboratory study of the electrical and hydraulic properties of rocks under simulated reservoir conditions. Marine and Petroleum Geology, 9, 115-127. KNIGHT, R. & NUR, A. 1987. Geometrical effects in the dielectrical response of partially saturated sandstones. The Log Analyst, 28, 513-519. KNIGHT, R. & ENDRES,A. 1991. Surface conduction at the hydrocarbon/water interface. Society of Professional Well Log Analysts, 32nd Annual Logging Symposium, paper I. KULENKAMPFF,J. M. & SCHOPPER,J. R. 1988. Low frequency conductivity--a means for separating volume and interlayer conductivity. Society of Professional Well Log Analysts, 12th European Formation Evaluation Symposium, paper P.

THE COMPLEX RESISTIVITY OF SANDSTONE SAMPLES --,

BORNER, F. D. & SCHOPPER,J. R. 1993. Broad band complex conductivity lab measurement enhancing the evaluation of reservoir properties. Society of Professional Well Log Analysts, 15th European Formation Evaluation Symposium, paper A.

LIMA, 0. A. L. t~ SHARMA, M. M. 1992. A grain

conductivity approach to shaly sandstone. Geo-

physics, 55, 1-10. LOCKNER, D. A. & BYERLEE, J. D. 1985. Complex

logically mature.

mature,

texturally

157 submature

to

Sample Z3 (Block 18-2)." Lower Permian "Penrith Red Sandstone", predominantly quartz grains cemented by quartz over-growths with iron oxide petina, subrounded-rounded, mineralogically and texturally sub-mature.

resistivity measurements of confined rock. Journal

of Geophysical Research, 90, 7837-7847. MARSHALL, D. J. t~ MADDEN, T. R. 1959. Induced

polarization, a study of its causes. Geophysics, 24, 790-816. RINK, U. • SCHOPPER,J. R. 1974. Interface conductivity and its implications to electrical logging.

Society of Professional Well Log Analysts, 15th Annual Logging Symposium, paper J. SEN, P. N. 1980. The dielectric constant and conductivity response of sedimentary rocks. Society of Petroleum Engineers, paper 9379. SEN, P. N. 1981. Relation of certain geometrical features to the dielectric anomaly of rocks. Geophysics, 46, 1714. SEIGEL,H. 0. 1959. A theory for induced polarization effects (for step excitation function). In: WArr, J. R. (ed.) Over Voltage Research and Geophysical Applications. Pergamon Press Inc., 4-21. TAHERIAN, M. R., KENYON, W. E. & SAFINYA, K. A. 1990. Measuremen of dielectric response of watersaturated rocks. Geophysics, 55, 1530-1541.

Appendix: geological description of sandstone rocks (a) Outcrop rocks Sample Z1 (Block 15-8): Lower Carboniferous sandstone, average grain size 0.2 ram, 95% q u a r t z , alkali feldspar, clay, biotite, alcite cement (5%) with some chert, angular to sub-angular, poor sphericity, minera-

Sample Z4 (Block 16-2): Upper Carboniferous sandstone, grain size : 0.10.3 mm, 85% quartz, 0% alkali feldspar, 5% mica, very irregular, poor sphericity, texturally immature and mineralogically submature.

Sample Z5 (Block 19-4): Lower Triassic 'Bunter' sandstone, fine to medium grain sizes (< 0.5 mm), 95% quartz, % alkali feldspar and calcite, sub-rounded, poor sphericity, texturally and mineralogically mature.

(b) Reservoir rocks Sample Z7: Glauconitic sandstone, semi-friable, grains are sub-rounded with regular to good selection. Mineralogy also includes quartz, feldspar and mica.

Sample Z8." Sandstone with pseudo-argilaceous matrix (27 %), quartz (31%), K-feldspar (18%), glauconite (6%), plagioclase (5%), others (2%). Cements include dolomite and pyrite.

Sample Z9." Sandstone with pseudo-argilaceous matrix, quartz, K-feldspar, glauconite, and plagioclase.

Acoustic wave anisotropy in sandstones with systems of aligned cracks A. S H A K E E L 1 & M . S. K I N G 2

1Production Department, Oil and Gas Development Corporation, F-8 Markaz Islamabad, Pakistan 2 Department of Earth Resources Engineering, Royal School of Mines, Imperial College, London SW7 2BP, UK

Abstract: Seismic anisotropy has been studied on a number of dry cubic sandstone

specimens, of 51 mm side, in which a system of aligned cracks has been first introduced progressively by the application of a polyaxial state of stress, and then closed by hydrostatic stress. One P- and two S-wave velocities polarized at right angles, along with the deformation, have been measured at each stress level in each of the three principal stress directions. Thomsen's (1986) anisotropy parameters (e, 7, 6) have been calculated at each stress level during the cracking and crack closing cycles using Nishizawa's (1982) theory. Test results indicate that anisotropy in the P-wave velocity is greater and more sensitive to the presence of aligned cracks than that observed for S waves. Modelling studies show that the P-wave anisotropy parameter e is always greater than that of anisotropy parameter 8, for low crack densities and for small aspect ratios. The reverse is true for high crack densities and low aspect ratios. The results of numerical studies indicate that S-wave anisotropy is independent of the nature of the saturating fluid and that it is possible to observe elliptical anisotropy in a medium containing aligned dry ellipsoidal inclusions. It is well known that the presence of microcracks and fractures reduces the acoustic velocities of P- and S-waves in rocks. When the principal stresses are altered on a rock that initially has a random distribution of cracks, the crack distribution no longer remains randomly oriented. The effect of an applied non-hydrostatic stress is to close cracks in some directions and leave cracks open in others (Sayers 1988). Those cracks with their normals lying close to parallel to the new major principal stress will tend to be closed more than those with their normals subparallel to the new minor principal stress (Sayers 1988). The elastic and transport properties of the rock then become anisotropic in their behaviour, with the degree of anisotropy depending on the magnitude of the principal stress differences, the type of fluid filling the cracks (Xu & King 1989, 1992; King et al. 1995a,b). Seismic anisotropy was studied more than 40 years ago by Postma (1955) and Uhrig & Melle (1955), but for a long time its effect was ignored or considered insignificant, due to the fact that most of the seismic surveys carried out were for P-wave reflection and conducted at small angles to the vertical. However, for seismic surveys conducted with large angles of the incidence waves (such as VSP surveys), the effect of a n i s o t r o p y c a n n o t be i g n o r e d ( C r a m p i n 1985a,b). Seismic anisotropy due to aligned

cracks has been extensively studied by, amongst others, Crampin (1984, 1985a,b) and Crampin & Atkinson (1985), who are of the opinion that Swave velocities are more sensitive to the presence of aligned cracks and that they provide a better quality of information on anisotropy effects than does the P wave. Crack orientation, when cracks are aligned vertically, can easily be determined by the splitting of vertically propagating polarized shear waves. This splitting occurs as a result of azimuthal anisotropy induced by the microcracks and fractures. A knowledge of seismic anisotropy can provide useful information about the mineralogy, the orientation of cracks and pores, the degree of cracking and crack geometry, orientation of the in situ stress field, and the possible proportion of gas and liquid within the inclusions in hydrocarbon reservoirs (Crampin 1985a). Thomsen (1986) has derived a set of three dimensionless anisotropy parameters (e, 7 and 8) to describe weak to moderate transverse isotropy of a medium. These parameters are defined in terms of the five components of the stiffness tensor (Cll , C33 , C13 , C44, C66) relating stress and strain for the transversely isotropic medium as follows:

Cll -C33 V 2 1 - V22 -- - -2C33 V 22

SHAKEEL,A. & KING, M. S. 1998. Acoustic wave anisotropy in sandstones with systems of aligned cracks In. HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 173-183

(1)

173

174

A. SHAKEEL & M. S. KING C66 - C44

V 21 - V 22

2C44

V 22

(2)

(~ ~__ (C13 + C44) 2 - (C33 - C 4 4 ) 2 2C33 (C33 - C44)

(3)

The parameters are all zero for an isotropic medium and their deviation from zero represents the degree of anisotropy. The value of ~, which is always positive, represents the relative difference between the P-wave velocities propagating perpendicular (Vp1) and parallel (Vp2) to the axis of symmetry. The general term 'anisotropy' of a rock usually refers to the quantity e, calculated using the following equation for small values o f e, Vp 1 B Vp 2 E -- - -

re2

(4)

The parameter 3' describes the S-wave anisotropy of a transversely isotropic medium. It is the relative difference between the faster S-wave velocity (Vsl) and the slower S-wave (Vs2) velocity travelling in a transversely isotropic medium. Thus, for small values of 7, it can be used to define 'S-wave anisotropy' of a medium (Thomsen 1986) as VS1 m Vs 2

7 -- -

Vs2

(5)

where Vsl and Vs2 are S-wave velocities propagating parallel to the plane of cracks with their polarization parallel and perpendicular to the plane of cracks, respectively. The parameter dominates the anisotropic response when the acoustic wave propagates in a plane which is parallel or approximately parallel to the axis of symmetry. It is independent of the seismic velocities of the medium perpendicular to the axis of symmetry and can take either positive or negative values. As shown by Thomsen (1986), the parameters ~, 3" and g are less than 0.2 in magnitude for weak-to-moderate anisotropy. Furthermore, Thomsen (1986) states that elliptical anisotropy will be observed if 6 = ~. Since the parameters ~, 3' and ~ are easily interpretable and can be calculated from the five elastic constants obtained from Nishizawa's (1982) theory, they are used here to model and study the variation in anisotropy as a function of aspect ratio, crack density and stress.

Experimental system A polyaxial stress loading system, developed at

Imperial College of Science and Technology London, has been used for testing 51mm-side cubic rock specimens. The system, described in a preliminary technical note by King et al. (1995a) and in detail by Shakeel (1995), consists of a loading frame in the form of an aluminium alloy ring within which two pairs of hydraulic rams and ultrasonic transducer holders are mounted to provide orthogonal stresses on the cubic rock specimen in the horizontal plane. Each of the three principal stresses may be varied independently in the range 0 to 115 MPa in the horizontal principal directions and to over 750 MPa in the vertical major principal direction. The horizontal principal stresses may be servocontrolled using facilities associated with a Schenk compression testing machine. The vertical major principal stress is provided through ultrasonic transducer holders mounted in a Schenk 160-tonne closed-loop servo-controlled compression testing machine. Stress is transmitted to each of the six faces of the cubic rock specimen through 5 ram-thick magnesium faceplates matching approximately the elastic properties of the rocks being tested. Deformation of the rock specimen is measured by pairs of extensometers (LVDTs) mounted in each of the three principal directions. An isometric view of the polyaxial loading frame is shown in Fig. 1. Each of the three pairs of transducer holders contains stacks of piezoelectric transducers capable of producing or detecting pulses of compressional (P) or either of two shear (S) waves polarized at right angles propagating in one of the principal stress directions. The transducer holders have a bandwidth in the range approximately 450 to 800 kHz for P-wave and 350 to 750 kHz for S-wave pulses. Loading in the 1-direction is characterized by the major principal compressive stress (cq) direction and that of 2- and 3- as the intermediate (or2) and minor (a3) principal stress directions, respectively. The wave type nomenclature employs two suffixes 'i' and 'j' (as with Vij) where T refers to the propagation direction of the wave and 'j' to the polarization (particle motion) direction. Thus V33 is the P-wave velocity propagating in the minor principal stress direction and V13 is the S-wave velocity propagating in the major principal stress direction with polarization in the 1-3 plane. A total of nine components of velocity are measured: three compressional VPll , VP22 and VP33 and six shear VS12, VS13, VS21, VS23, VS31 and VS32 Both the P- and S-wave velocities are measured with an accuracy of +1% and a precision of +0.5%.

ANISOTROPY IN CRACKED ROCKS

175

LOAD IN 1-DIRECTION APPLIED IN I~I~pRSCHENK

160-TONNE S E R V O - C O N T R O k L E D

ESSION TESTING MACHINE

9

,

,

4.

5.

6.

7. 8. 9.

TRANSDUCER HOLDERS HYDRAULIC PRESSURE, 2-DIRECTION HYDRAULIC RAM, 2-DIRECTION HYDRAULIC PRESSURE, 3-DIRECTION HYDRAULIC RAM, 3-DIRECTION "fRANSDL~ER HOLDERS CUBIC ROCK SPECIMEN REACTION RING

Fig. 1. Isometric view of the polyaxial loading system.

Results and discussion First a numerical example is provided to enable a better understanding of the effects of the different parameters, such as crack aspect ratio, crack density and type of saturating fluid on the anisotropy parameters and on the acoustic velocities of such a cracked solid permeated with aligned ellipsoidal inclusions. Finally, the theory is used to study the anisotropy as a function of stress for a solid progressively permeated with a system of aligned cracks.

Numerical results and discussion In this numerical example, P- and S-wave velocities and Thomsen's (1986) anisotropy parameters are calculated as a function of aspect ratio for a solid permeated with aligned ellipsoidal inclusions. Nishizawa's (1982) theory is used to calculate the elastic constants. The aspect ratio of the inclusions is varied from a =0.0001 (almost flat cracks) to a = 1 (spheres). Four crack densities are studied, ~=0.01, 0.05, 0.10 and 0.20. Both types of inclusions are investigated: dry inclusions with a fluid bulk modulus

of 1.5• -4 GPa, and liquid-filled inclusions with a fluid bulk modulus of 1.5 GPa. The isotropic background material is the same as that used by Nishizawa (1982): matrix density 2.7 g cm -3 and Lame's constants A = # = 39 GPa. Figures 2 and 3 show results of the Thomsen's anisotropy parameters as a function of aspect ratio for dry and liquid-filled inclusions, respectively, for four different crack densities ~= 0.01, 0.05, 0.10 and 0.20. It is clear from these figures that the values of anisotropy parameters increase as the crack density is increased from ~ = 0.01 to 0.20. They all become zero for an aspect ratio a = 1, corresponding to the isotropic situation. Note that for dry inclusions (Fig. 2) all the anisotropy parameters have a non-zero constant value for a large range of small aspect ratios and that they only tend to zero for large aspect ratios approaching a = 1. Hence, for a large group of small aspect ratios the resultant anisotropy is hardly affected by a change in aspect ratio for the case when solid is permeated by dry inclusions. The non-zero constant values of the parameters ~ and 7, for a large group of small aspect ratios for dry inclusions, indicate that

176

A. SHAKEEL & M. S. KING 0.04

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0.15

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>~ o.lo

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." . - : -" . " . ~. - - - : . ' T . . " . ' : "

-".:. 7----.-.-:

: .--.'..--:.--.

- .-""

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(c) . . . . . . . .

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=

. . . . . . . .

I

. . . . . . . .

I

. . . . . . .

0.20

.". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

-

8

p..

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L

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,

. . . .

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Ratio

(d) Fig. 2. Thomsen anisotropy parameters as a function of aspect ratio for dry inclusions for crack densities, (a) ~ = 0.01, (b) ~= 0.05, (c) ( = 0.10, (d) ~= 0.20.

there is also a constant difference between the two P- and two S-wave velocities propagating both parallel and perpendicular to the plane of cracks for the same range of aspect ratios. This conclusion corresponds to Fig. 4 which shows a very small variation in P- and S-wave velocities propagating both parallel and perpendicular to the plane of cracks as the aspect ratio is changed

for dry inclusions of crack density of 0.01. In this figure (also in Fig. 5) the slower acoustic velocities (VP2 and VS2) are represented by dotted lines and the faster (VPi and VS1) by solid lines. A study of Fig. 2 indicates that the anisotropy parameters follow a certain pattern for dry inclusions, i.e. for low crack densities < 0.1, e > 6 > 7, (Figs 2 a-b) and for high crack

ANISOTROPY IN CRACKED ROCKS 0.03

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i

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....o~

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. . . . . .

l

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(d) Fig. 3. Thomsen anistropy parameters as a function of aspect ratio for liquid filled inclusions for crack densities, (a) ~= 0.01, (b) ~= 0.05, (c) ~= 0.10, (d) ~=0.20.

densities ~>_0.1, 6 > ~ > 7 (Figs 2 c-d). However, for a solid permeated with liquidfilled inclusions, a large variation in anisotropy parameters ~ and 6 is observed as the aspect ratio is changed (Fig. 3). The changes in e are related to P-wave velocities, especially VP2 which is strongly influenced by the liquid-filled inclusions as the aspect ratio is changed. This

result corresponds to Fig. 5a which shows a significant variation in V P 2 a s the aspect ratio is changed for liquid-filled inclusions of crack density ~=0.01. It can be seen from Fig. 3 that the value of ~ tends to zero for very small aspect ratios for all the crack densities, indicating that the difference between the P-wave velocities in both the directions parallel and perpendicular to

178

A. SHAKEEL & M. S. KING 6.60

. . . . . . . .

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. . . . . . . .

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. . . . . . . .

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.........................................................................

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. . . . .

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Aspect Ratio

(a)

3.85

Vs~

3.80

3.75

3.70

3.65

3.60

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, ,

0.0001

. . . . . . .

I

. . . . . . . .

0.001

I

,

0.01

,

,

. . . . .

I

0.1

Aspect Ratio

(b)

Fig. 4. Changes in acoustic wave velocities as a function of aspect ratio for dry inclusions (a) P-wave velocities and (b) S-wave velocities. The crack density is ( = 0.01.

the plane of cracks also tends to zero. This effect is clearly shown in Fig. 5a. The changes in ~5are related to those P- and S-wave velocities which are either propagation or polarization perpendicular to the plane of cracks (3-direction). A comparison of Figs 2 and 3 with each respective crack density, indicates that there is hardly any difference in the value of anisotropic parameter 7 for a large range of aspect ratios when either dry or liquid-filled inclusions are used. This behaviour corresponds to the fact that S-wave velocities are not affected much as the condition of inclusions is changed from dry to liquid-saturated. Since the parameter 7 is constant for a large range of aspect ratios for

both dry and liquid-filled inclusions, the acoustic velocities VS1 and VS2 are not strongly affected by aspect ratios (Figs 4b and 5b for a crack density ( = 0.01). For all the cases studied for dry and liquidfilled inclusions, the values of e, 7 and 6 are always positive, indicating that the P- and Swave velocities (VP 1 and VS1) propagating perpendicular to the axis of symmetry are always greater than those propagating along (VP2 and VS2) the symmetry axis (Figs 4 and 5). Finally, Figs 2 and 3 show that the parameters e and 6 are equal for aspect ratios lying between a = 0.4 and 1 for both dry and liquid-filled inclusions. This suggests that the resultant anisotropy is

ANISOTROPY IN CRACKED ROCKS 6.60

........

i

........

i

. . . . . . . .

179 i

Vp l

6.55

6.50

6.45

6.40

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Crack

density

........

6.25

= 0.01

........

I

0.0001

I

0.001

........

i

0.01

.......

O.t

Aspect Ratio

(a) 3.85

.

.

.

.

.

.

.

.

1

.

.

.

.

I . . . .

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~

. . . . . . .

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i

.

.

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.'a

.

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3.70

3.65

3.60 0.01.|

Crack density = 0.01 .

.

.

.

.

.

.

.

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1

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I

.

0.01 Aspect

.

.

.

.

.

.

.

I

,

[

. . . . .

0.1 Ratio

(b)

Fig. 5. Changes in acoustic velocities as a function of aspect ratio for liquid-filled inclusions (a) P-wave velocities and (b) S-wave velocities. The crack density is ~= O.O1. elliptical (Thomsen 1986) for both the dry and saturated cases in this limited range of aspect ratios. Experimental results and discussion

Tests have been performed on five dry sandstone specimens, in which a system of aligned cracks has been first introduced by increasing the major (el) and intermediate (~2) principal stresses in unison to near failure, while keeping the minor (or3) principal stress constant at some low level. The aligned cracks are then closed by the application of a hydrostatic compressive stress. The nine components of velocity are measured throughout three separate stress cycles. The first

involves measurements on the flesh, uncracked rock specimen during the application of an increasing hydrostatic stress. The second cycle involves measurements while a system of aligned cracks, with their normals parallel to the minor stress direction, is formed in the rock specimen (cracking cycle). The third cycle involves measurements during the application of a further increasing hydrostatic state of stress to close the cracks formed during the cracking cycle (crack closing cycle). Discussed here as being characteristic of the studies made on five sandstones tested in this research programme will be that of Penrith sandstone. This is a fine-to-medium grained sandstone of lower Permian age having a low

180

A. SHAKEEL & M. S. KING .

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Fig. 6. P and S-wave velocities as a function of hydrostatic stess during the initial stress cycle for Penrith sandstone sample, (a) P-wave velocities and (b) S-wave velocities.

clay content (3%), an effective porosity of 13%, a permeability of ~150 mD, a grain density 2.6 gcm -3 and a bulk density of 2.26 gcm -3 in its dry state. Figure 6 shows changes in the three P- and six S-wave velocities plotted as a function of hydrostatic stress on the fresh uncracked rock specimen. Although loading in the 1-, 2-, and 3directions is identical there is a small difference between the changes in P- and S-wave velocities which is due to the differences in the initial elastic properties between these three directions. It will be observed that the sandstone exhibits behaviour that is close to being isotropic, with both sets of P- and S-wave velocities increasing in magnitude with increasing stress and lying within + 1% error bar, except at the lowest stress level. Figure 7 shows changes in the three P- and six S-wave velocities during the cracking cycle. The minor principal stress (o'3) was kept constant at 3 MPa while o']--o'2 were increased in unison in steps from 3 to 100 MPa. Then, while maintaining the intermediate principal stress at 100 Mpa (limited by the experimental system), the major

i~

S13 i ....

~ i .... 50

o I = 02 ( M P a )

$32 i .... 75

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S12 i.,,. 100

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F i g . 7. P a n d S - w a v e v e l o c i t i e s a s a f u n c t i o n

of stress

during the cracking cycle for Penrith sandstone sample, (a) P-wave velocities and (b) S-wave velocities.

principal stress was increased in steps to 132 MPa until the specimen was near failure. The acoustic velocities propagating in the 3direction show an initial increase with stress due to the closure of pre-existing cracks with their normals in the 1- and 2-directions, followed by a decrease as dilatant cracks with normals parallel to the 3-direction begin to form and open up. It is concluded from Fig. 7, with VPll~VP22 and VS12~,~VS21 all increasing monotonically, that the majority of the cracks formed are aligned in the 1-2 plane, perpendicular to the 3-direction. Shear wave birefringence occurs in all directions of propagation except along the symmetry axis (3-direction) for obvious reasons of symmetry. This effect was also observed in the experiments of Nur & Simmons (1969). The cracking cycle velocity data plotted in Fig. 7 indicate that the magnesium plates match the sandstone well in elastic properties up to stresses of o1 = cr2 = 100 MPa, when the majority of the aligned cracks are formed. As o'] is further increased (with o'2 constant), the platens cause confinement and the S-wave velocities propagating in the 1- or 2-direction and polarized in the 3-direction (V13 or V23) become higher than the

ANISOTROPY IN CRACKED ROCKS 5~

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o l 9 o2 = 100MPa "4''I

(a)

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,~.,

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Aspect Ratio 2.9

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Crack Density

01 - 0 2 - 0 3 (MPa)

Fig. 8. P and S-wave velocities as a function of hydrostatic stress during the crack closing cycle for Penrith sandstone sample, (a) P-wave velocities and (b) S-wave velocities.

Fig. 9. Thomsen's (1986) anisotropy parameters for Penrith sandstone sample during the cracking cycle as a function of (a) stress and (b) crack density of the aligned cracks.

velocities that are propagating in the 3-direction (V31 or V32). This behaviour suggests that the aligned crack density towards the extremities of the specimen in the 3-direction is higher than in the centre for values of stress greater than 100 MPa. Figure 8 shows changes in three P- and six Swave velocities plotted as a function of hydrostatic stress during the subsequent crack closing cycle. As the stress is increased, both sets of Pand S-wave velocities appear to be approaching asymptotic values that are only slightly lower in magnitude than those shown in Fig. 6 for the preliminary uncracked cycle. Upon removal from the loading frame after completion of the tests, the specimens all showed signs of throughgoing fractures aligned close to normal to the 3direction. The nine components of velocity determined as a function of stress during the cracking and subsequent crack closing cycle have been used to evaluate the Thomsen's (1986) anisotropy parameters and crack density for the cracks aligned perpendicular to the symmetry axis (3-direction). The procedure, employing Nishizawa's (1982) theory, first to model the velocity data, is

described in detail by Shakeel (1995), who found excellent fits (within 4-1% at all stress levels) in comparing the theoretically modelled and the laboratory measured velocities during both the cracking and crack closing cycles. As the Penrith Sandstone was tested in its dry state, a value of 1.5x 104 GPa was chosen for the fluid bulk modulus. A range of aspect ratios (0.0005 to 0.002) was employed during each of the stress cycles to obtain the best match between the modelled and experimental velocities. It was found that a value of aspect ratio c~--8.0xl0 ~ provides the best match between the modelled and the experimental velocities during the cracking cycle and for the crack closing cycle for most of the stress levels. Figure 9a shows changes in the anisotropy parameters e, 7 and 6 as a function of (71 and 0"2 during the cracking cycle, during which o.3 was kept constant at 3MPa. All the anisotropy parameters increase as the stress is increased due to an increase in crack density (Fig. 9b). The rate of increase in the value of these parameters is lower as the stresses o-~ =o-2 are increased initially from 2 to 100 MPa, but it becomes much

182

A. SHAKEEL & M. S. KING .

.

.

.

I

,

.

Aspect ratio

.

.

.

. . . .

i . . . .

Aspect ratio

a = 0.001~;

i

i . . . .

. . . .

i

. . . .

i'.

a = 0.0005

/

o 3 - 2 MPa (Constant)

Delt~

1.5

Epsilon,

9 ~

1.0

y

Gamma. ,

,

~ ,

,

t

,

,

,

,

I

25

,

,

,

,

50

. 25

I~ c; 1 = 02 = 03

.

.

.

i

. "~.

.

50

,~.

.

75

.

,

. . . .

n . . . .

100

1~

13o

75 0 1 = 02 ( M P a )

(MPa)

-' ~

o I > o2= I00MPa

(a)

(a)

. . . .

t

,

Aspect Ratio

J

9 -

J . . . .

L . . . .

L . . . .

Dolts,

a = 0.0015

i . . . . 6 "--'~/~

U) 0.02

0.04

0.06 Crack

0,(]~

0.I

0.12

0,05

O.l Crack

Density

0. I$

0.2

0.25

Density

(b)

(b)

Fig. 10. Thomsen's (1986) anistropy parameters for Penrith sandstones sample during the crack closing cycle as a function of (a) stress and (b) crack density of the aligned cracks.

Fig. 11. Thomsen's (1986) anistropy parameters for Crosland Hill sandstone sample during the cracking cycle as a function of (a) stress and (b) crack density of the aligned cracks.

higher at higher stresses. The higher rate of increase of anisotropy parameters for stresses o'1 >a2 = 100MPa is due to the nucleation and coalescence of the majority of the aligned cracks, which is also clear from the sharp decrease in V33 and S-wave velocities propagating or polarized perpendicular to the plane of cracks (Fig. 7). Figure 10a shows changes in the anisotropy parameters e, 7 and 6 as a function of hydrostatic stress during the crack closing cycle. All the anisotropy parameters decrease as the stress is increased due to a decrease in crack density (Fig. 10b). Cracks close very quickly during the initial loading, resulting in a consequent rapid decrease in the value of the anisotropy parameters. When the stress is increased further, a major fraction of the crack surface area comes into close contact which slows down the closure of cracks and the anisotropy parameters decrease much more slowly than before. Results in Fig. 10 show that the anisotropy becomes weakto-moderate and elliptical (e = 6) for hydrostatic stresses >10 MPa.

The anisotropy parameters follow the same pattern during the cracking and crack closing cycles, i.e. at each stress level ~ > 7 indicates that the anisotropy in P-wave velocities is greater and more sensitive to the crack density than the anisotropy in S-wave velocities. Furthermore, for higher crack densities (~> 0.08), ~ is greater than ~, which is in accordance with the prediction of Nishizawa's theory as shown in Fig. 2. A study of Figs 9 and 10 also indicates that elliptical anisotropy is only possible in the weak anisotropic region (anisotropy p a r a m e t e r s < 0.2) which occurs only at low crack densities (~ < 0.06). These results are true for all the other sandstones tested in this research program. As an example, Figs 11 and 12 show similar anisotropy results for the Crosland Hill (low clay content [< 1%], effective porosity 6% and permeability < l mD) sandstone specimen during the cracking and crack closing cycles.

Conclusions (1) The results of the experimental study for

ANISOTROPY IN CRACKED ROCKS .

.

.

.

i

.

.

.

.

i

.

.

.

.

i

.

.

.

.

Aspect ratio ct 0.0005 =

t.6

~

171tt,

8

': 25

50 oI=

183

75

We wish to acknowledge with thanks, the support provided by Shell Expro, British Gas, BP Exploration and AGIP for this research project. The senior author is especially indebted to N. Hyder of the Joint Venture Department, OGDC, for arrangements, and the Oil and Gas Development Corporation of Pakistan for providing the finance necessary to present this paper. Special thanks are also due to Dr N. A. Chaudhry for providing data of one of his specimens to conduct some of the modelling work.

ioo

c 3 (MPa)

o z =

(a) References

. . . . . . .

i

. . . .

0 . . . .

Aspect Ratio cc= 0.0005 "4

/

#~ /

i

. . . .

Delta, 8

/ :,= 1.6 d

o.0

Ion ,

i

/7 o.1

0.2 Crack Density

o.3

S. 1984. Anisotropy in exploration seismics. First Break, 2, 19-21. - 1985a. Evaluation of anisotropy by shear wave splitting. Geophysics, 50, 142-152. 1985b. Evidence for aligned cracks in the Earth's crust. First Break, 3, 12-15. - • ATKINSON, B . K . 1985. Microcracks in the Earth's crust. First Break, 3, 16-20. KING, M. S., C H A U D H R Y , N. A. & SHAKEEL, A. 1995a. Experimental ultrasonic velocities and permeability for sandstones with aligned cracks. International Journal of Rock Mechanics and Mining Science and Geomechanics Abstracts, 23, 291-302. - - , SHAKEEL, A . & C H A U D H R Y , N . A. 1995b. Acoustic wave propagation and permeability in sandstones with systems of aligned cracks. Presented at the Geophysical Society of London, Borehole Research Group, Conference on Developments in Petrophysics, Sept, 1995. NISHIZAWA,O. 1982. Seismic velocity anisotropy in a medium containing oriented cracks--transversely isotropic case. Journal of the Physics of the Earth, 30, 331-347. NUR, A & SIMMONS,G. 1969. Stress-induced velocity anisotropy in rocks: An experimental study. Journal of Geophysical Research, 74, 6667-6674. POSTMA,G. W. 1955. Wave propagation in a stratified medium. Geophysics, 20, 780-806. SAYERS, C. M. 1988. Stress induced ultrasonic wave velocity anistropy in fractured rock. Ultrasonics, 26, 311-317. SHAKEEL, A. 1995. The effect of oriented fractures on elastic wave velocities, attenuation and fluid permeabilities of sandstones. PhD Thesis, Imperial College of Science, Technology and Medicine, University of London. THOMSEN, L. 1986. Weak elastic anisotropy. Geophysics, 51, 1954-1966. Xu, S & KING, M. S. 1989. Shear-wave birefringence and directional permeability in fractured rock. Scientific Drilling, 1, 27-33. & - 1992. Modelling the elastic and hydraulic properties of fractured rocks. Marine and Petroleum Geology, 9, 155-166 U H R I G , L. F & VAN MELLE, F. A. 1955. Velocity anisotropy in stratified media. Geophysics, 20, 774--779 CRAMPIN,

0.4

Fig. 12. Thomsen's (1986) anistropy parameters for Crosland Hill sandstone sample during the crack closing cycle as a function of (a) stress and (b) crack density of the aligned cracks.

dry sandstones suggest that the anisotropy parameter 6 is the most sensitive to the crack density. Moreover, for the dry rocks, the anisotropy in P-wave velocities is greater and more sensitive to the crack density than the anisotropy in S-wave velocities. (2) The results of the numerical study suggest that the anisotropy in P-wave velocities is greater when the saturating fluid is very compressible (gas) and when the cracks are flat (small aspect ratios), while the anisotropy in S-wave velocities is almost unaffected by the nature of the saturating fluid. (3) For dry inclusions over a large range of aspect ratios less than 0.1 the resultant anisotropy is hardly affected by a change in the aspect ratio. (4) The results of the numerical and experimental studies suggest that elliptical anisotropy will be observed in a m e d i u m containing aligned ellipsoidal inclusions of aspect ratios greater than 0.4.

Complementary functions reveal data hidden in your logs J. R. S A M W O R T H Wireline Technologies Limited, East Leake, Loughborough, Leicestershire L E 1 2 6JX, U K

Abstract: Many logging tools make multiple measurements of the same type that have more than one depth of penetration. Common examples are Compensated Density, Compensated Neutron and Array Induction logs. The purpose of the compensation is to reduce or remove the effects of a disturbance that distort the true measurement. Examples of this disturbance are the borehole size, mudcake and salinity. A general technique can be derived based on a theory of Linear Perturbation which requires no prior knowledge of the nature of the perturbation, the only requirement being that it is approximately locally linear. Various interpretations can be made of the general equation depending on the particular circumstances. The technique also produces a Complementary Parameter associated with the degree of correction. This parameter is usually discarded or paid scant regard, but can often be of some significant value and exposes surprising information. A number of examples can be used to illustrate these techniques, showing that they have wide applicability in situations ranging from difficult logging conditions (e.g. density through casing) to the apparently routine, where unusual and unexpected borehole fluids are revealed from neutron logs.

A very common method in wireline logging is to employ a system of transducers making similar measurements, spaced out along the logging tool. The main reason for this is to provide measurements with multiple depths of penetration in order to compensate for the effects of some disturbance to the measurement. This disturbance can have a multitude of origins, such as the borehole itself, its size, fluid nature, caliper fluctuations, etc., or near-borehole effects such as invasion. The compensation relies on the disturbance being common to the array of transducers, and requires a model to describe the disturbance (e.g. a step invasion profile). The multiple spacings employed have differing vertical resolutions, and much effort in recent years has been spent optimizing this resolution by ensuring that boundary information is not lost. The V E C T A R (Vertical Enhancement by Combination and Transformation of Associated Responses) computational technique is one such method (Elkington et al. 1990). In this paper, we will consider the converse of this combination method and develop equations which are not dependent on a pre-imposed model but are very general. In the process of doing this, we will see that another parameter is revealed that is orthogonal to the true value that is being examined.

Orthogonalization A definition of an orthogonal pair of parameters

is that although they are associated, varying one of them does not vary the other. For example, invasion depth and Rt (resistivity) are orthogonal, because varying Rt does not affect the invasion depth, and vice versa. However, if we measure resistivity using a dual induction tool, the deep and medium measurements are not orthogonal since variation in R t affects both deep and medium logs. The tornado chart shown in Fig. 1 is an attempt to transform the measurements into an orthogonal set, the tornado being a skewed orthogonal co-ordinate system. All dual or multiple measurements are intended to achieve similar objectives. It will, however, be noticed that the transformed orthogonal pair has two parameters--we get depth of invasion as well as Rt. This value of depth of invasion is an example of the orthogonal Complementary Parameter.

Linear perturbation and sharpness Let us consider an observation O, looking at a true value V, subject to a perturbation P. Let us suppose we perform all the chart book corrections we can (borehole size, etc.) but we are still left with a perturbation we cannot measure directly. Let us further assume that the perturbation is reasonably small and that it disturbs the observation from its true value in a linear way. We can then write:-

0 = V+ KP

where K = t h e proportionality constant i.e. the

SAMWORTH,J. R. 1998. Complementary functions reveal data hidden in your logs. In." HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 159-171

(1)

159

160

J.R. SAMWORTH

Fig. 1. A Tornado Chart--an example of a skewed orthogonal co-ordinate system.

perturbation rate. If we make two observations with different transducers but subject to the same perturbation we get:0 1 = V-t-

K1P

02 = V+ K2P.

(2)

(3)

We can eliminate the perturbation P from the two equations and solve for the true value V. With some re-arrangement, we then get: V -- O 1 ~-

(O1 - 0 2 )

(4)

K1 This is arranged in the following form:(True value) = (Observed value) + (Correction). It is important to note that the correction depends on the two observations and the ratio of the perturbation rates K2/K1 and not the individual rates themselves. This is very signifi-

cant, as the ratio of perturbation rates is easier to calculate, and additionally the rates can change their absolute values without invalidating equation (4) as long as the rate ratio is unchanged. We can also solve equations (2) and (3) for the perturbation P by eliminating V:/9__ 0 2 -- O1 K2 -- K1

(5)

This parameter is the Complementary Parameter and is orthogonal to the true value. It can often be numerically scaled into some useful unit but is frequently ignored. If the perturbation is due to a variety of different effects they become lumped into a correction that cannot be assigned an explicit physically meaningful value, so P becomes Unsharp. This is the price of getting a good assessment of the true value, V, which is Sharp. It is, however, often the case that many of the lumped parameters are constant over the length of the borehole. If these values can be ascertained independently, and any one of the

HIDDEN DATA IN LOGS

161

Fig. 2. Mudcake thickness from Density logs.

perturbations varies significantly over the borehole, this variable parameter can be derived explicitly and a curve plotted. That is, it becomes

is usually one of optimization. We can encapsulate this principle thus:-

Sharp.

a computational process on a measurement can only be justified if the result after the process is better than the original.

Principle of betterness Before considering examples of the application of Linear Perturbation it is prudent to consider our objectives. The main objective is to improve the quality of a measurement, not necessarily to make it absolutely correct, because this depends on the quality of our assumed model. We can often become unnecessarily obsessed with correctness, whereas the log interpretation process

Alternatively:if a measurement can be improved by applyNg a computational process it is usually worth doing. The result does not have to be correct, only better.

An example of this principle at work is the Compensated Density Log. In the presence of a

162

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mudcake, the corrected log is usually more accurate than either of the two component originals. If, however mudcake is not present, the compensated log is subject to composite errors from both measurements and can actually be worse than either of them. This situation can often occur in practice, especially in slim wells. We can see that we must, therefore, apply the technique circumspectly.

Application of linear perturbation We will now consider several applications of the theory.

Density logs We can apply equation (4) directly to the long and short-spacing density logs.

We then get:-

pt=pn+

where pt PL Ps Ks & KL

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(PL--PS)

(6)

True Density Long Spacing Density Short Spacing Density Perturbation rates.

This equation is identical to that derived by applying the geometric factor theory to density logs (Samworth 1992). If we wish to explore the complementary parameter we need to set up the original equations. Density logs can be expressed in terms of a Geometric Factor J:

HIDDEN DATA IN LOGS

163

Fig. 4. Use of a derived Apparent Caliper to improve Slim Array Induction logs.

PA= Jpm~+ (1 - J ) Pt Pmc = mudcake density. Rearranging; PA = Pt + J(Pmc -- Pt).

(7)

(8)

If we approximate J to a straight line function of standoff, d, i.e. we get:-

J = Kd,

PA = Pt + Kd (Pine-- Pt).

(9) (10)

This is the linear perturbation equation from which (6) can be derived. We can now use equation (5) to derive the standoff, d i.e.

Ps--PL d=(Pmc-P,)(Ks-KL)"

(11)

A log of this mudcake thickness is shown in Fig. 2. It is, of course, similar in character to the density correction but is scaled in inches.

Density log through drill pipe Occasionally, circumstances arise when the borehole stability is so poor that it is not possible to leave the hole open for conventional logging. It is then possible to run a variant of the density tool inside the drill pipe to log the density of the formation outside the pipe. In this case, there is no mudcake and we have no knowledge of the borehole caliper. A special form of density tool is employed which has no preferential circumferential collimation, i.e. it looks all round the hole. The long and short detectors are calibrated for the through-pipe conditions, and linear perturbation

164

J.R. SAMWORTH

Fig. 5. Invasion indications from a Slim Dual Induction Log.

applied. The degree of correction and complementary functions are not now associated with mudcake. Figure 3 shows a compensated density log obtained in this manner. If the original sharp value required is the bulk density, we do not have to assign an explicit meaning to the density correction; it can remain largely unsharp. Although unsharp, it is still probably safe to

assume that areas of high corrections are areas of hole enlargement. If we pursue this assumption, we can compute a caliper log using equation (11). This caliper log is shown in Fig. 3.

Caliper from array induction logs Induction logs can be combined in a similar way. Since inductions measure conductivity, we can

HIDDEN DATA IN LOGS

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Fig. 6. Cross plot to indicate effect of perturbations on Neutron log count rates. set up the linear perturbation system as in equations (2) and (3). This has been previously explored for slimline array tools (Samworth et al. 1994). Figure 4 shows an example of this application. A difficult horizontal well was logged with a slim array induction tool, without an opportunity to run any other log. The borehole fluid was saline (.07 52 f2m) and since no caliper was available to correct the logs, they were apparently quite poor. (The right hand set of curves in Fig. 4). Since we know the mud resistivity, by assuming that the two shallowest measurements see no further than the invaded zone we can use linear perturbation to calculate an apparent caliper. This caliper, shown in the left-hand track, was then used to correct the deeper reading measurements. A much more systematic log then results (in the centre track of Fig. 4). This is a form of optimization of the induction logs, and it leads to a better product without necessarily being absolutely correct.

Invasion indication from induction logs Figure 5 shows what can be achieved with a simpler slim dual induction tool. Only two conductivities are measured here, but unlike the previous example, the caliper is known as well as the mud resistivity, and the appropriate corrections can be applied. When linear perturbation is applied here, as well as deriving Rt, we get a lumped complementary function associated with both Rxo and invasion diameter. This is shown as the shaded curve in Fig. 5 and gives some indication of the character of the invasion. It can be seen, for

example, that there are three permeable zones, the invasion of the lower two being uniform since the invasion indicator is of a trapezium shape. However, the upper zone shows a graded form on its top edge, probably indicating a gradation in permeability.

Borehole fluid salinity from neutron logs We can adapt linear perturbation to the dual neutron tool. Dual neutron tools are usually designed so that the ratio of the count rates from near and far detectors is related to the formation porosity. The design is normally such that the sensitivity of this ratio to such things as borehole fluid salinity is minimized. However, a complementary function can be calculated specifically to be sensitive to this salinity. This can be seen in Fig. 6 where we have cross plotted the short spaced count rate against the long spaced. On this plot, all points on a straight line through the origin have the same ratio, and this represents the same porosity. A line can also be drawn for constant salinity but with porosity varying. This mesh is a skewed orthogonal system, as described earlier. In setting up the linear perturbation equations in this case, we shall use the two count rates V1 and V2, which are the unperturbed values. So we now get: O1 = V1 +K1P

(12)

0 2 = V 2 + K2P.

(13)

We can establish a relationship between 01 and 02 by eliminating P. We get:

166

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Fig. 7. Borehole salinity from Neutron logs (1).

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K1

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(14)

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(15)

For the particular case of ratio processing: K1 01 V1 m = / ( 2 - 02 -- V2"

(16)

Since we actually observe Ol and O2, m can be calculated and we can migrate along the line until we reach V] and V2. This method has

previously been explored in some detail (Samworth, 1991). If, however, we can identify the positions of O] and 02 on the ratio line, we can identify which line of constant salinity we are on, and we can then estimate the borehole salinity. Some examples now follow to show the usefulness of this complementary parameter. Figures 7, 8 and 9 show sets of logs, on a compressed vertical scale in a dolomite reservoir. The field was being produced by an injected waterflood, and the wells were close to each other. The reservoir section is the whole of the lower halves of the wells where the gamma ray log activity increases.

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Fig. 11. Borehole salinity in a horizontal well (2). Figure 7 shows a well where the well fluids were static. The salinity indicator shows low salinity, i.e. oil, above a high salinity sump in the reservoir section. Figure 8 shows a log taken with the well flowing, i.e. the injector had not been turned off. Here the salinity profile is inverted, but the inversion starts several hundred feet into the reservoir. Figure 9 is similar to Fig. 7, but with a blip at a similar place to where there is a change in Fig. 8. The conclusion from these logs must be that the waterflood is breaking through at the top of the reservoir and not efficiently sweeping the lower levels.

Figure 10 shows a horizontal well where there are several intervals with an anomalous Array Induction response (e.g. at 5810-5910). The neutron based salinity indicator shows high levels at these points indicating water plugs in an otherwise oil-filled well. The nuclear logs are shown in Fig. 11 for reference. Comparison of the logs with a plot of the hole trajectory shows depressions at these points, the salinity indicator showing that these are full of water.

Conclusion There is much information to be had from well logs by interpreting them in a slightly unconven-

HIDDEN DATA IN LOGS tional way, so it is imprudent to discard any data, especially raw data. The linear perturbation technique is completely general, and does not rely on any particular physical model. It is applicable to a wide variety of logs where multiple measurements of a similar type are made. The method also produces a complementary parameter which can be very useful in revealing effects not apparent on the normal logs.

This paper illustrates some of the work carried out by the Research and Development Department of Wireline Technologies Ltd, and grateful thanks are given to that company for permission to publish.

171

References ELKINGTON,P. A. S., SAMWORTH,J. R. & ENSTONE,M. C. 1990. Vertical enhancement by combination and transformation of associated responses. Transactions of the 31st Annual Logging Symposium, SPWLA, Paper HH. SAMWORTH, J. R. 1991. Algorithms for compensated neutron logging--57 varieties. Transactions of the 14th European Logging Symposium, SPWLA, Paper A. - 1992. The dual-spaced density log, characteristics, calibration and compensation. Log Analyst 33, 4249. , SPENCER, M. C., PATEL,H. K. & ATACK,N. A. 1994. The array induction tool advances slim hole logging technology. Transactions of the 16th European Logging Symposium, Paper Y.

In situ stress prediction using differential strain analysis and ultrasonic

shear-wave splitting B. W I D A R S O N O , 1 J. R. M A R S D E N 2 & M. S. K I N G 2 1Lemigas, Jakarta, Indonesia 2 Department o f Earth Resources, Engineering Royal School o f Mines, Imperial College, London S W 7 BP, U K Abstract: Knowledge of the /n situ states of stress in rock masses is of considerable importance to a number of subsurface engineering activities, including those involved in exploiting petroleum and geothermal energy reserves. In this paper, a comparison is made of two laboratory techniques, based upon stress-relief microcracks, for determining the in situ state of stress: differential strain analysis (DSA) and ultrasonic shear-wave splitting (USWS). Measurements on ten sandstone samples recovered from deep boreholes, made using the well-established technique of DSA, have been compared to those made by the comparatively new technique of USWS and to sleeve fracturing measurements of in situ stress made in the corresponding boreholes. The results obtained indicate that the USWS technique, with its ability to test a large number of samples quickly, provides a useful adjunct to DSA and sleeve fracturing in determining trends in in situ stresses. Used in combination, the two laboratory techniques have also proved useful for examining rock micro-structural features.

A number of operations involved in the exploitation of petroleum and geothermal energy resources require a knowledge of the in situ state of stress. Such data are required for determining borehole stability in the drilling phase, for avoiding solids production problems and for hydraulic fracturing stimulation in the production phase, and for reservoir characterization in reservoir engineering. A common method for obtaining in situ stress data from great depth is indeed by hydraulic fracturing or, alternatively, sleeve fracturing (Desroches et al. 1995). These techniques, however, possess certain disadvantages with regard first to cost and second to technical considerations in fractured formations, deviated well bores and high pressures and high temperature formations. Results obtained from these methods are often influenced and biased by stresses close to the well bore and hence do not reflect the governing in situ stress field. To overcome some of these problems, the technique of differential strain analysis (DSA) has been developed. Since it was first suggested by Strickland & Ren (1980) as a tool for in situ stress determination, DSA has been used frequently; successful applications have been reported by various investigators (Dey & Brown 1986; Dyke 1988; Oikawa et al. 1993). Nevertheless, considerations of the length of time required for a DSA test have led to efforts to find alternative methods. Early studies (Yale & Sprunt 1989) utilized the phenomenon of ultrasonic S-wave splitting on rock specimens

taken from great depth which contain stress relief microcracks. The main objective of this study is to contribute to the development of the technique and to compare the results obtained with those from other proven methods. For the purpose of this study, specimens were prepared from core samples taken from various petroleum wells in the Irish and North Seas. The samples comprised ten sandstone specimens from depths lying between 1361 and 1422 m and between 3232 and 3304 m, eight of which (SSA) were oriented and so could be used for determining the actual orientation of the in situ stresses. The other two samples (SSB) were not oriented; they are, however, considered valuable for further comparison studies. Descriptions of the rock samples are provided in Table 1.

Differential stain analysis (DSA) Technique DSA is a method based on the existence of oriented microcracks generated within core samples as a result of stress relief processes which occur during the core process and recovery of the cores from depth. Evidence of the existence of this type of microcracks has been reported by various investigators using different approaches (Kowallis & Wang 1983, amongst others), such as comparing images from scanning electron microscopy (SEM), studying P- and S-wave velocities adjacent to

WIOARSONO,B., MARSDEN,J. R. & KIN6, M. S. 1998. In situ stress prediction using differential strain analysis and ultrasonic shear-wave splitting In. HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 185-195

185

B. WIDARSONO ET AL.

186

Table 1. Description of sandstone samples Sample

Depth (m)

Grain size (mm)

Comments

SSA-1 SSA-2 SSA-3 SSA-4 SSA-5 SSA-6 SSA-7 SSA-8 SSB-1 SSB-2

1361 1363 1364 1366 1374 1399 1409 1422 3232 3304

0.5 0.1-1.0 0.5-1.0 0.54).75 0.5 0.14).5 0.14).5 0.5 0.14).5 0.14).5

Well cemented, very weak bedding Well-cemented, poorly-sorted, weak bedding Fairly well-cemented, strong bedding Well-cemented, no sign of bedding Well-cemented, weak bedding (possible micaceous laminae) Dark red, very weak bedding Well-cemented, weak bedding/laminae Well-cemented, strong bedding Well-cemented, no bedding/laminae Well-cemented, no bedding/laminae

the borehole and in the laboratory, and employing differential strain analysis itself. For example, Teufel (1983) observed anisotropy of P-wave velocity measurements and correlated this with results from the anelastic strain recovery technique (ASR). Basic assumptions of the technique are presented in length by Strickland & Ren (1980) who, in brief, assume that aligned microcrack densities in different axes are proportional to the relieved stress magnitudes in these axes. Consequently, when the sample is compressed hydrostatically, the resulting strains will show preferential orientation in magnitudes which are proportional to the microcrack densities. A further assumption is that all microcracks existing within a tested sample are of stressrelief type, or at least that all pre-existing microcracks do not affect sample deformation significantly.

Experimental procedure Three basic steps are followed in specimen preparation: machining the specimen, attaching the strain gauges and encapsulating the specimen. Each step in specimen preparation must be performed carefully in order to prevent generation of new microcracks in the rock sample. Flat surfaces are machined on each specimen in at least three orthogonal directions, by first sawing them with a diamond saw and then by hand lapping or surface grinding. After ovendrying at 35 ~ strain gauge rosettes are attached to the specimen following the arrangement shown in Fig. 1. The rock sample is finally encapsulated with epoxy resin in an elastomer membrane. The strain gauged and encapsulated specimen is placed in an oil-filled pressure vessel and left for approximately 24 h in order to ensure temperature stabilization. This procedure avoids any temperaturerelated fluctuations while strain

Fig. 1. Axes system and strain gauge orientations.

gauge readings are made during the test. Hydrostatic pressure on the specimen is first increased in steps of 200 psi to 21 MPa (3000 psi), then in steps of 500 psi to 55 MPa (8000 psi). During this loading procedure, a transition from microcrack closure to intrinsic elastic deformation is generally found to occur. From 55 MPa (8000 psi) to the maximum pressure (usually around 83 MPa-12000 psi) 1000 psi increments are usually chosen since microcrackfree linear stress-strain behaviour generally occurs in this range of pressures. A strain gauged and sleeved fused silica specimen of known physical properties is also tested adjacent to the rock test specimen to provide any corrections necessary for environment-related non-linearities in the specimen and strain gauge responses.

Procedure of analysis Additional input data is required for analysing DSA measurements, including: vertical in situ or

IN SITU STRESS PREDICTION Pressure

(MPa)

100

/

80

60

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Fig. 2. Typical compression curves from DSA test. overburden stress, /n situ pore pressure, and Poisson's ratio for the tested rock. The orientation of the reference line with regard to the axes of the cubic specimen is also required if the true orientation of the in situ stress field is to be determined. The strains recorded by the data logger are fitted by a series of curve-fitting programs to produce the pressure-strain curves by way of a quadratic fit using five adjacent data points (Dyke 1988). A microcrack closure strain tensor is obtained from these quadratic compression curves (Fig. 2) for each of the specimens tested. To create a complete microcrack strain tensor, six components only are required from the twelve strain gauge measurements. This permits a statistical analysis to be made of the redundant data. From the microcrack strain tensor, the principal microcrack strains and their orientations relative to the reference line are calculated using the method proposed by Strickland & Ren (1980). The ratios of principal strains are taken as the ratios of the principal effective stresses and, by a series of tensorial transpositions to vertical and horizontal planes, the principal strains can be converted to principal in situ stresses using values of overburden stress, pore pressure and Poisson's ratio. Furthermore, the true orientations of the stresses can be determined if the true orientation of the reference line is known.

Ultrasonic shear wave splitting (USWS) Technique The use of acoustic S-wave splitting (birefringence) as a source of information regarding the

187

medium through which it propagates remains relatively novel despite its origins in studies of earthquake seismology. Despite the abundance of observations, it is still not clear exactly what causes S-wave splitting in the Earth's crust (Crampin & Lovell 1991), although it is generally taken to be caused by aligned discontinuities. Crampin (1978) recognized that most rocks in the crust are likely to contain discontinuities, and that S-wave splitting is probably the most diagnostic feature of wave propagation in such anisotropic rocks. Attempts have been made to relate S-wave splitting to the orientation of in situ stress-relief induced microcracks in cores taken from great depths. Yale & Sprunt (1989) utilized ultrasonic S-wave splitting on oriented core samples, and concluded that this approach can be used to predict the orientation of the major horizontal in situ stress. Shear-wave splitting results from the division of a polarized S-wave into two separate polarized S-waves travelling at different speeds when a source of anisotropy is encountered in its path. When a plane polarized S-wave is propagated through a medium containing a set of aligned microcracks it will be split into two orthogonally polarized S-waves, with one polarized parallel to the plane of the microcracks and the other, travelling at a lower velocity, polarized perpendicular to the plane of the microcracks. Garbin & Knopoff (1975) proposed a theory to explain the velocity variations caused by a single set of parallel cracks which is based on scattering of elastic waves by penny-shaped cracks. The theory explains the variation of S-wave velocity with changes in ray path angle and wave polarization relative to the crack plane. The degree of splitting is related to the time lag between the arrival of the two waves at the receiver. The degree of S-wave splitting and hence velocity anisotropy increases with an increase in crack density.

Experimental procedure and analyses As part of this study, S-wave splitting tests were conducted on the same specimens as used in the earlier DSA tests, except in the case of one which exhibited such a poor degree of cementing that satisfactory acoustic coupling between specimen and transducers could not be achieved. The elastomer sleeves were removed from the DSA samples tested earlier and the latter were re-cut with flat surfaces perpendicular to the vertical (Z-axis) and horizontal (X- and Y-axes). They were then oven dried at 35~ so that the specimens could be tested dry. The principal

188

B. WIDARSONO ET AL.

Table 2. In situ stress data from DSA tests 0.1

0.2

0.3

Sample

o1/0. V 1

Azimuth (0)2

Dip (0)2

0"2/0"1

Azimuth (0)2

SSA-1 SSA-2 SSA-3 SSA-4 SSA-5 SSA-6 SSA-7 SSA-8 SSB-1 SSB-2

1.005 1.017 1.010 1.130 1.000 1.059 1.001 1.004 1.004 1.008

204N 106N 116N 74N 38N 331N 229N 164N 240 179

80 73 76 59 87 66 86 81 83 74

0.892 0.824 0.877 0.939 0.832 0.760 0.902 0.866 0.888 0.928

71N 3 08N 213N 351N 293N 52N 326N 20N 132 88

Dip 0"3/0"1 (O) 7 17 2 4 3 2 0 7 2 1

0.849 0.807 0.832 0.805 0.817 0.676 0.824 0.835 0.763 0.879

0"3/0"2

Azimuth (0)2

Dip (o)

0.955 0.982 0.948 0.858 0.982 0.983 0.914 0.966 0.858 0.948

340N 219N 304N 262N 203N 140N 57N 290N 42 358

7 1 14 26 3 23 4 6 7 16

1 0.v = vertical or overburden stress. 2 Azimuths measured clockwise with respect to North (Z-axis) or, for unoriented cores (SSB), clockwise from reference line (X-axis).

axes (Fig. I) and reference lines used were the same as those used in the DSA tests, in order to maintain compatibility between the DSA and Swave splitting results. USWS measurements are performed by rotating the rock specimen containing stress relief microcracks while pulses of planar S-wave are transmitted parallel to the specimen axis under nearatmospheric pressure conditions. In the presence of aligned microcracks, S-wave first arrivals observed by the receiving transducer (polarized parallel to the polarization of the transmitter) show changes in magnitude as the specimen is rotated. When the direction of polarization of the transducers is parallel to the aligned microcracks, the S-wave first arrival time is a minimum. Conversely, when the transducer polarization is perpendicular to the aligned microcracks, the first arrival time is a maximum. This direction is that of the greatest stress relaxation and hence is the major in situ stress direction. Each specimen was first assembled between pairs of broadband transducers having S-wave frequencies in the range 300 to 800 kHz (as described by King et al. 1995) with a proprietary visco elastic S-wave couplant and a stress of 2.5 MPa applied to the transducers to provide good acoustic coupling. This level of axial stress has been shown experimentally to have a negligible effect on cracks oriented sub-parallel to this direction of propagation of S-waves. At the start of a test, each specimen is arranged so that the transducer polarization is in the Y-axis direction and the propagation of acoustic energy is either in the Z-axis direction (vertical) or X-axis direction (horizontal). During a test, the specimen is rotated through an angle between 0 ~ and

180 ~, measured relative to the reference line, in increments of 15~ At each 15~ step, the transit time of first arrivals and waveforms in digital form are recorded. After each test, the S-wave transit time is converted to velocity.

Experimental results D i f f e r e n t i a l s t r a & analysis

Results of the in situ stress predictions for all specimens tested are shown in Table 2, in which stress magnitudes are presented as ratios in order to provide a comparison of results. Figure 3 shows plots of the results for SSA sandstones in the form of an equal-angle stereonet (lowerhemisphere projection). Trends of the stresses determined from the oriented cores of the SSA specimens are given in degrees measured clockwise from North, whereas those obtained from the two unoriented SSB sandstones are simply measured clockwise from an arbitrary reference line. Consequently, the results for SSB samples are not plotted on stereonet projection, since no common reference line exists among the specimens tested, and such plots could imply misleading relations. The results shown in Table 2 and Fig. 3 show that the major principal stress (o.~) lies near vertical, to within 0 ~ to 23 ~ This result can be regarded as sufficiently precise for a technique based on stress relief microcracks, since it is common that the orientations of the microcracks are modified by grain scale inhomogeneities. The similarity in magnitudes between major (o1) and vertical (i.e. overburden) stresses (o'v), represented by the ratio o'1/o'v being near to unity, also indicates that o'1 lies in the vertical

IN SITU STRESS PREDICTION

North f 1

2

6 b

7%o8~3 2 1

3 0(~. 2 1,2,3 ... sample number

A~ 3

Fig. 3. Lower hemispherical projection showing in sltu stress orientations from DSA tests on sandstone SSA.

direction. It can also be concluded from the results for SSA sandstone that the depths from which the core samples were recovered are not far above the depth at which o.H becomes equal to o-1, as indicated by the closeness of the ratios o.2/o.1 and o.3/0-~ to unity. This interpretation is in accordance with global data for the vertical horizontal stress ratio versus depth compiled by Brown & Hoek (1978). In the case of the intermediate and the minor stresses, 0-2 and 0-3, it is evident that these lie in a horizontal plane, or at least sub-parallel to horizontal. However, it is obvious from the scatter observed that the orientations of the two horizontal stresses are interchangeable. This is understandable, since the two stresses are very close in magnitude, and each has caused a similar degree of oriented microcracking in the samples. If these ratios (0"3/0" 2 column in Table 2) are averaged, a minor-intermediate stress ratio of 0.95 (with the exclusion of SSA-4, which is significantly different from the others) is obtained. Thus the difference of stress relief microcrack density in the two principal directions is minimum and, taking into account experimental and analytical error (e.g. determining ranges for slopes of the pressure-strain curves), the implied stresses could be interchanged in direction and magnitude. At this point, it is worth noting that, with a mere 5% error bar to represent the errors, both trends and magnitudes of the two horizontal stresses are interchangeable. In fact statistical analysis of each over-determined DSA dataset (which was necessary to obtain the best fit tensor of crack closure strains) yielded a maximum error of

189

between approximately 2% and 7% with regard to the principal stress ratios from any single DSA tests, and an error 'cone' of approximately 5~ to 12~ for the principal stress orientations from any single test. From the hemispherical plot of all the oriented DSA data (Fig. 3) it can be seen that the combined results yield a scatter of approximately 30 ~ in the azimuths of the horizontal (i.e. intermediate and minor principal) stresses. However, this does not imply the statistical analyses of the DSA results could just have easily yielded horizontal stress orientations in any azimuths from 0~ ~ since this would have necessitated the microcrack densities and stress magnitudes being isotropic in the horizontal plane. Whilst this is possible, it is only so for those relatively few cases where the magnitudes of the minor and intermediate stresses are exactly the same. For all other combinations within the limits or error, the general directions of the principal stress orientations are as seen in Fig. 3 and only the magnitudes vary. Thus, although the intermediate and minor stresses are very close in magnitude, the DSA method has still been able to identify the general orientations of the stresses. From the hemispherical projection, it is clear that one of the two horizontal stresses lies within the range 290 ~ to 350 ~ from North, with the scatter in azimuth due probably to variation in the orientation of the stress-relief microcracks caused by grain-scale heterogeneities. On the other hand, the other horizontal stress lies within the range 20 ~ to 71 ~ from North such that, with the two horizontal stresses being similar in magnitude, the intermediate principal stress (o.H) can lie in either of the ranges 290 ~ to 350~ or 20 ~ to 71~ Arguably, therefore, o.H can lie in either of the ranges 310+10 ~ N (or 130+10~ or 220+10~ (or 40+10~ These results are in reasonably good agreement with results of an earlier study reported by Desroches et al. (1995), who conducted analyses of the in situ state of stress in this area using DSA on samples from the very same core sections as tested in this study, as well as using hydraulic fracturing and sleeve fracturing in downhole tests in the same well and at the same depths. The earlier combination of the results from these three techniques indicated o.h to lie in the range N65~ ~ Note also that this earlier study showed that stress data from DSA analyses on these core sections were not influenced by slight anisotropy in the samples nor by variations in the elastic properties or rock strengths over the cored intervals. The in situ stress prediction from the two SSB

190

B. WIDARSONO ET AL.

136oN

166oN

196~

226oN

256oN

286oN

316oN t

60

63

66

69

72

75

Transit time (pSec)

60

63

66

69

72

Transit time (pSec)

Fig. 4. S-wave waveforms for sample SSA-1 at various rotation angles. (Propagation in vertical Z-axis, and polarized in Y-axis direction at X-axis reference line. Arrows indicate detected first peak).

Fig. 5. S-wave waveforms for sample SSA-I at various rotation angles. (Propagation in horizontal X-axis, and polarized in Y-axis direction at X-axis reference line. Arrows indicate detected first peak).

sandstone specimens is similar to the results for SSA, even though the true orientations cannot be determined due to the unoriented nature of the core. Since the core is from vertical boreholes, it can be inferred from Table 2 that the major principal stress lies near-vertical; consequently the other two principal stresses lie nearhorizontal.

in other directions. Although this behaviour is exhibited by all specimens tested in this study, there is one case (SSA-8) where signal attenuation was so severe that it proved impossible to pick the first arrival time. The degree of attenuation, nevertheless, varies from one specimen to another in a manner related to the state of cementation of the specimens (Table 1). For the purpose of predicting in situ stress orientations, transit time values were identified and selected from the waveforms. Since the Swave signals observed during the experiment vary in quality due to different degrees of attenuation, it was found that the S-wave velocity calculated from the first peak (or trough) is more reliable for interpretation than the group velocity calculated from the first arrival. Figure 6 shows examples of the variation in first peak velocity with rotation angle as seen for sample SSA-1. The velocity plots are shown with an error bar of +0.5 %, estimated as representing the confidence in picking transit time values (+0.125 #s) from waveforms due to variations in signal quality and oscilloscope resolution. In general, the velocities plotted against rotational

S h e a r w a v e splitting

During each test, a set of waveforms was recorded for each 15~ rotation relative to the reference line (X-axis for the vertical, and Z-axis for the horizontal wave propagations). Figures 4 and 5 illustrate examples of waveforms recorded during measurements on SSA-1 in the vertical (Z-axis) and horizontal (X-axis) directions. As expected, the plots show that the transittime (At) varies with rotation angle. This variation in At can be considered as an indication of the existence of an oriented set of cracks, or at least (provided that a homogeneous background pore system exists) is more influential in reducing S-wave velocity than any other sets of microcracks with lower density oriented

IN SITU STRESS PREDICTION

S-wave [first peak] velocity (m/s) 2000 a) Z-direction propagation Y-axis polarization

191

minimum velocity direction. For horizontal propagation (X-axis), the vertical (or sub-parallel) in situ stress (o-v) coincides with the direction of maximum velocity, as in the case of 0-H for vertical propagation. Both 0-H and 0"h are orthogonal to O-v.

19oo

Table 3. in situ stress orientations from USWS tests

1-_+0.5%

18oo

1~o

'

18o

2~o

'

'

24o

Sample 270

300

crI inclination O"H azimuth (~ vertical) (o)l

~h azimuth (o)l

o from North

S-wave [first peak]

velocity (m/s) 2100 b) X-direction propagation Y-axis polarization

2000

9

1•

19oo

t

9

~b

I

6;

i

9'o 90

I

120 o from vertical

150

180

Fig. 6. Variation of S-wave first peak velocity with rotation angle for sample SSA-1. (a) Vertical Z-axis propagation, polarized in Y-axis direction; (b) horizontal X-axis propagation, polarised in Y-axis direction.

angle result in a sinusoidal pattern, indicating the presence of azimuthal velocity anisotropy over 90 ~ As shown in Fig. 6, similar behaviour is also observed in both the vertical and horizontal directions, indicating the presence of aligned stress relief microcracks of different densities as the source of anisotropy. Generally, for SSA sandstone specimens, S-wave velocity splitting of 1 to 3.5% and 3 to 13.5% (relative to the highest velocity) for vertical and horizontal propagation, respectively, are observed. For SSB samples, SSB-1 shows 2.2% splitting, whereas SSB-2 shows 1.1% and 1.6% velocity splitting, respectively in the vertical and horizontal propagation direction. Accepting the hypothesis of a relation between S-wave splitting and in situ stress relaxation and orientation of stress relief microcracks, velocity plots such as in Fig. 6 indicate the orientations of the in situ stresses. From the velocity profiles, for S-waves propagating in the Z-axis direction, the major horizontal stress (O-H) lies in the direction at which maximum velocity occurs, whereas the minor horizontal stress (o-h) lies at right angles to it, as indicated by the

SSA-1 SSA-2 SSA-3 SSA-4 SSA-5 SSA-6 SSA-7 SSA-8 SSB-1 SSB-2

0 75 2 0 0 0 0 0

7 6 ( 2 5 6 ) N 166(346)N 5 3 ( 2 3 3 ) N 143(323)N 83(263)N 197(17)N 120(300)N 343(163)N 106(286)N 105(=285) 75( = 255)

173(353)N 107(287)N 210(30)N 253(73)N 196(16)N 15(--195) 345(= 165)

1Azimuths measured clockwise with respect to North (Z-axis) or, for unoriented cores (SSB), clockwise from reference line (X-axis). 2 = Poor acoustic coupling due to poor cementation of the rock. Table 3 summarizes the results of the S-wave splitting measurements. Note that USWS measurements on SSA-3 and SSA-8 (Z-axis propagation) were not carried out, due to poor transducer-sample acoustic coupling. The results show that one of the principal in situ stresses lies in the vertical direction, even though accuracy is limited by the 15 rotational sampling. The vertical stress lies within 7.5 of the peak of the velocity profiles shown in the plots (except for sample SSA-2). The same resolution limit applies to 0-H. Furthermore, from comparisons between the horizontal and vertical velocity plots, it is apparent that 0-v is the major principal stress, o1, as indicated by relatively larger velocity anisotropy for horizontal than for vertical propagation. To illustrate this, 1 to 3.5% velocity variation exists over 0~ ~ relative to the highest velocity for vertical propagation, in comparison to 3 to 13.5% for horizontal propagation in the SSA sandstone. Consequently, 0-2 and 0-3 lie in the horizontal plane. As seen from DSA, the trends of the horizontal in situ stresses are more easily identified if illustrated on stereonet projections. Figure 7 illustrates the orientations of the major horizontal in situ stress axes for the SSA sandstones plotted. As with the DSA stereonet projections, the orientations are relative to

192

B. WIDARSONO ET AL. Reference line ~ / 7 7

f

1

/ e C~,H 1,2,3 ... sample number

Fig. 7. Lower hemispherical projection showing rh orientations from DSA tests on sandstone SSA.

North. The projection in Fig. 7 and the corresponding DSA results (Fig. 3), show that the major horizontal stress can lie either in the N W - S E or N E - S W directions. At this stage, therefore, no definitive conclusion can be drawn regarding the orientation of 0.iJ.

Comparison of the two techniques Orientation o f in situ stresses

In comparing the results from the DSA and the S-wave splitting, only the orientations of the stresses can be considered. No comparison can be made of stress magnitudes, since S-wave splitting tests do not provide stress magnitudes, even though a limited qualitative assessment can be made using velocity anisotropies. A summary of results of the two techniques are listed in Table 4. In the table, only the results for aH and ah from acoustic measurements and 0.2 and 0.3 from DSA are compared, since almost all analyses of S-wave splitting tests have shown that the principal stress 0.1 is essentially vertical (i.e. they show a minimum velocity at 90 to the vertical). In general, the results for SSA sandstone have shown that there is a reasonable agreement between the two techniques, although some inconsistencies appear when individual results are studied. It is clear that only SSA-I and SSA7 exhibit total agreement between trends for ei-i (USWS) and o2 (DSA), which is by definition correct for this case (i.e. intermediate principal stress is the major horizontal stress). The rest of the samples have, in contrast, tended to show

agreement between trends for 0.I-1 (USWS) and 0.3 (DSA), which is incorrect by definition, since 0.3 is the minor principal stress, and for this case it should coincide with the minor horizontal stress (0.h). This behaviour can be explained by the ratio 0.3/0.2 approaching unity (approximately 0.95), such that differences in microcracking in the two principal directions are minimal. Nevertheless, as indicated by the stereonet projection in Fig. 7, there is also no clear consistency in 0.n orientations, suggesting no clear consistency in the orientation of the vertical microcracks. This appears to be due to the effect of grain scale heterogeneities, over which the far-field horizontal in situ stresses are not large enough to maintain sufficiently large and uniform tensile forces across the rocks at the granular scale. Grain scale heterogeneities certainly appear to have their effects in 0.1 prediction. The DSA results for SSA sandstone in Table 4 show that the orientation of 0.1 varies within a range 00-23 ~ from the vertical, whereas the USWS results show almost all the 0.1 to be vertical (Table 3). Undoubtedly, rotational sampling of 15~ reduces the accuracy of stress orientation, and it is likely that grain scale heterogeneities (reflected in local preferential orientation of the microcracks) contribute significantly to any disagreements between the two techniques. Such heterogeneities certainly have greater effects on DSA measurements, since the portions of the specimen measured in DSA are limited to the surface areas underneath the strain gauges. This is much less representative than the rock mass tested by the USWS technique. The results from S-wave splitting are probably more reliable in this particular case. The results for SSB sandstone in Table 4 show a reasonable agreement between the two techniques, even though the results for SSB-2 exhibits about 30 ~ difference between o-2 and 0.H, and between 0.3 and 0.h- Although there is no evidence, this disagreement is probably caused by other sources of acoustic velocity anisotropy such as preferential alignment of sandstone grains. The differences in the fundamental concepts of the two techniques provide, nevertheless, advantages and disadvantages for both techniques. In S-wave splitting, any acoustic propagation is influenced by the 'averaged' properties of the whole specimen volume, and therefore representative of the whole specimen. In contrast, any microcrack strain measured in a DSA test represents only the areas covered by the strain gauges, which are generally small compared to the overall specimen dimensions.

IN SITU STRESS PREDICTION

193

Table 4. Comparison of/n situ stress orientations from DSA and USWS DSA

0-1

S-Wave Splitting

O'H

Oh

Sample

Azimuth

Dip

Azimuth

Dip

Azimuth

Dip

Azimuth

Azimuth

SSA-1 SSA-2 SSA-3 SSA-4 SSA-5 SSA-6 SSA-7 SSA-8 SSB-1 SSB-2

204 106 116 74 38 331 229 164 240 179

80 73 76 59 87 66 86 81 83 74

71 308 213 351 293 52 326 20 132 88

7 17 2 4 2 2 0 7 2 1

340 219 304 262 203 140 57 290 42 358

7 1 14 26 3 23 3 6 7 16

76(= 256) 53(-- 233) 2 83(= 263) 197(= 17) 120 = 300) 343 (= 163) 106(=286) 105(= 285) 75(=255)

166(= 346) 143(= 323) 2 173(= 353) 107(= 287) 210( = 30) 253(= 73) 196(= 16) 15(= 195) 345( = 165)

(o)l

~ (o)

(o)1

~ (o)

(o)1

(o)

(o)1

(o)1

1Azimuths measured clockwise with respect to North (Z-axis) or, for unoriented cores (SSB), clockwise from reference line (X-axis). 2 = Poor acoustic coupling due to poor cementation of the rock.

However, in the presence of large discontinuities, the reverse is true. Large discontinuities in specimens influence acoustic wave propagation, whereas any non stress relief behaviour in the DSA tests can usually be avoided by strain gauge emplacement. Consequently, DSA can produce more reliable results in this case. The DSA results in Table 2 show that the principal stresses do not lie in exactly horizontal or vertical directions. In other words, the induced cracks in most cases are not exactly parallel or perpendicular to the vertical axis in situ. However, the reasonably good agreement between the two techniques has shown that acoustic velocity anisotropy can still be observed even though the microcracks dip from these directions, which is more often than not likely to be the case. This fact is very important in any effort to establish USWS as an alternative technique for in situ stress determination, bearing in mind that it is most likely that grain scale heterogeneities can cause local deviations in microcrack orientations.

Configuration system

o f stress relief microcrack

In DSA the principal strains are determined from analysis of the microcrack closure strain tensor obtained from a test. The principal strains obtained provide the orientations of the stress relief microcracks, since it is assumed that the greatest strain occurs in the direction normal to the plane of the microcracks. In other words, it is assumed that it is represented by three

dominant and mutual perpendicular microcracks sets. Although this assumption is logical, as demonstrated by Charles et al. (1986), DSA does not provide a direct illustration of the microcrack system. Direct observation such as scanning electron microscope (SEM) of oriented samples used in conjunction with DSA can, however, provide an insight into microcrack orientations and distributions. The results of S-wave splitting measurements can provide, to some extent, additional information on microcrack configurations. Velocity plots, such as shown in Fig. 6, have demonstrated that velocity anisotropy can occur between measurements in the vertical and horizontal directions. There are several factors that can lead to such acoustic anisotropy, and to S-wave splitting in particular, but it is generally accepted that aligned cracks are the major cause of S-wave splitting (Crampin & Lovell 1991). At the small scale, such as in USWS tests, there is always a possibility that other sources of anisotropy, such as lamination and crystal alignment, can contribute to the overall anisotropy. As shown in Table 1, SSA sandstone samples exhibit signs of bedding (between 4 ~ and 22 ~ from the horizontal), even though not all samples were found to exhibit dominant bedding. However, in this study, there is evidence that the presence of bedding planes has not contributed to the velocity anisotropy for the Xaxis (horizontal) propagation direction. For example, the results for SSA-5 (Table 3) show that, despite the 18.5 ~ dip in bedding (Table 1), the USWS measurements indicate the presence of a set of horizontal microcracks as indicated

194

B. WIDARSONO E T AL.

by 0 ~ in the O 1 column. Another example is seen with SSA-2, for which the bedding dipped at 22 ~ from the horizontal and where measurements in the horizontal direction indicated the minimum velocity to be reached at a rotation angle of 165~ (i.e. 15~ from the vertical, implying Crl acts at 75 ~ from the vertical. Clearly this is incorrect if one is to take the results of the DSA to be reliable (Table 3); the real cause of the anisotropy is unclear, although it may be caused by strong crystal alignment. The evidence of results for SSA-2 and SSA-5 have shown that bedding planes do not, in these cases, strongly influence velocity anisotropy for the horizontal direction, and hence do not confuse the subsequent interpretation of USWS measurements. The results obtained from the S-wave splitting tests do, however, tend to support the existence of aligned horizontal microcracks. In the vertical direction, results of the acoustic test have shown that it is most likely that vertical aligned microcracks cause S-wave velocity splitting, since no other apparent causes are observed. The question arises whether the suggested presence of two sets of mutually perpendicular vertically aligned microcracks, as implied by DSA, can be justified. For this, the results of Swave splitting cannot be used, since they do not show the existence of the second (i.e. the less dense) vertical set of microcracks (if it does exist, its existence is probably 'overlooked' by the transmitted acoustic energy and treated as merely a background for the first and more dense vertical microcrack set). Charles et al. (1986) outline theories of brittle fracture mechanics which explain crack opening under tensile forces (as might occur in the case of stress relaxation). If the existence of two sets of microcracks (one vertical and the other horizontal) whose generation is related to principal in situ stress relaxation can be proven experimentally, it is likely that a third set of microcracks (i.e. the second and less dense set of vertical microcracks) also exists, since the processes leading to it are essentially the same as those causing microcracks in the other two principal directions. Comparing results for the two techniques has demonstrated directly that three sets of mutually perpendicular microcracks exist in a rock material experiencing a process of relaxation from three in situ principal stresses, provided the relaxation forces are sufficient to generate them.

Conclusions A series of investigations of in situ state of stress using DSA and USWS has been conducted

successfully. In general, individual results of the two techniques are found to be in agreement, taking into consideration the facts regarding the local in situ state of stress field. The DSA and USWS results are also found to be in reasonable agreement with results from hydraulic fracturing and sleeve fracturing. Grain scale heterogeneities strongly influence the deviation of stress relief microcracks, as reflected by the scatter in horizontal in situ stress orientations shown by both techniques. Results for SSA sandstone samples have shown that, in areas with small differences in principal stresses, a greater number of samples will be required to overcome the influence of grain scale heterogeneities. Comparisons between individual results obtained from the two techniques have shown that S-wave splitting is a reliable one for determining orientations of in situ stresses by virtue of the existence of stress relief microcracks. The study has also shown that S-wave splitting analysis can be used independently with reliable results. This confidence, together with simplicity in sample preparation and speed in conducting tests, can be considered as the major advantage of this technique compared to other techniques based on stress relief microcracks such as DSA, differential wave velocity analysis, anelastic strain recovery or differential thermal analysis. Despite the indicated advantages, the study has also revealed some disadvantages in the acoustic technique. Its major limitation is its inability to provide estimates of in situ stress magnitude. Another less important disadvantage is the necessity to perform the test only in vertical and horizontal directions without compromising the simplicity in sample preparation and analysis of results. This requires that the principal in situ stresses always lie in vertical and horizontal planes, which is not necessarily true in all cases. Theoretically, however, this disadvantage can be reduced by reducing the sampling rotational interval, hence enabling more careful examination of the alignments of sets of microcracks. The fact that stress relief microcracks are not the only source of S-wave splitting is another disadvantage. However, wellprepared samples can minimize this significantly. When the two techniques are employed together, DSA provides information on in situ stress orientations and magnitudes, whereas Swave splitting provides confirmation of the orientations bearing in mind that the USWS 'sees' a larger volume of the sample than DSA. Combined utilization of the two techniques has also shown the potential for examining rock microfeatures, such as microcrack systems present in the rocks.

IN SITU STRESS PREDICTION We would like to thank British Gas and Chevron for their support in supplying core samples, and the British Geological Survey and PPPTMGB 'Lemigas' Indonesia for partially funding the research. We also wish to thank J. W. Dennis for his support during the project.

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DYKE, C. G. 1988. In-situ stress indicators for rock at great depth. PhD Thesis, Imperial College of Science and Technology, University of London. GARBIN, H. D. & KNOPOFF, L. 1975. The shear modulus of a material permeated by a random distribution of free circular cracks. Quarterly Applied Mathematics, 33, 296-300. KING, M. S., CHAUDHRY, N. A. & SHAKEEL, A. 1995. Experimental ultrasonic velocities and permeability for sandstones with aligned cracks. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 32, 155163. KOWALLIS, B. J. & WANG, H. F. 1983. Microcrack study of granite cores from Illinois deep borehole UPH3. Journal of Geophysical Research, 88, 73737380. OIKAWA, Y., MATSUNAGA, I. & YAMAGUCHI, T. 1993. Differential strain curve analysis to estimate the stress state of the Hijiori hot dry rock field. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 30, 1023-1026. STRICKLAND, F. G. & REN, N. K. 1980. Use of differential strain curve analysis in predicting insitu stress state for deep wells. Proceedings 21st US Symposium on Rock Mechanics, Rolla, Missouri, 523-533. TEUFEL, L. W. 1983. Determination of the principal horizontal in-situ stress directions from anelastic strain recovery measurements of oriented core from deep wells: application to the Cotton Valley formation of East Texas. In." NEMAT-NASSER, S. (ed.) Geomechanics, American Society of Mechanical Engineers, New York, 55-63. YALE, D. P. & SPRUNT, E. S. 1989. Prediction of fracture direction using shear acoustic anisotropy. The Log Analyst, 30, 65-70.

Dolomite cement distribution in a sandstone from core and wireline data: the Triassic fluvial Chaunoy Formation, Paris Basin R. H. W O R D E N

School of Geosciences, The Queen's University, Belfast, BT7 INN, UK

Abstract:The distribution of mineral cements in oil fields is critical to the spatial variation of porosity and permeability. The distribution of dolomite cement within fluvial Triassic Chaunoy sandstones in the Paris Basin was studied using core description, petrography, core analysis (porosity and permeability), wireline data interpreted to give mineralogy, porosity and permeability data and geochemical data. Petrographic analysis revealed that dolomite and quartz cements are the main diagenetic minerals. Using sonic transit time, density and neutron porosity log, the overall proportions of quartz, dolomite and shale as well as porosity for each depth interval could be resolved. Petrographic and core analysis data showed that permeability could be calculated from wireline-derived porosity and mineralogy data. There is excellent correlation between core analysis porosity and permeability and their wireline-derived equivalents. There is also excellent correlation between wireline-derived mineralogy data and quantitative petrographic mineralogy data. The wireline-derived mineralogy data show that dolomite is preferentially concentrated at the tops of most sand bodies. Porosity and permeability are consequently lowest at the tops of individual sand bodies due to the localized dolomite cement. There are a number of potential causes for this distribution pattern although geochemical and petrographic data showed that a combination of early pedogenetic dolomite cementation and later recrystallization, possibly due to an influx of organically-derived CO2, are most likely.

Knowledge of the way in which porosity and permeability are distributed throughout an oil field is an important building block in a reservoir model. The key factors which control porosity and permeability in sandstones are depositional characteristics such as grain size and sorting and diagenetic features such as cements and secondary porosity. Most reservoir simulation models incorporate sub-units of common primary sedimentary origin. The distribution of reservoir quality is thus usually defined in terms of the morphology of the sedimentary architecture. However, reservoir rocks seldom retain their depositional porosity. Instead, porosity is usually degraded by a variety of diagenetic processes. The effects of these processes are not necessarily confined to the boundaries of depositional sedimentary units. Common diagenetic processes may commonly either transcend sedimentary architecture or may lead to the subdivision of self-contained sedimentary units in terms of porosity and permeability. There is no framework for predicting diagenetic cement distribution in sandstones on the reservoir scale. It is not yet generally possible to predict or model reservoir porosity and permeability variations over the distribution of the primary sedimentary units due to the impact of diagenesis. This is clearly unsatisfactory and may lead to systematically incorrect reservoir models.

One of the key problems involved in describing the distribution of diagenetic cement is the cost (in terms of time and money) of acquiring the data. Petrographic data are usually collected at a far lower density than core analysis data (if at all), are harder to quality-control and are highly operator-dependent. In this paper, the method of assessing carbonate cement distribution in sandstones using petrophysical logs (hereafter known as wireline logs) will be described. This method was used to describe the distribution of dolomite cement in Triassic fluvial clastic sediments of the Chaunoy Formation in the Paris Basin, France. The controls on dolomite cement distribution will be discussed, the effects of dolomite cement (and by inference, quartz cement) on reservoir flow properties, defined, and possible mechanisms that controlled the carbonate cement distribution pattern investigated.

Geological setting The Paris Basin is an intracratonic basin with an aerial extent of approximately 6000km 2 and about 3000m of present day sediment infill deposited on Hercynian basement (Fig. 1; Pommerol 1974, 1978). There are two main permeable, petroleum-bearing reservoir units in the central part of the Mesozoic of the Paris

WORDEN,R. H. 1998. Dolomite cement distribution in a sandstone from core and wireline data: the Triassic fluvial Chaunoy Formation, Paris Basin. In. HARVEY,P. K. & LOVELL,M. A. (eds)

Core-Log Integration, Geological Society, London, Special Publications, 136, 197-2t 1

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Fig. 1. Geological map of the Paris Basin with the approximate extent of the Triassic sandstones. The well under investigation (L) is marked. Basin; the Late Triassic (Keuper) fluvial sandstones and the Middle Jurassic marine carbonates (Pages 1987). The Paris Basin experienced a simple subsidence history that included periods of relatively rapid burial. Rifting started in the Permo-Triassic followed by thermal subsidence in the Jurassic and Cretaceous (Pommerol 1978; M6gnien 1980a,b; Brunet & Le Pichon 1982; Loup & Wildi 1994). Maximum burial in the central part of the basin occurred during the Oligocene-Miocene and was followed by minor uplift during and following Alpine and Pyrenean tectonism (M6gnien 1980a,b; Brunet & Le Pichon 1982; Pages 1987). Triassic sediments in the central part of the basin reached maximum burial depths of about 3000-4000 m. The Chaunoy Formation in the well examined is presently buried to between 2200m and 2500m. Sandwiched between the Triassic sandstones and the Mid Jurassic carbonates are organic-rich Liassic shales. They are mature to the point of oil generation and expulsion at the base of the Lias, in the centre of the basin (Herron & Le Tendre 1990). This source rock reached maturity at the time of maximum burial and charged both Triassic and Mid Jurassic reservoirs with oil (Poulet & Espitalie 1987). The Triassic sandstones are composed of several reservoir units. The Late Carnian to Norian Chaunoy Formation has limited aerial extent, lies in the deepest part of the basin, slightly to the West of the basin centre and has no outcrop (Fig. 1; Bourquin & Guillocheau 1993; Bourquin et al. 1993; Fontes & Matray 1993. Matray et al. 1993). The Chaunoy was deposited as a minor transgressive-regressive cycle within an overall transgressive phase that ended with Rhaetic marine sediments (Bourquin & Guillocheau 1993). The Chaunoy Formation

is composed of alluvial fan conglomerates, coarse-grained channel-fill fluvial sandstones and flood-basin siltstones. It was deposited in an arid environment as an alluvial and fluvial fringe to the western rifted margin of the basin (Bourquin & Guillocheau 1993; Bourquin et al. 1993). Locally important pedogenic and phreatic dolomite cements are found within the Chaunoy Formation (Sp6tl & Wright 1992). Burial diagenesis resulted in precipitation of abundant quartz and dolomite, and less common calcite and saddle dolomite cement (Demars & Pagel 1994; Worden & Matray 1995). Previous diagenetic studies of the Chaunoy Formation showed that quartz cement grew at temperatures a little lower than those attained at maximum burial, whilst sparry, rhombic ferroan dolomite cement grew at maximum burial (Demars & Pagel 1994). The pedogenic dolocrete has a limited range of 613C values ( - 7 to 0%o; Sp6tl & Wright 1992).

Methods Core description and petrography Slabbed core from the well was examined for general lithology, facies variations, sedimentary structures and grain size. Grain size of the core was measured at regular intervals by comparing core to standard grain size charts under a binocular microscope. Petrographic analysis was performed on 22 thin sections stained for carbonates and feldspars and impregnated with blue-dyed epoxy resin. Grain size and sorting class were assessed quantitatively in thin section by measuring sizes of one hundred grains per section. Detrital grains, cements and porosity were quantified by point counting using three hundred grain counts per section.

Petrophysical (wireline and core analysis) data Porosity and permeability core analysis data for the sampled well were made available to the authors by Elf (99 data points from the interval under investigation). Core porosity data have an uncertainty of somewhat less than 0.5% that arises due to the variable amount of stress relaxation following withdrawal of the core from the subsurface. Analytical errors are insignificant. Sonic transit time, neutron porosity, density and other wireline data, recorded at 5 cm intervals by petrophysical logging methods, were also made available by Elf. These data have been used to derive porosity and mineral

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Table 1. Definition of terms and units used in equations 1 to 7 Term Definition At Atminx

At e P Pminx

p4

~bn (~nminx

4~n, minX pi ps qtz%

sonic transit time recorded by log (#see fl-1) sonic transit time of mineral X (#sec fl-l) sonic transit time of fluid in pore space (#sec fl 1) density recorded by log (g cm-3) density of mineral X (gcm -3) density of fluid in pore space (g cm-3) neutron porosity recorded by log (porosity units) neutron porosity of mineral X (porosity units) neutron porosity of fluid in pore space (porosity units) proportion of mineral X (as fraction of total rock volume) porosity (as fraction of total rock volume) permeability intercept of a regression line on a porosity--permability cross plot slope of a regression line on a porosity-permability cross plot percentage of quartz in a depth interval derived from wireline logs

proportions using methods outlined by Hearst & Nelson (1985) and Doveton (1994). The gamma log is commonly used to define the 'shaliness' (where shale in this context routinely describes the overall clay mineral content of the rock and is not a reference to the grain size or texture of the sedimentary rock) of sandstones although this approach is invalid for simple crystal chemical reasons. Composite gamma logs record the total potassium, thorium and uranium contents of the rock by detecting the -,/-rays associated with decay of the radioactive isotopes of these elements and their daughter products. Spectral gamma logs differentiate the v-radiation from the three elements. However, using any of the gamma logs for a shale or total clay mineral estimate is invalid. Potassium is commonly held in the minerals: Kfeldspar, illite and the mica group of minerals. Most clay minerals apart from illite do not contain potassium. Thus the potassium gamma signal records the relative abundance of Kfeldspar, illite and mica indiscriminately and does not record the shale or clay mineral content. The thorium gamma signal, often mistakenly thought to reflect specific clay minerals, records the abundance of thoriumbearing trace minerals and cannot be used to estimate volumes of clay minerals (Hurst & Milodowski 1996). Consequently, a multiple logtransformation approach was used to derive the shale content (as well as the dolomite and quartz contents) and gamma ray logs have not been used to derive the shale content. The signals from the sonic transit time (At), neutron porosity (On) and density logs (p) can be integrated and resolved for three mineral types and total porosity using three algorithms relating each separate log signal at any given

depth to solid grain volume (occupied by the three minerals) and the assumption that the sum of the three minerals fractions plus porosity equals unity. This also assumes linear relationships between mineral proportions and their contribution to the petrophysical signal. Thus, with four equations and four unknowns (proportions of three minerals plus porosity), the following algorithms can be solved simultaneously at each depth interval: At = mini. Atminl q- min2.Atmin2 + min3.Atmin3 + &to

(1)

p = minl .Pminl + min2.Pmin2 + min3.Pmin3 + CrO(2)

On = min 1. Onmi n 1 ~- min2. Onmin2 -4min3.Onmin3 + Ono

(3)

1 + min 1 + min2 + min3 + O.

(4)

The terms used in the equations above are defined in Table 1. Petrophysical responses of each mineral were taken from Rider (1986). Geochemical data

Fluid inclusion thermometry was performed using a Linkam THM600 heating-cooling stage with 0.1~ precision. Phase transition temperatures were determined by temperature cycling; heating experiments were conducted before freezing to prevent inclusion deformation by ice growth that would effect homogenization temperatures. Fluid inclusion homogenization temperatures were collected from quartz and

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Results Core description and p e t r o g r a p h y

Grain size data are displayed in Fig. 2. Most of the core is either fine-grained (silt/mud, grain size < 62 #m) or coarse-grained (coarse sand to conglomerate, i.e. grain size > 1000 #m). Fine-grained core is mottled in appearance, very well lithified and shows abundant evidence of pedogenesis with rootlet structures, rhizoeretions and nodules (see, for example, Sp6tl & Wright 1992). Petrographic analysis showed that fine grained units are highly dolomitic with a substantial clay mineral component. The dolomite is finely crystalline non-ferroan dolomite. The coarse grained sandbodies are composed of massive, largely structureless sediments. Petrographic analysis showed that the sandstones are sub-lithic to sub-arkosic (according to Folk 1974) with a significant volume of polycrystalline quartz grains ( 1 0 ~ 0 % of quartzose grains). The feldspar population is approximately equally split between plagioclase and Kfeldspar. An average sandstone composition is given in Table 2. Table 2. Average petrographic data from the Chaunoy Formation sand bodies. Twenty two samples were examined petrographically. The figures illustrate the importance of dolomite in the Chaunoy Formation Grain/cement type Fig. 2. Core description and petrographic data. Grain size is shown as a continuous log. The petrographic data are represented by bars at the appropriate depths with mineralogy represented (see key). Core analysis data are also displayed on this diagram. There are 99 porosity and permeability data points.

ferroan (rhombic) dolomite cements. Fluid inclusions could not be examined in the microcrystalline dolomite because of the limited resolution of the microscope. Six core samples from this well were examined by this technique giving more than one hundred data points. For carbon isotope analysis, N d - Y A G laser sampling was used following Smalley et al. (1992). This has a spatial resolution of about 50#m (ablation pit diameter) with analytical precision of +0.1%o for ~513C. Samples for laser ablation were plasma-ashed to remove any organic material (oil) in the pore system. The laser could be used to sample individual dolomite crystals within pores, without fear of contamination from any other crystals. Eight core samples were examined by this technique giving more than fifty data points.

Polycrystalline quartz Monocrystalline quartz (Total detrital quartz K-feldspar Plagioclase Quartzose lithic fragments Detrital mica Detrital clay Kaolinite Illite Chlorite Authigenic K-feldspar Authigenic Quartz Calcite cement Dolomite cement

Mean % 21.1 13.0 34.1 8.9 4.6 13.2 0.8 3.2 2.5 0.4 0.5 1.1 11.6 1.4 16.4

Standard deviation % 8.6 8.2 6.6) 4.1 3.5 8.8 1.1 5.4 4.2 1.1 0.9 1.4 8.5 5.9 22.0

Sandbodies contain two distinct dolomite morphologies. Sandbodies contain rhombic, pore-filling, ferroan dolomite crystals that are generally greater than 200#m in size (Fig. 3b). The rhombic ferroan dolomite is texturally and mineralogically chemically distinct from the dolocrete. The dolomite in the top portions of most sand bodies grades into the overlying silty

DOLOMITE AND CEMENT DISTRIBUTION IN A SANDSTONE

201

Fig. 3. Photomicrographs of (a) microcrystalline non-ferroan dolomite at the very top of a sand body with partial replacement of detrital silicate grains and (b) grain-rimming quartz cement (Q) and pore-filling rhombic ferroan dolomite (DOL) enclosing the quartz cement. Remnant porosity (~) is minor and occupies pore centres. Scale bars are 200 #m.

dolocrete layers and tends to be extremely finely crystalline. The proportion of microcrystalline dolomite increases upwards to the top of sand bodies. A 'floating grain texture' is present at the tops of sand bodies due to mass silicate graindissolution and replacement by microcrystalline dolomite (Fig. 3a). The sandstone also contains localized quartz cement (e.g. minor quartz cement labelled in Fig. 3b). Textural considerations show that the microcrystalline dolomite pre-dated the ferroan rhombic dolomite. To facilitate the subsequent comparison between petrographic data and wireline-derived

mineralogical data, the petrographic data have been coverted into proportions of quartz, dolomite and shale. In this manipulation, quartz is the sum of detrital quartz grains, quartz cement, quartzose lithic fragments and feldspar; dolomite is the sum of all type of dolomite and other carbonate minerals; shale is the sum of clay, micas, and micaceous lithic fragments. There is a broad correlation between grain size and p e t r o g r a p h i c a l l y - d e f i n e d m i n e r a l o g y : coarse- grained intervals are mostly quartz-rich, the finer intervals are relatively shale and dolomite rich (Fig. 2). However, the correlation

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Fig. 4. Porosity-permeability data from sandstones. Data have been subdivided by petrographicallydefined mineral proportions. High quartz content samples are those with greater than 80% quartz; medium quartz content samples have between 60% and 80% quartz; low quartz content samples have less than 60% quartz. Regression lines have been plotted through the core data for the high, medium and low quartz content samples. Note that the slope and the intercept of these regression lines changes systematically as the quartz content changes.

between grain size, mineralogy and reservoir properties is not perfect. Sand bodies can also have high dolomite contents (e.g. 2457-2458 m 2472-0m, 2482.5-2483.5 m etc., on Fig. 2). This pattern shows that dolomite content and grain size together, probably control the reservoir properties of the sandstone. The petrographic data seem to show that dolomite is concentrated in the top portions of the sandstone units (e.g. 2457m and 2472m) although insufficient samples were examined petrographically to prove that this pattern was common and predictable.

Fig. 5. Wireline sonic transit time, density and neutron porosity through the cored portion of the Chaunoy Formation.

is considerable scatter in the data; there is a wide range of permeability for a given porosity. This probably signifies that there is more than one control on the evolution of porosity and permeability.

Core analysis data

Wireline log analysis

Core analysis data are displayed as continuous logs in Fig. 2. Porosity 1.1-19.0%. Permeability varies from < 0.1 mD to > 5000 mD. Porosity and permeability are highest where the rocks are most coarse grained. However, again the correlation is not perfect; the tops of the sandbodies tend to have low porosity and permeability values relative to the middle and lower portions of sand bodies (Fig. 2). Consequently, grain size and facies variations cannot be used in isolation to understand or predict variations in reservoir quality. Core analysis data are also plotted on a conventional log-linear diagram (Fig. 4). There

Wireline log analysis has been used to determine porosity and mineralogy (with three components; quartz, dolomite and shale). These data have then been used to compute permeability. These data will be used subsequently to assess dolomite cement distribution within the reservoir. Sonic transit time, neutron porosity and density log data for the cored interval are presented as functions of depth in Fig. 5. The same data are cross-plotted in Fig. 6 with the positions of the three minerals added. Equations 1-4 can be solved for porosity plus three solid grain components. The logs have been converted

DOLOMITE AND CEMENT DISTRIBUTION IN A SANDSTONE

203

Fig. 6. Cross plots of (a) sonic transit time against density and (b) neutron porosity against density. The positions of the three mineral groups (quartz, dolomite and shale--where shale refers to all clay minerals and not a texture or fabric) used to define the mineralogy of the formation are marked on both plots. The position of the pore fluid is off the scale but the general direction is marked.

Fig. 7. Data quality assurance: (a) comparison of wireline-derived porosity and core analysis-derived porosity. There is a good correlation between the two datasets. The intercept on the x-axis shows that the wireline porosity data are over-estimating porosity by about 0.024 (note that this over-estimate is subsequently accounted for in all the following calculations and plots); (b) comparison of petrographically-determined quartz and wireline- derived quartz; (c) comparison of petrographically-defined dolomite and wireline-derived dolomite. Porosity and mineralogy from core and petrographic sources is well matched by the values defined from the transformed wireline logs.

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R.H. WORDEN

Fig. 8. Combination diagram of grain size data (derived from core description, Fig. 2) and mineral proportions, porosity and permeability (derived from wireline log analysis). There is excellent agreement between quartz proportion and reservoir quality. The agreement of these with grain size is complex. The tops of some sand bodies have a high dolomite content and correspondingly poorer reservoir quality (e.g. 2470-2471 m). Sand bodies are numbered for reference to Figs 2 and 9. Core analysis porosity and permeability data (dashed and faint) have been added to the diagram for comparison with the wireline-derived data.

into fractional porosity, and the fractional quantities of quartz, dolomite and shale. The rock was thus assumed to consist of three minerals; 'quartz' (all silica minerals and feldspar), 'dolomite' (all carbonate minerals) and 'shale' (all clay minerals). Each individual group of minerals 'quartz', 'dolomite' and 'shale') was assumed to have effectively uniform responses to the three wireline logging tools. Petrographic analysis shows that the quartz/feldspar ratio is generally much greater than about 4 (Table 2), suggesting that the assumption about the quartz component is reasonable. Feldspar and quartz have similar wireline responses (at least for sonic, density and neutron porosity logs) so that the arkosic portion of the sandstone is probably adequately accounted for. The lithic portion of the sandstone is probably represented by 'shale' together with quartz. Dolomite dominates the carbonate mineral population within the rock.

Shale represents the sum of all clay minerals in the rock, although preliminary quantitative Xray diffraction (XRD) data show that these are dominated by kaolinite and illite. Water saturation was calculated using resistivity logs, the Archie equation and neutron porosity values (and the ultimate porosity values computed from the simultaneous solution of the neutron porosity, density and sonic transit time logs were iteratively fed back into the Archie equation until convergence was achieved). The average wireline response properties of the mixed wateroil fluid were calculated depending on the specific saturation (Sw) at the depth interval. Fluid type was found to have little effect upon the subsequent calculations. The wireline-derived porosity data compare favourably to core analysis porosity data with a correlation coefficient of 0.74 (Fig. 7). The wireline porosity values slightly over-estimate

DOLOMITE AND CEMENT DISTRIBUTION IN A SANDSTONE the porosity (if the core porosity data are correct). Consequently, wireline-derived porosity data have been corrected for this slight overestimate by subtracting 0.024 from the fractional wireline porosity values to take the intercept through the origin (Fig. 7). The wireline-derived mineralogical data also compare favourably to the quantitative petrographic data, the two having very good correlation coefficients (average correlation coefficient of 0.84; Fig. 7). Thus, despite the paucity of petrographic data, it is possible to derive continuous and credible mineralogical data from wireline data. Porosity and mineral proportion data were smoothed by averaging over a 0.3 m interval to reflect the realistic resolution of the logging tools (Fig. 8; Hearst & Nelson 1985; Doveton 1994). The results of the wireline data transform into mineral proportions and porosity are given in Figs 8 and 9. There are distinct intervals that are enriched in dolomite and others enriched in quartz. The shale fraction tends to be highest in the dolomite zones. However, the tops of sand bodies have high dolomite contents in the absence of shale (e.g. 2470-2472m) without any corresponding change in grain size. This leads to asymmetry in the mineralogy of individual sandstone beds. The summary diagram, Fig. 9, shows that, on average, sand bodies have the most dolomite in the top quarter. The derivation of permeability from porosity is not a simple task. Permeability is, of course, affected by porosity, but it is also controlled by the shape and size of pore throats that connect pores. The degree of connectivity of the total porosity and the dimensions of pore throats are critical to permeability. It is not possible to derive permeability from a simple porosity value with any degree of accuracy using a simple transform in these rocks. However, recent network modelling work by Bryant et al. (1993) and Cade et al. (1994) has shown that permeability may be predicted from porosity if the fundamental controls on porosity evolution are known. The main generic controls on porosity loss are compaction and cementation. Cementation may be subdivided further between grainrimming cements and pore-filling cements. The different cement morphologies have different effects upon permeability for unit porosity loss due to their different effects upon the pore network. Chaunoy sandstones of the same depositional facies are cemented by both quartz and dolomite (Fig. 3b). Quartz cement forms approximately equal thickness overgrowths whilst dolomite cements tend to fill pores (Fig. 3b; Cade et al.

205

Fig. 9. Summary diagram illustrating the non-uniform distribution of dolomite and porosity in the Chaunoy Formation sand bodies. The numbers refer to the sand bodies on Figs 2 and 8. (a) Dolomite is preferentially concentrated in the top quarter of each sand body. (b) Conversely, porosity is concentrated in the middle two quarters of each sand body. 1994). These two different cement morphologies have profoundly different effects upon the pore network. Core analysis data from the Chaunoy Formation were subdivided on the basis of the quartzdolomite ratios using the wireline-derived mineralogy data. Regression analysis (Fig. 4) shows that the quartz-rich (and thus presumably quartz-cemented) samples have shallower porosity-permeability slopes (ps) and higher permeability intercepts (pi) than quartz-poor (and thus presumably dolomite-cemented) samples in accord with the network modelling discussed above. Algorithms were derived for describing the change in both slope and intercept of the porosity-permeability curves as a function of total quartz content: (intercept) pi = 2.777x 104X 10 (4"55xl~

(5)

(slope) ps = 30.75x 10 (-3"077x ~~ xqtz%)

(6)

in which 'qtz%' is the quartz fraction of the rock as defined by wireline analysis. It was thus possible to predict permeability as a function of the wireline-derived porosity and mineralogy using the following algorithm: permeability (mD) = pi x 10(ps•

(7)

The results of these calculations are displayed in Fig. 8. Inspection of Figs 2 and 8 shows that the wireline-derived permeability curve corresponds well to the core analysis data. Geochemical data

Fluid inclusion data are reported in Fig. 10. Rhombic ferroan dolomite grew at a range of

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R.H. WORDEN

Fig. 10. Fluid inclusion homogenization data from quartz and rhombic ferroan dolomite. Whilst there is overlap between the temperatures of growth of dolomite and quartz, these data support the late (maximum burial) growth of rhombic ferroan dolomite cement following quartz cement growth in the Chaunoy Formation.

temperatures with a mean of 119~ This is a considerably higher temperature than the quartz cement (mean 103~ and confirms the textural evidence (Fig. 3b) that ferroan rhombic dolomite grew after quartz. The temperature for dolomite growth corresponds to maximum burial and the time of oil generation from the Liassic source rocks. Stable isotope data from ferroan rhombic dolomite are given in Fig. 11. These data are compared to carbon isotope data from pedogenic dolomite. The ferroan rhombic dolomite has considerably lighter carbon isotopes than the pedogenic dolomite. The ferroan rhombic dolomite has a range of 813C values consistent with input from recrystallzed pedogenic dolomite and input from a source depleted in 13C.

Fig. 11. Carbon isotope data from rhombic ferroan dolomite with data from the pedogenic dolomite (after Sp6tl & Wright 1992; Sp6tl et al. 1993). The rhombic ferroan dolomite represents mixing between the indigenous pedogenic carbonate and a source of carbonate characterized by carbon relatively depleted in 13C. This must indicate a major organic input into the system (probably in the form of either aqueous bicarbonate, or CO2 dissolved in oil or water).

used to define the spatial distribution of dolomite cements in these sandstones. The derivation of porosity, mineralogy and permeability from wireline data has distinct advantages over core analysis and petrographic data. Most importantly, mineralogical data from logs can be derived for uncored intervals. Petrographic data are usually sparse (due to cost and time) and are 'operator'-dependent. In contrast, wireline data are typically available for most wells in a reservoir and are (in principle) operator-independent. Petrographic data are rarely collected in such abundance that cement distribution can be observed within reservoir units, whereas such data can be derived from wireline mineralogical analysis.

Discussion Quantitative mineralogical data have been generated from sonic transit time, density and neutron porosity wireline logs. Gamma logs cannot be used for mineral identification due to the variable site of radiogenic potassium in a variety of minerals and the non-concordance between uranium and thorium and specific minerals (Doveton 1994; Hurst & Milodowski 1996). The Triassic sandstones and mudstones of the Paris Basin have been resolved into quartz, shale and dolomite. Dolomite has a diagenetic (i.e. non-primary) origin. Wireline logs can be

Amount and distribution of dolomite cement in the Chaunoy sandstone Petrographic analysis hinted at the heterogeneous distribution of dolomite cement in the Chaunoy formation sandstones (Fig. 2). Without a major sampling and petrographic analysis programme, it would be difficult to analyse and describe that heterogeneity. The interpreted wireline data have confirmed that dolomite is not homogeneously distributed throughout the Chaunoy Formation sandstone (Figs 8 and 9).

DOLOMITE AND CEMENT DISTRIBUTION IN A SANDSTONE

207

Fig. 12. Theoretical dolomite distributions from four potential controlling processes. The model represents a sand body sandwiched between pedogenic dolocrete layers. (a) Pedogenic dolomite cement; there would be most dolomite at the top of each sand body. (b) Dolomite cemented, sourced from the dolocrete during burial, transported by diffusion; cement should be equally abundant at the tops and bases of sand bodies with a minimum at the centre. (c) Dolomite distribution controlled by high permeability streaks enabling input from external sources; fluvial sandstones usually fine upwards leading to high permeability bases and thus most dolomite at sand body bases. (d) Dolomite distribution controlled by the relative buoyancy of oil (which may have carried dissolved CO2) or a separate CO2 gas phase caused dolomite cementation and thus leading to most dolomite at the tops of sand bodies.

Dolomite in the Chaunoy has either a pedogenic (i.e. very early diagenetic) or burial diagenetic origin (Fig. 12). Detrital dolomite may be discounted as an option because of the relatively soluble nature of dolomite and the perfect crystal forms observed in core and thin section (Fig. 3). Wireline data cannot be used to discriminate between different dolomite grain morphologies (e.g. microcrystalline or coarse rhombic) or between dolomites of different mineral chemistry (e.g. non-ferroan or ferroan dolomite). The wireline data have shown that the tops of most coarse grained sandbodies have the greatest quantity of dolomite (Figs 8 and 9). The dolomite content varies between, as well as within sandbodies. Sand bodies 3, 4, 5, 7, 8 and 9 all have significantly more dolomite in the top quarter of the sand body than in other quarters. However, sand body 1 has much more dolomite than sand body 5. Dolomite can occupy more than 50% of the solid portion of a sand body (e.g. sand body 1). The partially replacive nature of the dolomite within sand bodies has already been established from petrographic data (Fig. 3a) so that the high dolomite contents probably

reflect partial replacement of detrital silicate mineral grains as well as precipitation of dolomite into pre-existing pore spaces.

Effect of dolomite cement upon the reservoir properties of the Chaunoy Formation sandstone The present day porosity of the quartz-rich samples is significantly less than the porosity that would result from compaction alone (with no cementation) of quartzose sandstones at these depth of burial. Quartzose sandstones should have approximately 25-30% porosity after compaction during maximum burial to about 3000m (see, for example, North 1985). The actual porosities, even in the quartz-rich intervals, are only as high as about 19% indicating that some of the quartz in the rock must be quartz cement. The main control on porosity and thus permeability in the quartz-rich portions of the rock must be the extent of quartz cementation. Quartz-rich portions of sandbodies have

208

R.H. WORDEN Thus, not only does the dolomite cement preferentially obscure porosity at the tops of the sandstone units, but it also leads to disproportionately lower permeabilities than quartz-cemented sandstones of similar porosity.

Origin of dolomite cement in the Chatmoy Formation sandstone

Fig. 13. Sketch of the morphologies of quartz cement and dolomite cement in a porous sandstone matrix. Quartz cement typically forms overgrowths and tend to leave pores relatively unobstructed and thus has a minimal impact upon permeability (see also Fig. 4). Dolomite tends to form rhombic euhedral crystals that sit in pores, blocking porosity to flowing fluids and thus reducing permeability to a greater degree than an equivalent volume of quartz cement. better permeabilities for a given porosity than their quartz-poor equivalents (Figs 2 and 4). For example, in dolomite-rich, quartz-poor samples with 10% porosity, permeability is typically about 1-2mD. In dolomite-poor, quartz-rich samples with 10% porosity, permeability is typically, about 10-100mD. This is reflected by the slightly steeper permeability-porosity gradient and higher permeability intercept of the regression line through the dolomite-rich, quartz-poor samples than the regression line through the dolomite-poor, quartz-rich samples on Fig. 4. This pattern confirms that the main mechanism of porosity-loss in the quartz-rich samples (quartz cementation) is less detrimental to permeability than porosity-loss in the quartzpoor samples (dolomite cementation) as suggested by Cade et al. (1994). Dolomite cement in the Chaunoy Formation sand bodies tends to fill pores and block pore throats thus degrading permeability at a greater rate than quartz cement that forms equal-thickness rims to grains (Fig. 13).

The dolomite-rich fine-grained (mud and silt) beds in the Chaunoy Formation resulted from dolocrete pedogenesis (Sp6tl & Wright 1992) in inter-channel facies. The similarity between the (very fine) crystal size and texture of the dolomite in the fine beds and the dolomite at the very tops of the sand bodies (Fig. 3a) suggests that some of the dolomite in the sand bodies may be related to the formation of the dolocrete during pedogenesis The replacive nature of the finely crystalline dolomite in the sand bodies, as indicated by the corrosion of detrital quartz and feldspar grains (Fig. 3a), supports the development of this dolomite by pore waters that simultaneously dissolved silicate minerals and precipitated carbonate minerals during pedogenesis. However, much of the dolomite within the sand bodies does not have the same morphology and chemistry as the dolocrete material: much occurs as coarsely crystalline, ferroan dolomite rhombs (Fig. 3). From the textural and mineral chemical evidence, this must have a different genesis than the dolocrete. Textural evidence shows that rhombic ferroan dolomite post-dates quartz cement overgrowths (Fig. 3b). Aqueous fluid inclusion temperatures in Fig. 10, and also reported by Sp6tl et al (1993) and Demars & Pagel (1994), from rhombic ferroan dolomite are somewhat higher than present day temperatures, suggesting that dolomite grew at close to maximum burial and temperature conditions in the Oligocene/Miocene (Loup & Wildi 1994). Maximum burial of the sedimentary pile during the Oligocene/Miocene was also the time of petroleum generation and migration from the Liassic source rocks into the Chaunoy Formation. Rhombic ferroan dolomite cement therefore grew in the Chaunoy Formation at approximately the same time as oil was being generated from the overlying Liassic source rocks (Poulet & Espitalie 1987). Carbon stable isotope data for the burial diagenetic dolomite cements (Fig. 11; Worden & Matray 1995) indicate that carbon depleted in 13C has been added to the dolomite relative to the pedogenic dolocrete (Sp6tl & Wright 1992). Carbon depleted in 13C is thought typically to

DOLOMITE AND CEMENT DISTRIBUTION IN A SANDSTONE have an organic origin (e.g. Longstaffe 1989). The most obvious source of organically-derived bicarbonate or CO2 in the Paris Basin is the Liassic shale source rock. pH-buffered rocks undergo carbonate mineral precipitation when the partial pressure of CO2 is increased (Lundegaard & Land 1989) suggesting that at least some of the rhombic ferroan dolomite cement may be the direct result of bicarbonate or CO2 influx increasing the partial pressure of CO2. Liassic source rocks may have expelled CO2 during or before oil generation. The Triassic sandstones in the Paris Basin are presently in equilibrium with CO2 in the reservoir; that is partitioned between the two liquid phases: oil and water (Matray et al. 1993). The equilibrium partitioning of CO2 between formation water and oil suggests that CO2 may have been brought into the reservoir by the oil in solution. Subsequent partitioning of CO2 into the formation water may then have caused dolomite supersaturation and precipitation. Alternatively, CO2 may have migrated into the Chaunoy sand bodies as a separate gas phase resulting from thermal decarboxylation of organic matter prior to the onset of oil generation. Whatever the mechanism, isotopic data dictate that an increase in the partial pressure of CO2 (from an organic source) was most likely responsible for the precipitation of dolomite cement in the sandbodies during diagenesis at close to maximum burial.

Origin o f the dolomite cement distribution pattern Dolomite cement is generally concentrated at the tops of sand bodies in the Chaunoy Formation (Figs 7 and 9). There are several potential generic controls on dolomite distribution patterns (Fig. 12): (1) The cement at the tops of sand bodies may be a direct result of pedogenesis that occurred at the same time as the development of the pedogenic dolocrete in the fine grained units. This would occur preferentially at the tops of sand bodies adjacent to zones of active dolocrete pedogenesis. This process is probably at least partly responsible for the dolomite cement distribution in the sand bodies. (2) In principle, dolomite distribution in sand bodies may be due to diffusion from the pedogenic dolocretes that encase the sand bodies. In this case the dolomite would be redistributed by diffusion from the dolo-

209

crete into the sand bodies. This would influence the top and base of sand bodies eqpally and result in a minimum dolomite cement content at the centre of the sand bodies. Note that this is not observed (Figs 8 and 9) and that rhombic ferroan dolomite has a carbon isotope signature that is different from the pedogenic dolomite (Sp6tl & Wright 1992; Worden & Matray 1995). (3) Cement distribution could be influenced by reservoir quality at the time of cementation. High permeability streaks or gradational permeability may have focussed flow and input of CO2 into specific portions of the rock. Fluvial sandstones usually fine upwards resulting in diminishing permeability towards sand body tops. This would lead to the most extensive dolomite cementation occurring at the bases of sand bodies. However, note that the Chaunoy sandstones do not fine upwards (Fig. 2) and do not have dolomite preferentially at sand body bases. (4) Isotope data (Fig. 11) suggest that some of the carbon in the dolomite has an organic source and might have come from the oil source rock. Oil and CO2 may have migrated into the rock at about the same time (i.e. as CO2 dissolved in oil, Matray et al. 1993). Alternatively, CO2 may have migrated into the rock separately as a free gas phase. Due to buoyancy, the tops of each sand body should be the first part of the sandstone to encounter either oil (laden with CO2) or free CO2 gas. In summary, the tops of each sandbody may have received CO2 preferentially and thus caused localized dolomite precipitation. However, it is generally considered that oil emplacement hinders diagenetic processes so that the opportunity for this process to operate may be limited to a window of opportunity between the onset of oil emplacement and some elevated level of oil saturation (e.g. Worden et al. 1998). The absence of dolomite cement at sand body bases and abundance at sand body tops, the reported organic carbon isotope signal in the rhombic ferroan dolomite and the mixture of pedogenic dolomite textures and burial diagenetic textures in the sand bodies suggest that options 1 and 4 together are probably responsible for the distribution of dolomite in the Chaunoy sandbodies. The key implication from this analysis of dolomite cement distribution is that the dolo-

210

R.H. WORDEN

mite is heterogeneously distributed in the reservoir. A reservoir model (e.g. for simulation purposes) should take account of the fact that, despite approximately uniform grain size, porosity and permeability are not uniformly distributed due to preferential cementation at the tops of individual sandbodies. The combination of petrography, geochemistry and petrophysics was necessary to produce a model of cement distribution in this case. There are no 'off the shelf' models that can presently be fitted to the distribution of cements in reservoir sandstones in general. In time, generic models may be available as more case studies of cement distribution are performed, but until that time, each reservoir should be analysed using a similar combination of tools as that used in this study if reservoir quality heterogeneity is an issue.

Conclusions (1) Wireline petrophysical data have been successfully manipulated to give mineralogy in terms of the amounts of quartz, shale and dolomite, as well as porosity. (2) Core analysis data show that dolomite cement has a more detrimental effect upon permeability than quartz cement. Permeability has thus been calculated from the wireline porosity data using algorithms that account for the variation in mineralogy as well as porosity. (3) Petrography and, more importanly, wireline log data, have shown that dolomite c e m e n t is not u n i f o r m l y d i s t r i b u t e d t h r o u g h o u t the sandstones within the C h a u n o y Formation. Rather, dolomite cement is localized within the top portions of individual sandstone units. (4) Reservoir quality in the Chaunoy Formation is not just a function of depositional facies but is also a function of localized cement distribution. Building a reservoir model using primary sand body architecture alone is insufficient to correctly describe reservoir quality. (5) Sand bodies contain microcrystalline nonferroan and replacive dolomite as well as rhombic ferroan and pore-filling dolomite. Textural and mineral chemical data show that the microcrystalline dolomite probably grew during pedogenesis of the overlying fine-grained facies. Fluid inclusion and isotope data, together with textural evidence, show that the rhombic ferroan dolomite probably grew at close to maximum burial in the mid Tertiary in the presence of organically derived CO2.

(6) Dolomite cement may be localized at the tops of the sand bodies because of the proximity of overlying fine-grained units when they were undergoing pedogenesis and because the tops were the first part of each sand body to receive a charge of CO2. The CO2 influx may have occurred as a separate buoyant gas phase or as a gas dissolved in oil. (7) Reservoir simulation of sandstones should account for the fact that cement patterns do not necessarily follow primary sedimentary architecture. Cements may typically be heterogeneously distributed in individual sand bodies and this may have important consequences for how petroleum is produced to optimize flow rate and recovery. I would like to thank Elf-Aquitaine (F. Walgenwitz and G. Sambet especially) for kindly providing the core analysis and wireline data. Part of the study was the result of a collaborative research programme including BP, BRGM, Elf-Aquitaine, the University of Paris V1 and the European Community under contract JOUF-0016c. D. C. Herrick and an anonymous reviewer are thanked for identifying key areas of the manuscript for improvement.

References BOURQUIN,S. 8z GUILLOCHEAU,F. 1993. Gbometrie des sbquences de d6p6t du Keuper (Ladinien Rh&ian) du Bassin de Paris: implications gbodynamiques. Comptes Rendus Acad~mie des Sciences, Paris. 317, S6rie 2, 1341-1348. , BOEHM, C., CLERMONTE, L, DURAND, M. & SERRA, O. 1993. Analyse facio-sbquentielle du Trias du centre-ouest du bassin de Paris fi partir des donnbes diagraphiques. Bulletin de la Societe G~ologique Francais, 164, 177-188. BRUNET, M.-F. & LE PICHON,X. 1982. Subsidence of the Paris Basin. Journal of Geophysical Research, 87, 8547-8560. BRYANT, S., CADE, C. • MELLOR, D. 1993. Permeability prediction from geological models. American Association of Petroleum Geologists Bulletin, 77, 1338-1350. CADE, C., EVANS,I. J. 8s BRYANT,S. 1994. Analysis of permeability controls: a new approach. Clay Minerals, 29, 491-501. DEMARS, C. & PAGEL, M. 1994. Palbotemp6ratures et pal6osalinites dans les gr~s du Keuper du Bassin de Paris: inclusions fluides dans les min6raux authig~nes. Comptes Rendus Acad~mie des Sciences, Paris. 319, serie 2, 427-434. DOVETON, J. H. 1994. Geologic log analysis using computer methods. AAPG computer applications in geology, 2. American Association of Petroleum Geologists, Tulsa, USA. FOLK, R. L. 1974. Petrology of sedimentary rocks. Hemphill, Austin.

DOLOMITE AND CEMENT DISTRIBUTION IN A SANDSTONE FONTES, J. C. & MATRAY, J.-M. 1993. Geochemistry and origin of formation brines from the Paris Basin. Part 2, Saline solutions -A associated with oil fields. Chemical Geology, 109, 177-200. HEARST J. R. & NELSON, P. H. 1985. Well logging for physical properties. McGraw-Hill, New York HERRON, S. L. & LE TENDRE, L. 1990. Wireline sourcerock evaluation in the Paris Basin. In: Huc, A. Y. (ed.) Deposition of organic facies. American Association of Petroleum Geology Studies in geology, 30, 57-71 HURST, A. & MILODOWSKI,A. 1996. Thorium distribution in some North sea sandstones: implications for p e t r o p h y s i c a l e v a l u a t i o n . Petroleum Geoscience, 2, 59-68. LON~STAVFE, F. J. 1989. Stable isotopes as tracers in clastic diagenesis: In: HUTCHEON J. (ed.) Miner-

alogical Association of Canada short course in diagenesis. 201-277. LouP, B. & WILDI, J. W. 1994. Subsidence analysis in the Paris Basin: a key to Northwest European intracontinental basins? Basin Research, 6, 159177. LUNDEGARD, P. D. & LAND, L. S. 1989. Carbonate equilibria and pH buffering--response to changes in PCO2. Chemical Geology, 74, 277-287. MATRAY, J.-M., FOUILLAC,C. & WORDEN, R. H. 1993. Thermodynamic control on the chemical composition of fluids from the Keuper aquifer of the Paris Basin In: PARNELL, J. RUEFEL, A. H. & MOLES N. R. (eds) Extended abstracts from Geofluids '93, 12-16. MEGNIEN, C. 1980a. Tectogen+se du Bassin de Paris: etapes de L'evolution du bassin. Bulletin de la Societe Gdologique Francais 22, 66%680. - - , 1980b. Synthdse gdologique du bassin de Paris. Stratigraphie et paleogeographie. M e m o i r e BRGM 101, 466.

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NORTH, F. K. 1985. Petroleum Geology. Allen and Unwin, Boston PACES L. 1987. Exploration of the Paris Basin. In: BROOKS, J. & GLENNIE, K. (eds) Petroleum Geology of North West Europe. Graham and Trotman, UK, 87-93. POMMEROL, C. 1974. Le bassin de Paris. In: DEBELMAS, J. (ed.) Geologie de la France. Doin, Paris, 230258 , 1978. lEvolution pal6og6ographique et structurale du Bassin de Paris, du Pr6cambrian /t l'actual, en relation avec les r6gions avoisinantes. Geologie en Mijnbouw, 57, 533-543. POULET, M. & ESPITALIE, J. 1987. Hydrocarbon migration in the the Paris Basin In: DOLI6EZ, B. (ed.) Migration of hydrocarbons in sedimentary basins. Editions Technip, Paris, 131-171. RmER, M. H. 1986. The geological interpretation of well logs. Blackie. Glasgow. SMALLEY, P. C., MAILE, C. N., COLEMAN, M. L. & ROUSE, J. L. 1992. LASSIE (laser ablation sampler for stable isotope extraction) applied to carbonate minerals. Chemical Geology (Isotope Geoscience Section), 101, 43-52. SPOTL, C. & WRIGHT, V. P. 1992. Groundwater dolocretes from the Late Triassic of the Paris Basin, France: a case study of arid, contintental diagenetic facies. Sedimentology, 39, 1119-1136. - - , MATTER, A. & BREVART,O. 1993. Diagenesis and pore water evolution in the Keuper reservoir, Paris Basin (France). Journal of Sedimentary Petrology, 63, 909-928. WORDEN, R. H. & MATRAY, J.-M. 1995. Cross formational flow in the Paris basin. Basin Research, 7, 53-66. --, SMALLEY,P. C. & OXTOBY,N. H. (1988) Can oil emplacement prevent quartz cementation in sandstones? Petroleum Geoscience, 4.

Conjunctive interpretation of core and log data through association of the effective and total porosity models P. F. W O R T H I N G T O N

Gaffney, Cline & Associates, Bentley Hall, Blacknest, Alton, Hampshire GU34 4PU, UK Abstract: Traditionally, the deterministic open-hole petrophysical evaluation of non-Archie primary reservoirs has been undertaken exclusively within one or other of two intergranular systems, those of effective and total porosity. Yet, these interpretative models can be considered conjunctively with the object of inter-model validation of petrophysical interpretation. These considerations reveal ways of demonstrating the numerical compatibility of the two approaches. The compatibility is expressed in terms of equalities that contain core-calibrated, log-derived parameters and that are founded on the underlying petrophysics. The equalities must be satisfied if the petrophysical procedures are to be applied consistently and correctly. These inter-model algorithms constitute a basis for a proposed quality assurance scheme in well-log interpretation that goes beyond tying log data back to core. They suggest quality control points at which core-calibrated log data can be examined to assess the meaningfulness and performance of interpretation procedures at different stages of the petrophysical evaluation process. These assessments form a basis for the development of measures of confidence in the practice of open-hole well-log interpretation for porosity and hydrocarbon saturation, regardless of whether the interpretation is ultimately set in the context of the effective or the total porosity model. More generally, the subject matter forms part of a broader thrust to introduce a systematic quality assurance culture into open-hole petrophysical interpretation.

Open-hole petrophysical evaluation of nonArchie rocks, those that do not satisfy the conditions for the application of the laws of Archie (1942), has traditionally drawn upon either effective or total porosity concepts as a basis for the determination of reservoir porosity and fluid saturations. The difference between the two concepts lies in the interpretative treatment of the electrochemically-bound interstitial water. This should not be confused with capillary bound water, whose volume can be an order 9f magnitude greater (Pallatt & Thornley 1990), nor with those dual-porosity waters that are distinguished by pore type. Petrophysicists have usually operated the effective and total porosity models discretely, selecting one or the other at an early stage of the formation evaluation process. This exclusive choice has been driven by company culture, software considerations, statutory requirements, or technical or personal preference. The practice of selecting a discrete interpretative model constrains the manner in which core data can be used to support and validate log interpretation. Thus, for example, effective porosity cannot easily be determined in the laboratory, and yet these data are strictly required by the effective porosity model in order to control the porosity interpretation of neutron~lensity log cross plots. In contrast, the electrochemical shale parameters needed to

evaluate water saturation as per the total porosity model can be determined in the laboratory but they cannot be measured directly downhole. The constraints imposed by an exclusive porosity model therefore limit the benefit that can be derived from the ensuing integrated analysis of laboratory and downhole data. The key to improved core and log interpretation is to operate the effective and total porosity models in parallel, using the cross-linkages between them to transpose the advantages of one in support of the other, especially where core data can be used to provide quality control on the log interpretation. The purpose of this paper is to develop the opportunities for improved quality assurance in formation evaluation, in accordance with earlier projections (Worthington 1991), by considering how the effective and total porosity models can be operated conjunctively to allow inter-model validation of petrophysical interpretation. The primary aim is to demonstrate the numerical equivalence of the two models through equalities that contain core-calibrated, log-derived parameters and that honour the principles of the underlying physics. Thus, the objective is an interpretative scheme that brings together traditionally separate areas of petrophysical systemics within a quality-controlled integrated framework.

WORTHINGTON,P. F. 1998. Conjunctive interpretation of core and log data through association of the effective and total porosity models. In: HARVEY,P. K. 8s LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 213-223

213

214

P.F. WORTHINGTON

Fig. 1. Effective and total porosity models for a water-wet reservoir.

Philosophy of formation evaluation Traditional formation evaluation practice is set within either the effective or the total porosity system, although one or two procedures that are seen as transgressing this otherwise exclusive divide have emerged over the past 15 years (e.g. Juhasz 1981). The basic difference between the effective and total porosity approaches can be summarized with the aid of Fig. 1, which describes the key parameters for a water-wet reservoir. A nomenclature is provided at the end of the text. The effective porosity system regards those waters that are electrochemically bound to (clay) mineral surfaces as an integral part of the minerals themselves. Therefore, the bound-water porosity qbbw is incorporated within a wetted clay-mineral fraction, which is loosely termed a wetted shale volume fraction Vsh, and which is allowed to have different physical properties from those of the clean rock matrix. Only the electrochemically-free fluids comprise the porosity, which is termed 'effective'. Some authors have used the term 'effective clay mineral volume' to be synonymous with Vsh (Hurst & Nadeau 1994), but note with caution that others have used the seemingly related term 'effective clay' specifically to distinguish a clay mineral that is electrochemically active from one that is not (Johnson & Linke 1977). In contrast, the total porosity system separates the electrochemically bound waters from the clay-mineral fraction and groups these with the free fluids that occupy the remainder of the pore space. Therefore, d0bw is distinct from the clay-mineral fraction, which is sometimes loosely termed a dry clay fraction Vd. The dry clay is required to have the same physical properties as those of the clean rock matrix, but it is allowed different electrochemical char-

acteristics, quantified through the cation exchange capacity per unit pore volume Qv. All constituent fluids comprise the porosity, which is termed 'total'. Despite this seemingly all-embracing name, the term 'total porosity' does not include fracture porosity, and the sum of these is better described as 'absolute porosity'. The role of the effective and total porosity systems within the overall scheme of formation evaluation is governed in some quarters by the polarization of the subject area into statistical and deterministic methods of interpretation. These approaches, too, need not be operated exclusively, but their integrated use extends beyond the scope of this paper and is the subject of another that is in preparation. Statistical methods of petrophysical interpretation are based on the global approach to the solution of log response equations (Mayer & Sibbitt 1980). If these equations contain characterizing shale properties and solve for a wetted shale fraction, the computed porosity will be an effective porosity. If the response equations do not contain characterizing shale properties and do not solve for a wetted shale fraction, the implicit requirement for a total porosity approach must be one of identical log responses to clean rock matrix and dry clay minerals for all the log responses represented within the input matrix over the evaluation interval. The highly tenuous nature of this general assumption inhibits the meaningful operation of the global approach in the total porosity system. On the other hand, deterministic methods can be operated in both the effective and the total porosity systems, because logs can be used more selectively on the basis of their being fit-forpurpose. Therefore, the following discussion is set within the context of deterministic petrophysics. The scheme of deterministic open-hole petro-

EFFECTIVE AND TOTAL POROSITY

Effective porosity model

RESERVOIR

t

FORMATIONWATERSALINITY

CLAY-MINERALCONTENT 1

I

ARCHIE

POROSITY

Sw

"7

+ +

NON-ARCHIE

TOTAL I EFFECTIVEpoROSI ] TY I POROSITY

EFFECTIVE Sw

215

+

I

I TOTAL Sw

Fig. 2. Scheme of deterministic open-hole petrophysics. physics is illustrated in Fig. 2. Reservoirs with a high formation-water salinity and a low claymineral content are usually termed Archie reservoirs, wherein the effective and total porosities are essentially the same, because there are negligible bound-water effects. Otherwise, they are termed non-Archie reservoirs, because there can be a significant bound-water saturation. Non-Archie reservoirs can be evaluated in terms of either effective or total porosity but, whichever parameter is adopted, the subsequently derived water saturations must also be set within that same porosity system, for consistency. Exceptions to this simplified distinction include fine silty reservoirs and those where the rock matrix exhibits microporosity. In both cases, non-Archie behaviour can be caused by a low surface charge density integrated over a huge pore surface area rather than the conventional case of a higher surface charge density (due to strong cation-exchange characteristics) integrated over a more limited pore surface area. It is difficult to accommodate such cases of high pore surface area within either the effective or the total porosity system. In the following discussion the context is one of dispersed shales within a water-wet reservoir that has no fracture porosity and whose matrix comprises sandstone, limestone or dolomite, or some mixture thereof.

Because the chemically-bound water is included within a wetted shale fraction, logs are corrected for shale effects so that the bound water might be removed from consideration. The wetted shale is therefore replaced by electrochemicallyinert rock of identical geometry and with the same physical properties as the clean rock matrix. This is done by using the generic correction algorithm:

Xcorr=X - Vsh (Xsh -Xma )

(1)

where X is the log response, Xsh is the log response to shale, Xma is the log response to clean rock matrix, and Xcorr is the log response corrected for the effect of shale. Equation (1) should use Vsh values that are derived from a compatible shale indicator. For example, V~h from the gamma log might not be appropriate to correcting the density-log response whereas V~h derived from a neutron-density shale indicator would be more suitable. In general, the resulting values of Xcorr become less accurate with

increasing Vsh. After applying equation (1) to the sonic, density and neutron logs, effective porosity can then be evaluated as for clean sands:

doe=( x .... --Xma)/(X f --Xma ).

(2)

The results are implemented over net sand intervals, which can be selected subsequently. Effective water saturation Swe is the fractional water content of the free fluid. It is evaluated using a shaly-sand conductivity algorithm, such as the modified Simandoux equation (Bardon & Pied 1969): Ct = (Cw/Fe*)

awen* + Vshfshawe n*-I

(3)

where Ct is the conductivity of the reservoir rock, Cw is the conductivity of the formation water, Csh is the conductivity of the wetted shale, n* is the intrinsic Archie saturation exponent, and the intrinsic formation factor Fe* is compatible with the effective porosity system in that it is calculated from doe as follows:

Fe* = a*/doem*

(4)

where a* and m* are the intrinsic Archie porosity coefficient and exponent, respectively. A scheme for deterministic petrophysical interpretation (with a* = 1) within the effective porosity system is presented as Fig. 3. The immediate deliverables are dOeand She, where She

216

P.F. WORTHINGTON

Shale-corrected Log Response DENSITY NEUTRON SONIC

_I -

Porosity Oe

L F

] ARCHIE'S [ ~ A=W FIRST 1F / Oee.m

Core control in clean zones HELIUM POROSITY Core control INTRINSIC POROSITY

EXPONENT m*

Formation water sample WATER CONDUCTIVITY Cw

UNIFIED SHALE / VOLUME FRACTION Vsh Jl Log Response LATEROLOG

INDUCTION Ct C sh

/

I

I_ ~_1 MODIFIED SIMANDOUX EQUATION Ct=Cw

I

I

F-~

Swen +Vs h Csh Swn-1

Core control INTRINSIC SATURATION EXPONENT

Oe Swe

n*

Fig. 3. Petrophysical interpretation within the effective porosity system.

is the effective hydrocarbon saturation such that She = 1-Swe.

as that of Waxman & Smits (1968): C t = ( C w / F t * ) S w n• -q- (BQv/Ft*)Swtn*-I

Total porosity model The electrochemically-bound water is intuitively separated from a dry clay-mineral fraction and is included within the porosity. Logs are not corrected for the effects of dry shale, which is therefore presumed to have the same physical properties as the rock matrix. The assumption of identical physical properties for dry shale and matrix is approximately satisfied only in the case of density. Total porosity is therefore evaluated from the density log as for clean sands: dot = (Pg-- Pb)/(Pg-- Pf )

(5)

where Pb is the log-measured bulk density, pf is the density of interstitial fluids within the volume sensed by the density tool, and pg is the grain density rather than a pure matrix density. The results are implemented over net sand intervals, which must be specified at the outset, because grain density will take account of constituent shale, and this might cause intervaldependent departures from classical matrix values, giving rise to a potential non-conformance with the assumptions of the method. Total water saturation Swt is the fractional water content of the total fluid. It is evaluated using a shaly-sand conductivity algorithm, such

(6)

where Qv is the cation exchange capacity per unit pore volume (equiv. litre-1), B is the equivalent conductance of the (sodium) clayexchange cations (S m -1 equiv. -1 litre), a function of Cw, and Ft* is compatible with the total porosity system in that it is calculated from dot as follows: Ft* = a*/dotm*

(7)

A scheme for deterministic petrophysical interpretation (with a * = l ) within the total porosity system is presented as Fig. 4. The immediate deliverables are dot and Sht, where Sht is the total hydrocarbon saturation such that Sht = 1-Swt.

Association of the models The effective and total porosity systems are associated through a sequence of relational algorithms that allow parameters calculated in one system to be transposed to the other. These associations form the basis for any conjunctive use of the two systems.

Shale volume fraction The wetted and dry shale fractions are related

EFFECTIVE AND TOTAL POROSITY

_I

Log Response DENSITY

Porosity

Core control in clean zones HELIUM POROSITY

L

ARCHIE'S FIRST ~ W L

[

1,o,

217

Core control INTRINSIC POROSITY EXPONENT m*

Formation water sample WATER CONDUCTIVITY Cw

r ~176 , 89 Log Response LATEROLOG INDUCTION Ct

WAXMAN-SMITS EQUATION C t = C w Swtn*+ BQ v Swtn*-I

Ot

Swt

]

B -- f (Cw) Core control INTRINSIC SATURATION EXPONENT n*

Fig. 4. Petrophysical interpretation within the total porosity system. through the expression: Vsh = Vd

+ ~bw

(8)

where ~)bw: Vsh ~)tsh

(9)

and ~tsh is the porosity of the wetted shale calculated from the expression: ~tsh = (Pcl -- Psh)/(Pcl -- Pf)

(10)

It follows from equations (11) and (12) that grain density can be re-expressed in terms of qbe and Vsh as follows: Pg = P m a ( 1 - - ~ e - - V s h ) + PclVsh(1 --(~tsh) 1 -- qbe -- Vsh ~tsh

(13)

Equation (13) reduces to Pg-----Prna when Fsh=0. In the effective porosity system, pg is taken as the matrix density Pma. In the total porosity system, pg is not necessarily equal to Pma.

where Psh is the density of the wetted shale, pd is the density of the dry shale fraction and for practical purposes pcl is equated to pg. Shale porosity is referred to the volume of wetted shale. By combining equations (8) and (9) we have:

Porosity

Vcl = Vsh ( 1 - (~tsh)-

where Vsh is ideally the wetted shale fraction from the neutron-density log combination. An alternative form of this expression is:

(11)

In the effective porosity system, the estimated Vsh is distinct from the rock matrix: in the total porosity system, the unknown Vcl is grouped with the rock matrix.

Grain density If Vma is the fractional volume of the rock matrix, the grain density can be expressed: Pg = (Pma Vma + Pcl Vcl )/( Vma "[- Vcl).

(12)

Equation (12) reduces to pg= Pma when Vd = 0.

Effective and total porosity are related through the expression: Ct = Ce q- Vsh Ctsh

~)t = q~e-k-~)t Swb

(14)

(15)

where Swb is the bound-water saturation, pursuant upon the dual-water model of Clavier et al. (1984). Equation (15) follows from the equality: Swb = Vsh Ctsh/~)t

(16)

Yet another form of equation (15), based on the normalized Qv concept of Juhasz (1981), is

218

P.F. WORTHINGTON

written: ~t-- ~be-~ ~t

Qv/Qvsh

(17)

to be. Equations (4) and (7) can be combined as follows: (20)

where Qv is relative to the cation exchange capacity per unit pore volume of a reference shale Qvsh. If the bound water has a unit density, a plausible assumption, equation (17) can also be written in the empirical form of Hill et al. (1979):

Equation (20) does not explicitly include the porosity coefficient a*, although that quantity is intuitively related to the value of m* for free-fit regressions of reservoir data.

~)t = ~e nt- ~)t Qv (0.084 Ce~)'5 nt- 0.22)

Conductivity o f reservoir rock

(18)

where Ce is the concentration (in equiv, litre -1) of the equilibrium water in the free pore space, net of cation adsorption, and can be expressed as a calculable function of Qv, Swt and saturating water concentration Cs, at a reference temperature of 25~ Equation (18) has been seen as a quantitative link between the effective and total porosity models (Ruhovets & Fertl 1982), but it is not a generally applicable equality, although Juhasz (1979) did cite evidence that the volume of bound water is effectively independent of temperature over the range 20-200~ Note the complication, to be discussed later, that the conventional laboratory measurement of porosity through helium expansion is often carried out on a humidity-dried sample, and there is a view that the measured porosity is actually a hybrid porosity, being somewhere between the limits of effective and total porosity.

Formation res&tivity factor Laboratory measurements of formation resistivity factor F fall under the umbrella of special core analysis and, as such, are usually carried out on lithologically cleaner samples, because it is the practice to preserve the better quality reservoir rock. Otherwise multiple-salinity measurements of electrical conductivity can furnish an intrinsic formation factor F* for non-Archie reservoirs. The resulting formation factors are then correlated with their respective porosities with the object of characterizing the first Archie equation through the intrinsic porosity coefficient a* and exponent m* so that:

Ft* = Fe* (~)e/~)t) m~

By equating the reservoir rock conductivities of equations (3) and (6) and setting Sw = 1, we have: (Cw + B Qv)/Ft* = (Cw/Fe*) + VshCsh and therefore Qv = Ft* (Cw + Vsh CshFe*) - Fe. Cw

Equation (22) describes the relationship between Qv and Vsh assuming that the water-zone forms of the Waxman-Smits and Simandoux equations are valid within their respective porosity systems. It is interesting to consider the boundary conditions on equation (22). When Vsh=0, Fe* = F t* and therefore Qv=0. When Vsh = 1 for a perfect shale in which Cw approaches the bound-water conductivity Cbw, 4~e= 0 and therefore Fe* is infinite. Under these conditions, equation (22) reduces to: Qvsh =

Ftsh* Csh -- Cbw B

(23)

where Ftsh* is the intrinsic formation factor of the perfect shale. Significantly, equation (23) might allow the determination of Qv for a perfect shale by drawing upon the relationship: ftsh* ~---a*/~)tsh m*

(24)

Equation (23) can be rewritten:

(19)

This equation is applied in both the effective and the total porosity systems, in the form of equations (4) and (7), respectively, according to the nature of the log-derived porosity. There are no separate relationships for the two systems even though the earlier comments about porosity measurement might suggest that there ought

(22)

BF~.

Cbw ~- BQvsh

F* =a*/q~ m*

(21)

(25)

Ftsh* or, alternatively: Ftsh*= Ftsh (1 + (BQvsh/Cbw))

(26)

where Fts h =

Cbw/Csh.

(27)

EFFECTIVE AND TOTAL POROSITY

In practice, however, g s h = 1 will correspond to an imperfect shale that does not fully comprise clay minerals and therefore the limiting conditions will not be attained. Note that equation (27) reveals the same definition of shale formation factor as that related directly to q~tsh through a pseudo-Archie expression in the dual-porosity model of Raiga-Clemenceau et al. (1984).

Fluid saturations The interpreted hydrocarbon-filled porosity must be the same in both the effective and the total porosity systems, otherwise the computed hydrocarbons in place will be different. Therefore: ~e She = ~t Sht

characterization of pseudo-matrix, the average mixture of matrix and dry shale within the net sand, which must be specified at the outset. Fluid density within the flushed zone must also be quantified over the same intervals. The density log remains uncorrected for shale effects and it is used directly to infer total porosity. The computed porosities are input to the first Archie equation to evaluate corresponding intrinsic formation factors. Agreed algorithms for (a) the equivalent conductance B in terms of Cw and (b) the cation exchange capacity per unit pore volume Qv in terms of total porosity have to be established in support of the water saturation equation (Fig. 4). The above contrasting procedures suggest a sufficient degree of difference to allow scope for independent cross-checks between the two approaches.

(28)

Relative strengths

or

(1

- Swt)

(29)

Swt = 1 - (~e/4)t) (1 - Swe)

(30)

Oe (1 -- Swe ) = (~t

219

so that

Equation (30) allows a direct comparison of the water saturations inferred in the two systems.

Quality assurance for formation evaluation The effective and total porosity models can only be used conjunctively to enhance core-calibrated log interpretation if the two approaches are sufficiently different to furnish independent evaluations. The degree of independence is governed by systemic differences in the application of these two models.

Systemic differences The effective porosity system entails the characterization of matrix, fluid and shale points without the need to specify net sand at the outset. The neutron and density logs are corrected for light hydrocarbon effects before all three porosity logs are corrected for shale effects. At that point the corrected porosity logs are deemed to be sensing effective porosity. The interpreted porosities are used to calculate corresponding values of intrinsic formation factor. A unified shale volume fraction and shale conductivity are other essential inputs to the water saturation equation (Fig. 3). The total porosity system entails the density

The relative strengths of the total and effective porosity systems are embodied within the meaningfulness of tying back to core data. This, in turn, raises the question of how the core data were measured. Tying back to core is not possible for the wetted shale fraction Vsh and the practice is uncommon for the dry clay-mineral volume fraction Vd in a solely petrophysical context. Nevertheless, X-ray diffraction, X-ray fluorescence, chemical and thermogravimetric analysis, scanning electron microscopy and infrared spectroscopy can be used to gain a quantitative insight into the occurrence of clay minerals and thereby to establish some reference basis for Vcl, although the subjective nature of some laboratory interpretations might detract from the perceived usefulness of this approach as a groundtruthing facility. In this respect, therefore, the total porosity system is the stronger and it affords some opportunity for tying claymineral content back to core. The relationship of Pma to pg can be used to validate the underlying assumption of the total porosity model, that the density of dry clay minerals equals that of rock matrix. This assumption is more likely to be satisfied where the clean rock matrix has constant density. It is unlikely to be satisfied where the clean matrix properties are markedly variable. Where the assumption is not satisfied but shale density is known, it is theoretically still possible to proceed with a total porosity approach, but on a levelby-level basis. This procedure would require a quantification of Vcl at each digital sampling level of the well logs, so that grain density might

220

P.F. WORTHINGTON

be evaluated at each level. This requirement would, in turn, necessitate a reversion to the relationship between V~h and Vcl. Because of this potential complexity, which many regard as prohibitive, the effective porosity model is seen as the stronger in terms of the opportunities for utilizing and validating the density of reservoir rock. Note, however, that this contention is dependent upon the measurement of a meaningful effective porosity. The tying back to core of log-derived porosity values is fraught with potential difficulty. It has been argued, but not universally, that the oven drying of core plugs at temperatures of around 105-110~ removes chemically-adsorbed waters without altering, chemically, the solid claymineral fabric. Therefore, it has been claimed that helium porosities measured subsequently on these plugs are likely to be total porosities. On the other hand, the humidity drying of core plugs at temperatures of about 60~ is claimed by some to retain the bound waters, while expelling the free water, so that porosities measured subsequently are effective porosities. This contention is at variance with the data of Hill et al. (1979), which suggest that some bound waters are expelled despite the humidifying process and that the measured helium porosities are intermediate relative to the effective and total porosities (Juhasz 1988). The effect may not be serious in reservoir rocks, for Pallatt & Thornley (1990) note that electrochemicallybound water accounts for less than 2.5% of the pore volume: for a rock with 20 porosity units, the estimated bound-water volume is therefore less than 0.5 porosity units, a figure which is equivalent to the uncertainty associated with core porosity measurement. Nevertheless, the only uncontentious way of groundtruthing log-derived porosity to conventionally-measured core porosity is to confine such comparisons to essentially clean zones. Under these conditions, both the effective and the total porosity models are equivalent. A comparison of Fe* and Ft* offers an intrinsic measure of the effect of pore geometry on electrical conduction, subject to the assumptions of equal porosity coefficients and equal porosity exponents for the two systems. Because the definitive multiple-salinity procedures furnish Ft*, the total porosity model provides the sounder physical basis. Laboratory-measured values of Ft* therefore serve as the definitive reference. In a user setting, values of Qv are obtained from a dubious relationship to porosity and estimates of gsh are made from one or more of several tenuous shale indicators. There is no

reference value of Vsh available from the laboratory: there might be values of Qv, which can be taken as definitive if these are determined meaningfully from multiple-salinity measurements of rock conductivity. Therefore, such a Qv database becomes the definitive core reference. The relationship of the equivalent conductance of (sodium) clay exchange cations B to the conductivity of saturating electrolyte Cw remains a weak link in the total porosity system, because several different relationships have been proposed. This weakness is not removed by the use of multiple-salinity conductivity data, which require a value of B before Qv can be quantified. Tying back log-derived water saturations to core is usually founded on the extraction of interstitial waters from vertical plugs cut from the inner parts of whole core pieces that have been drilled using a low-invasion coring bit with an oil-base mud. The procedure is established but not yet standard practice. The plug-extracted water saturations are notionally values of Swt. They can be used as a reference for the effective porosity system through the equivalence of hydrocarbon-filled porosity. In particular, the comparison serves as a validation of the feeder relationships, especially that of Qv vs ~bt, which is often highly tenuous. Q u a l i t y assurance s c h e m e

A quality assurance scheme for deterministic open-hole formation evaluation is shown in Fig. 5. The purpose of this simplistic scheme is to illustrate how the effective and total porosity models can be operated conjunctively to increase confidence in the resulting petrophysical interpretation. It is not intended to constitute the ultimate framework for quality control but rather to indicate how greater confidence in petrophysical interpretation can be secured through an integrated use of the two models. The first element of Fig. 5 is concerned with tying back Vsh to core-derived Vcl through ~tsh, the determination of which requires a knowledge of pcl. A satisfactory tie-back would reconcile wetted shale and dry clay-mineral fractions in the two porosity systems. Failure to secure agreement over net sand intervals could be attributed to an inappropriate log-derived shale indicator, to an unrepresentative dry clay density, or to subjectiveness in the interpretation of core data. As in all cases of tying log data to core, the scale disparity might render the datasets irreconcilable, especially in markedly heterogeneous reservoir zones. Further, an unsatisfactory outcome at the subsequent (second) key stage might suggest an iteration through the

EFFECTIVE AND TOTAL POROSITY

221

Fig. 5. Foundations of a quality assurance scheme for open-hole petrophysical interpretation.

first. The second element is concerned with tying the computed grain density pg back to core through log-derived values of Vsh, q~e and ~btsh, and a knowledge of pc~ and Pma- A satisfactory tie-back would substantiate the assumptions concerning Pcl and Pma, the latter being verifiable over any shale-free intervals of net sand. Failure to secure agreement could be attributed to unrepresentative densities of matrix or dry clay minerals or to errors in q~e, which is not qualityassured until the third key stage, again suggesting some degree of iteration. The third element of Fig. 5 reconciles logderived effective porosity with log-derived total porosity through a comparison of ~bt indirectly calculated from q~e with that interpreted directly within the total porosity system over net reservoir intervals. The third element also allows both the log-derived porosities to be referred to core porosity over net reservoir intervals. Because of the uncertainty associated with the influence of sample preparation on the measured core porosity of shaly plugs, the tying back to core might best be done in two stages. First, the log-core comparison should be restricted to clean intervals to establish that the interpretation systems are functioning under the most straightforward conditions. Second, in view of the earlier comments concerning a hybrid core porosity, the tying back to core over shalier intervals of net reservoir should allow the measured core porosity to be of intermediate value relative to the log-derived effective and

total porosities. Indeed, if the core porosity lies between the corresponding log-derived values, this might be the best quality assurance that one could reasonably expect to achieve. Failure to reconcile the two datasets would suggest shortcomings in Vsh and/or (/)tsh and would require some iteration through elements (1) and (2). The fourth element reconciles log-derived Fe* with log-derived Ft* through a comparison of Ft* indirectly calculated from -be* with that interpreted directly within the total porosity system over net reservoir intervals. This is no more than a check for internal consistency. However, the fourth element also allows both the log-derived intrinsic formation factors to be referred independently to laboratory values of Ft*, preferably those obtained from multiplesalinity conductivity measurements of plugs from net reservoir intervals. Failure to tie back to core would suggest transmitted errors in ~be and/or ~bt, or perhaps the use of an inappropriate value of the intrinsic porosity exponent m*. The fifth element of Fig. 5 allows Qv estimated from a total porosity log to be tied back to core values, preferably those obtained unambiguously from multiple-salinity conductivity measurements of plugs from net reservoir intervals. This exercise serves as a check on the meaningfulness or otherwise of the algorithm used to predict Qv from a porosity log. Since this algorithm is itself characterized using core data, the core-derived Qv data should be distinct from those used to establish the relationship between Qv and qSt. The fifth element also allows log-

222

P.F. WORTHINGTON

derived Vsh values to be reconciled with logderived Qv data through a comparison of Qv values calculated from Vsh with those inferred directly from porosity and already validated through reference to core data. Failure to reconcile the data at this key stage would most likely be attributable to uncertainties in B and Csh. The sixth and final element reconciles logderived effective water saturation with logderived total water saturation through a comparison of Swt indirectly calculated from Swe with that interpreted directly within the total porosity system over net pay intervals. The sixth element also allows both the log-derived water saturations to be referred to core-derived water saturation. Failure to reconcile the data at this stage would imply possible errors in 4)e, ~t, B, Csh, Cw, a* or m*, and it would require iterating perhaps as far back as the third key stage. The quantification of the inter-model comparisons should take the form of acceptable tolerances in the agreement between directly and indirectly inferred values of the relational parameters and in their validation against core data. The development of these tolerances would be an immediate sequel to broad adoption of this proposed conjunctive interpretation scheme.

Conclusions A comparison of open-hole petrophysical interpretation practices for non-Archie reservoirs that are set exclusively within either the effective or the total porosity system has identified a set of relational algorithms through which these interpretative models can be associated. This identified numerical equivalence allows intermodel assessments of the consistency and validity of the interpreted data at key stages of the petrophysical evaluation process, so that some measure of reliability may be established. The assessments involve comparisons of interpretations made by separately using the two porosity models as well as the tying of these interpretations back to core. The key stages form the basis for a quality assurance scheme that draws upon the integration of traditionally separate areas of petrophysical systemics. The development of such a scheme in the form of quantitative measures of inter-model agreement would constitute a logical extension of the demonstrated association of the effective and total porosity models. This initiative forms part of an essential thrust to complement the excellent quality control that currently exists in well-log data acquisition. At present, our ability to acquire petrophysical data

exceeds our ability to interpret, especially in the three-dimensional settings of extended reach and multilateral wells. Ongoing advances in the three-dimensional modelling of tool responses will shortly allow meaningful environmental corrections in a way that opens the door to enhanced 3D interpretation of open-hole well logs. If the community is to draw the greatest benefits from that projected situation, a qualityassured interpretation scheme will be required for open-hole petrophysics. This paper has emphasized the nature of the technical positioning that will be needed to secure those benefits as we approach the millennium.

Nomenclature B equivalent conductance of (sodium) clayexchange cations (S m 1 equiv. 1 litre) Cbw conductivity of bound water ( S m ') Csh conductivity of wetted shale fraction (S m -1) Ct bulk conductivity of reservoir rock (S m -1) Cw conductivity of free water ( S m -1) F* intrinsic formation (resistivity) factor in generic form Fe* intrinsic formation (resistivity) factor in the effective porosity system Ft* intrinsic formation (resistivity) factor in the total porosity system Ftsh formation (resistivity) factor of a perfect shale in the total porosity system Ftsh* intrinsic formation (resistivity) factor of a perfect shale in the total porosity system Qv cation exchange capacity per unit pore volume (equiv. litre-1) Qvsh cation exchange capacity per unit pore volume of shale (equiv. litre -1) She fractional hydrocarbon saturation in the effective porosity system Sht fractional hydrocarbon saturation in the total porosity system Swb fractional bound-water saturation Swe fractional water saturation in the effective porosity system Swt fractional water saturation in the total porosity system Vd volumetric fraction of dry clay minerals Vmavolumetric fraction of clean rock matrix Vsh volumetric fraction of wetted shale X generic log response Xcorr generic log response corrected for shaliness Xma generic log response to clean rock matrix Xsh generic log response to shale a* Archie intrinsic porosity coefficient Ce concentration of equilibrium free water (equiv. litre 1) c~ concentration of saturating water (equiv. litre-1)

EFFECTIVE AND TOTAL POROSITY m* Archie intrinsic porosity exponent n* Archie intrinsic saturation exponent Pb log-derived bulk density ( g c m -3) pc~ density of dry clay-mineral fraction (g cm -3) pf density of interstitial fluids (g cm -3) pg grain density over net sand intervals ~g cm 3) Pma density of clean rock matrix ( g c m -~) psh density of wetted shale fraction (g cm -3) Obw bound-water porosity fraction Oe effective porosity fraction Ot total porosity fraction ~tsh total porosity fraction of shale

References ARCHIE, G. E. 1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Trans. AIME 146, 54-62. BARDON, C. 8z PIED, B. 1969. Formation water saturation in shaly sands. Trans. SPWLA lOth Ann. Logging Syrup., Zl-19, Society of Professional Well Log Analysts, Houston, Texas. BUSH, D. C. & JENKINS, R. E. 1970. Proper hydration of clays for rock property determinations. Journal of Petroleum Technology, 22, 800-804. CLAVIER, C., COATES, G & DUMANOIR, J. 1984. Theoretical and experimental bases for the dualwater model for interpretation of shaly sands. Society of Petroleum Engineers Journal, 24, 153167. HILL, H. J., SHIRLEY,O. J. & KLEIN, G. E. 1979. Bound water in shaly sands--its relation to Qv and other formation properties. The Log Analyst 20(3), 3-19. HURST, A. & NADEAU, P. 1994. Estimation of water saturation from clay microporosity data. SPE Paper 28850, Society of Petroleum Engineers, Richardson, Texas.

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JOHNSON, W. L. & LINKE, W. A. 1977. Some practical applications to improve formation evaluation of sandstones in the Mackenzie Delta. Trans. CWLS 6th Formation Evaluation Symposium, R1-32, Canadian Well Logging Society, Calgary, Alberta. JUHASZ, I. 1979. The central role of Qv and formationwater salinity in the evaluation of shaly formations. Trans SPWLA 20th Ann. Logging Syrup., AA1-26, Society of Professional Well Log Analysts, Houston, Texas. - 1981. Normalised Qv--the key to shaly sand evaluation using the Waxman-Smits equation in the absence of core data. Trans. SPWLA 22nd Ann. Logging Syrup., Z1-36, Society of Professional Well Log Analysts, Houston, Texas. MAYER, C. t~ SIBBIT, A. 1980. GLOBAL, a new approach to computer-processed log interpretation. SPE Paper 9341, Society of Petroleum Engineers, Richardson, Texas. PALLATT, N. & THORNLEY,D. 1990. The role of bound water and capillary water in the evaluation of porosity in reservoir rocks. In: WORTHINGTON,P. F. (ed.) Advances in Core Evaluation, Gordon and Breach, Reading, 223-237. RAIGA-CLEMENCEAU, J., FRAISSE, C. • GROSJEAN, Y. 1984. The dual-porosity model, a newly developed interpretation method for shaly sands. Trans. SPWLA 25th Ann. Logging Syrup., F1-16, Society of Professional Well Log Analysts, Houston, Texas. RUHOVETS,N. & FERTL, W. H. 1982. Digital shaly-sand analysis based on Waxman-Smits model and logderived clay typing. The Log Analyst 23(3), 7-23. WAXMAN, M. H. & SMITS, L. J. M. 1968. Electrical conductivities in oil-bearing shaly sands. Society of Petroleum Engineers Journal, 8, 107-122. WORTHINGTON,P. F. 1991. The direction of petrophysics: a five-year perspective. The Log Analyst 32(2), 57-62.

Permeability prediction in anisotropic shaly formations S. X U & R. W H I T E

Exploration Geophysics Group, Research School of Geological & Geophysical Sciences, Birkbeck College & University College London, Malet Place, London WC1E 6BT, UK Abstract: We present a unified model for simulating the permeability and electrical

conductivity of anisotropic shaly formations. The model is based on Willis' formulae and the concept of a host medium, the selection of which is crucial in predicting these transport properties. Different rock components, including shales and mudrocks, are characterized by parameters typifying their pore geometry, namely the aspect ratio, size and orientation distribution of the pores. In this regard the model is an extension of the elastic model of Xu & White for predicting P- and S-wave velocities in siliciclastic rocks. The electrochemical effect of clay minerals on electrical conductivity is simulated by Waxman & Smits' model. A novel feature of the permeability model is that its percolation factor is estimated by a nonlinear transformation of the percolation factor found from conductivity measurements. The model was tested on the laboratory measurements published by Waxman & Smits. Comparison of the results with those from the Waxman & Smits, Dual-Water, and K o z e n ~ Carman models, and with multilinear and non-linear regression techniques, demonstrated that the unified model predicted conductivity and permeability more accurately than any of these models from the same number or fewer parameters. The improved prediction was most noticeable in samples containing a significant clay mineral fraction. Apart from Waxman & Smits' data, we have found no published dataset that is comprehensive enough to test physical predictions of both conductivity and permeability.

Permeability is one of three key rock parameters in reservoir simulation and the provision of detailed estimates of permeability is a prime objective of applied petrophysics. Since permeability cannot be measured directly by logging tools, it is usually estimated indirectly from well logs with calibration from cores. A common practice is to use core measurements to establish an empirical relationship between permeability and properties such as porosity and formation factor and then to apply that relationship to well logs to construct permeability logs. Permeability is a complex property to predict empirically and it is not easy to obtain detailed information on the parameters that control the flow of fluids through rocks. Needless to say, the prediction of permeability is problematic. It is well understood that permeability is controlled by five key factors: porosity, the size, shape, orientation and connectivity (percolation or tortuosity) of pores. Both laboratory measurements (e.g. Beard & Weyl 1973) and theoretical analysis (e.g. Carman 1956) indicate that permeability is more sensitive to pore size than porosity. Pressure, cementation, grain size, clay content, sorting and irreducible water saturation affect permeability indirectly by modifying or controlling the five key parameters mentioned above. Porosity can be determined

reasonably accurately from well logs but there are no direct measurements of pore size, shape and connectivity. Consequently, permeability prediction has to rely on indirect measures of these parameters. The danger of resorting to purely ad hoc empirical relationships is that they can end up eclectically tuned to a particular dataset. The effect of clay content on permeability has long been recognized. Thompson & Callanan (1981) measured porosity and permeability of synthetic clay samples at pressures in the range 0 to 10000 psi. Although the measured porosities were high (in the range of 20% to 50%), the permeabilities are 3 to 4 orders of magnitudes lower than those measured from artificial sand packs by Beard & Weyl (1973). The low permeabilities were explained as a result of the remarkable sealing power of clay particles. Thomson (1978) observed a progressive decrease in permeability with clay content from rock samples with clay content in the range of 5% to 15%. A number of authors observed a linear trend between log(k), the logarithm of permeability, and porosity 4) which was later explained as a result of a systematic reduction in both k and 4 by dispersed clays (Bos 1982). From laboratory measurements on over 100 shaley sand samples, Goode & Sen (1988) found a good

XU, S. • WHITE,R. 1998. Permeability prediction in anisotropic shaly formations. In- HARVEY, P. K. 8z LOVELL, M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 225-236

225

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S. XU & R. WHITE

correlation between log(k) and log(qSm/Qv), where m is the cementation factor and Qv the exchange cation molarity. Sen et al. (1990) measured Qv , the inverse surface-to-volume ratio (Vp/S), proton N M R decay time T1 and permeability k of some 100 sandstone core samples and found good correlations between k and log(q~mVp/S), log(q~m/Qv) and log(~bmT1). This is understandable since Vp/S, Qv and T1 are three different measures of clay content. The authors concluded that clay affects the permeability more in rocks where it adheres in pore throats than in those where it adheres in the pore pockets. Although considerable understanding of how clay affects permeability has been gained from laboratory measurements, little theoretical work has been done to model the effect. The majority of the models that relate permeability to clay content combine empirical and simple logical considerations (e.g. permeability must be dimensionally length squared). Starting with the Kozeny-Carman equation (Carman 1956), de Lima (1995) obtained some relationships between k, 49, Vp/S and Qv similar to those obtained empirically by Sen et al. (1990). We have developed a model for elastic wave velocities (Xu & White 1995a,b,c, 1996a) which can accurately simulate the combined effects of porosity, clay content, fluid content and frequency on elastic wave velocities in clastic silicate rocks. The model is founded on physical concepts and has demonstrated its practical utility in a number of case studies involving reservoir geophysics (formation evaluation, seismic modelling and interpretation). In addition to validation on published laboratory measurements, the model has been successfully tested on numerous wells, including four blind tests (Xu et al. 1997). The key feature of the model is its ability to predict the effect of clay content on wave velocities, including the two distinct porosityvelocity trends observed in the laboratory: one for shaly sands and the other for sandy shales (Marion et al. 1992). Empirical models that treat clay content purely as a lithological factor cannot explain this. Laboratory measurements and well logs indicate that two factors need to be considered; lithology and the influence of clays on pore compliance. Not only are clay particles more compliant than sand grains, their sheetlike nature tends to make the pore space more compliant. This greater compliance can be modelled by introducing an additional pore space characterized by a smaller aspect ratio than that of clean sand grains. Thus the model predicts the higher Vp/Vs values observed for

shales than sands. The elastic anisotropy of shaly formations is modelled through a preferred orientation for clay-related pores. Here we extend the model to predict the conductivity and permeability of shaly formations. Each pore is assigned an idealized ellipsoidal shape and embedded in a porous medium. We use Willis' (1977) formulae to compute its contribution to the overall conductivity and permeability. The interaction between this pore and other pores is modelled via the concept of a host medium. The properties of the host medium are then tuned to model the observed conductivity and permeability. The concept of the host medium was originally proposed by de Kuijper et al. (1995). In Xu & White (1996b) we show that alternatives such as the self-consistent scheme (SC) and the differential effective medium scheme (DEM) cannot model with Archie's law for clean sands whereas modelling with a host medium hunting technique reproduced Archie's Law when the fluid percolation factor was selected as 0.04q~. The next section describes the model which is then tested using published laboratory measurements. The results show that the transport properties of anisotropic shaly formations can be modelled by assigning a characteristic pore size, shape and orientation to their clay fraction. The model provides a permeability predictor that estimates a percolation factor from resistivity measurements, if available, and then applies it to permeability prediction.

The unified model for shaley formations As in the elastic model of Xu & White (1995a,b, 1996a), we assume that the total pore space can be divided into sand-related pores and clayrelated pores. The pore space is partitioned proportionately:

where Vc ~bc = _---~b 1

(2)

4,s = 4 - 4~c.

(3)

and

Vc is fractional clay content. The sand-related pores are characterized by a pore aspect ratio (ratio of short semi-axis to long semi-axis) C~s and pore size (long semi-axis) as and the clayrelated pores are similarly assigned a characteristic pore aspect ratio O~c and pore size ac. We

PERMEABILITY PREDICTION further postulate that the sand-related pores are randomly oriented whereas the clay-related pores tend to align themselves in a plane. This assumption conforms with observations of strong seismic and ultrasonic anisotropies for shales and isotropic wave propagation in clean sandstones. In modelling logs, Vc is generally replaced by shale volume Vsh, which would lump the fractional volume of silts and various mineral fragments with Vc. The model could in principle take account of these different components and different clay minerals if there were practical log analysis procedures distinguishing them.

227

five phases: 1. a non-conducting solid phase; 2. a clay mineral phase with a finite but very small conductivity; 3. a randomly oriented sand-related pore fluid phase with conductivity Cwe; 4. a clay-related pore fluid phase with the same pore fluid conductivity Cwe but with a preferred pore orientation; 5. a non-conducting hydrocarbon phase. The percolation factor Fc for conductivity is defined as in equation B1 in Appendix B:

CH= FcCwe+(1-Fc) Cm

Conductivity In simulating electrical conductivity special consideration must be given to the electrochemical behaviour of clays. Waxman & Smits (1968) demonstrated that shaley sands behave as perm-selective cation exchange membranes and their electrochemical efficiencies increase with increasing clay content. They modelled this by supplementing water conductivity Cw with a conductivity Ce from the clay counter-ions within the ionic double layers:

Ce = BQv

(5)

where /~eNa is the maximum (sodium) cation exchange ion mobility (in cm 2 Volt 1 s), b and 7 are empirically determined constants. Waxman & Smits (1968) found from their laboratory measurements that B = 1 - 0 . 6 e x p ( - Cw/0.013)]0.046

(6)

where Cw is in ohm m q. The effective conductivity of the formation water Cwe is simply the sum of Cw and Ce. Cwe = Cw + Ce

where Cn, Cwe and Cm denote the conductivity tensors of the host medium, formation water and matrix (mixture of the sand grains and clay particles). Fc describes the degree to which the fluid paths accord with a parallel tube model; it decreases with increasing tortuosity of the fluid paths. There is no information as to the value of Fc and we estimate it from the measurements. Fc can be correlated with other measured parameters, such as q~ and Qv, to see what factors control it.

(4)

where Qv is the molar volume concentration of clay exchange cations per unit pore volume (meq cm 3). Qv is a function of the cation exchange capacity (CEC) of clay minerals, clay content, porosity and the density of dry clays. B is the equivalent conductance of clay exchange cations (in ohm cm 2 meq 1) which is a function of the conductance of formation water Cw. At 25 ~ B = [1 - b e x p ( - Cw/~/)]0.0 l AeNa

(8)

(7)

In order to apply the modified Willis' formulae (equations A5-A11) to the conductivity of shaly sands, we subdivide the formation into

Permeability In simulating permeability, there is a problem in defining the intrinsic permeabilities of the inclusions. One approach is to start from the intrinsic permeability of two parallel plates: be k = -12

(9)

where b is the separation between the two plates. For a spheroidal inclusion defined by xZ/a~2+ y2/ a2+ z2/c 2= 1, equating the hydraulic aperture bh parallel to its long axis with the mean square value of the separation 2z gives: bh2 = 1 .fjs (2z)2dS = -~o~2a 2 = 2C2

(10)

where A=Tr a 2 is the area of the domain S defined by x 2 + y 2 = a 2 and a is the aspect ratio of the inclusion (c/a). For the intrinsic permeability of the inclusion parallel to its long axis we use k = 1__a2a2 = ~1c - ,~. 6

(11)

Strictly this should be a tensor property. A similar equation, but with an undetermined

228

S. XU & R. WHITE

numerical constant, is obtained from simple dimensional arguments. Since in practice a bestfit characteristic value is assigned to c, the numerical constant of 1/6 has no real significance. Unlike conductivity, the permeability of a porous rock does depend on pore sizes, especially pore throat diameters. The intrinsic permeabilities for sand- and clay-related pores are taken from equation 11 1

2

9

ks = gO~s as-

(12)

kc = ~1 ac 2ac2.

(13)

and in porosity from 5% to 31%, making it an ideal dataset for testing the model. Electrical conductivity Applying the model to conductivity measurements has two aims: (1) to test its capability of predicting electrical conductivity, and (2) to estimate a percolation factor for each sample for use in permeability prediction.

and

We introduce a separate percolation factor Fp for permeability since the relative weighting of permeability is not necessarily the same as that for conductivity. For example, a clay particle in a pore throat still conducts electricity but seriously impedes fuid flow. By employing a modified Voigt-Ruess-Hill average scheme, we define Fe for permeability as follows: kH = 0.2kll + 0.8k~, kll = Fpks + (1 - Fe)kc, k ~ -1 = Fpks < + (1 - Fp)kc q

(14)

where kH, ks and kc are permeabilities of the host medium, sand-related pores, and clay-related pores. The equation signifies that when the sandrelated pores are selected as the host medium, the system is most percolating and when the clay-related pores are selected, the system is least percolating. To apply Willis' formulae to permeability, the composite is assumed to consist of three phases: 1. an impermeable matrix phase of sand grains and clay particles; 2. a sand-related pore phase with a random pore orientation; 3. a clay-related pore phase with a preferred pore orientation.

Application to laboratory measurements The dataset The model was tested on the laboratory measurements published by W a x m a n & Smits (1968). The dataset provides the porosity, brine permeability, Qv and conductivities at four or more brine salinities of 49 sandstone samples (table 2 in Waxman & Smits 1968). The samples range in clay content from clean to very shaly,

The Waxman-Smits (WS) and the D u a l Water (DW) models (Clavier et al. 1984) were also tested on the dataset for comparison. To simulate electrical resistivity, all three models require porosity, clay content and brine conductivity (Cw) as input parameters. As there were no direct measurements of clay content, we estimated it from Qv using a relation given by Juhasz (1979): V~l(drv) = 9

Qvq~t

(15)

Pcl (dry) C E C c l

where Vcl(dry) denotes the dry clay content as a fraction of bulk volume, Pcl(dry) denotes the average density of the mixture of dry clay minerals (in gcm 3), 4t is the total (fractional) porosity and CECd is the averaged cationexchange capacity of the clay minerals present in the formation (meq gq dry clay). When applying the WS and D W models, the formation factor FF in both models was tuned to get the best fit. It is well recognized that FF is controlled by porosity and cementation factor which is, in turn, a function of pore geometry and tortuosity of the electrical current flow paths. Hence FF is expected to vary from sample to sample. When applying our model to the dataset, the percolation factor Fc was tuned by fitting the predictions with the conductivity measurements. Figure 1 compares sample measured electrical conductivities with predictions from the three models. All three work well for clean sandstone (upper figure). Our model worked slightly better than the WS and D W models for shaley sandstone (lower). The normalized mean square errors of fit (termed incoherence by de Kuijper et al. 1995) for all 49 samples is shown in Fig. 2. Our model (lower) fits the data slightly better than the D W (upper) and WS (middle) models. Figures 3 and 4 show the cross plots of the estimated percolation factor Fc as a function of porosity and shale volume. There are two welldefined trends on the Fc-q~ cross plot: one for

PERMEABILITY PREDICTION

229

25

~E20

[

1

v

"! 15

%,

0 1;

1;

2'0

10 lid 0

3;

35

4'5 50 55

40

Sample Number

5 0

25

50

100

150 200 (mS/cm)

7 250

Conductivity of brine

_AA

0

A

/o ,; 2o 25 3; 3'5 20 ~5 5; 5; Sample Number

A

57

"64 0

o .~ 2

/o t'5 2'0 2'5A3'o 35 4'0 ;

5; 5;

Sample Number

nl

0

0

50 100 150 200 Conductivity of brine (mS/cm)

250

Fig. 1. Comparisons between the measured electrical conductivities (solid squares) and those predicted using the WS (dash
Fig. 2. Normalized mean square error of fit (termed incoherence by de Kuijper et al. 1995) for the DW (upper), WS (middle) and unified (lower) models 9

FC versus porosity

-10 a

100.~ 604',,

~

3o9

shaley sands (upper trend) and one for sandy shales (lower trend). There is only one trend on the Fc-Qv plot, indicating that clay content plays a more important role in controlling percolation of the fluid phase.

Permeability Figures 5 to 7 show cross plots of permeability versus porosity, Qv and formation factor. Permeability is clearly affected by all three factors. There are two distinct trends on the k4~ and k - F F plots but apparently just one on the k-Qv plot. Of the many models in the literature for predicting permeability, the most popular are the Kozeny-Carman equation and multilinear and non-linear regression techniques. The K o z e n y - C a r m a n e q u a t i o n ( C a r m a n 1956) is based on a tube-like model of the pore paths in a rock. Flow through a porous medium is represented by a bundle of tubes of different radii. Within each tube, the flow is laminar rather than turbulent. The tubes are also

9

,mmI " 9

mt m |m 9 9

10-

9

9

6-

31

0.0

oi,

o12

o13

o14

Porosity (fractional) Fig. 3. Cross plot of the estimated percolation factor Fc as a function of porosity. assumed to be twisted with tortuosity 7-= La/L, where La is the assigned length of a tube and L is the length of the sample. Under these conditions, the Kozeny-Carman equation becomes k-

~bRh2

(16)

fTwhere R h is the mean hydraulic radius a n d f i s a dimensionless shape factor between 1.7 and 3. If R h is related to the specific surface area S, defined as the ratio of pore surface area to grain

230

S. XU & R. WHITE P e r m e a b i l i t y versus F F

Fc versus Qv - 1 0 -3 100-

9

&

~"..~

6o-

. .. 9 ii m

~9

30-

102 -

= ~ ' ~ 1 /n ~ 9 9 9

9 9 me

9

====

.~

10 ~

"'%

10-

9

6-

o==

9

~.

"

10_2 3-

1

I

006

0;

l O -4

I

o16 ,o

io

4'O

10

r

0

'

100

200

Fotenation F a c t o r

Qv (meq/ml )

Fig. 4. Cross plot of the estimated percolation factor Fc as a function of shale volume.

Fig. 7. Cross plot of the measured permeability as a function of formation factor.

volume, the equation becomes Permeability versus porosity

~)3

k= 9 "

102--

t:.

To apply the KC model to this dataset, we used the empirical equation given by Clavier el al. (1984) to estimate specific area S from Or:

g .~

(17)

f r S 2 ( 1 - ~b)2.

10 ~

9

el

9

102

10 ~

o9

o11

o12

o13

o14

Porosity (fractional) Fig. 5. Cross plot of the measured permeability as a

function of porosity.

where v is a constant, which was determined from laboratory measurements as 450 m 2 meq -1. Figure 8 shows the cross plot of the measured permeability and that predicted from the KC equation. Tuning the constants in the K o z e n y Carman equation will shift the graph upwards or downwards but will not change the scatter. The reason for the scatter is probably that the tortuosity and shape factor in the KC equation are assumed to be constant during the calcula-

Permeability versus Qv Measured versus predicted 10 -~ 102-

.-~ ,-.,.,

9 "":-:'k-'."

10 -2

10 ~

102 .....................................................................................

102

ii--...e ....... 102 ~o

o

10 ~

==

==

10' ...................................

%.

9

10-2-

0 mi

~ . . - ' - ......... ~ ..................... i ................. 10~

.'. : :'r 9

1 0 : ..................................................................................................

10 2

10

10 ~

10' I

0.06

0 I

91

013

0'.6

l'.0

3.0

Qv (meq/ml)

Fig. 6. Cross plot of the measured permeability as a function of volume concentration of clay exchange cations Qv, an indicator of clay content 9

10"

10 2

10 ~

10:

Measured permeability (roD)

Fig. 8. Cross plot of the measured permeability and that predicted using the KC model.

PERMEABILITY PREDICTION Factor F P versus Factor F C

Predicted v e r s u s measured 101~0~

10 -~

10 ~

----.

"~

102 . ...................................................................

10 2 i

10' 10"

~.~#.. .................... 102

"-.'i" 10 o ...............................; . . ;

.~

10 ~

-

-

"~

10"-

,.~

l O 2--

10 .2

"

BBB 9

i 9

9

10 -~_

10 ~

10 -6 10 -4

"l t

10~_

10 ............. | ........ ~.i,.~.. 9....................................................

10"

...

10.~ -

.........'.....'...~....~............................10 ~

t "-"

~"

231

10 ~

10 2

I

I

3

6

I

lO - 1 0 -~

)~0"~

Measured permeability (mD)

30

60

100

F a c t 9 f r o m conductivity data

Fig. 9. Cross plot of the measured permeability and that predicted from a mixed multilinear/non-linear regression.

tion. In reality they are a function of clay content and porosity and vary from sample to sample. Multilinear and non-linear regression techniques are widely used for permeability prediction. These techniques rely on correlation with measurements for which there is some plausible connection to permeability without necessarily investigating the basis of the correlation or the physics behind the equations. For multilinear regression, we assumed that logt0(k) was a linear function of porosity, Vsh and formation factor (FF). In looking for a nonlinear relationship, we regressed lOgl0(k) on log10(4~), lOgl0(Vsh) and lOgl0(FF) and various linear and non-linear combinations between log]0(k) and the input parameters. The following relationship gave the best result:

Fig. 10. Cross plot of Fc the percolation factor estimated from the conductivity data and Fp the percolation factor estimated from the permeability data.

Predicted versus measured

I~,T'

10~

10o

102

q ,,...,_

10~0,

ioe 10 .................................................................

9 9149 pe

e.e~,..~ ................... 10 2

9 9

10 ........................ i.............. ~ ........ i.................................................

i:

10 ~

~

10 -~ ...................... ..",........................................................................

lOio~,

10 .2

10 ~

10 ~

10 ~

1

04

Measured permeability (roD) Fig. 11. Cross plot of the measured permeability and that predicted using the unified model tested.

lOgl0(k) = - 0.76 + 2.851og10(~b)- 2.921og10(Vsh)

+ 0.01FF.

(19)

Figure 9 shows the cross plot of the predicted and measured permeabilities on a log-log scale. The prediction of permeability using the unified model requires characteristic pore sizes and aspect ratios for sand-related and clayrelated pores. Applications of the model of Xu & White (1995a) to published laboratory measurements and well log data indicate that % is approximately 0.12 and c~c is about 0.03. as and ac are largely determined by the sizes of sand grains and clay particles. The best fit as is about 0.25 mm which is within the range of grain sizes for sandstones (0.0625 mm to 2.0 mm) and the best fit ac is about 0.00005 mm which is within the range for shales (less than 0.00309 mm). The percolation factor Fp for permeability was first

determined from the permeability measurements and then correlated with Fc, the percolation factor found from the conductivity measurements (Fig. 10). For this particular dataset, we obtained the following empirical relationship. logl0(Fp) = 3.91 + 4.971og10(Fc).

(20)

Figure 10 shows an interesting phenomenon. For clean sandstone (high permeabilities) the magnitude of Fp is of the same order as that for Fc. But as clay content increases Fp decreases much faster than Fr indicating a much stronger effect of clay content on Fp than Ft. In other words, despite the similarity of the concepts of percolation for conductivity and that for permeability, they can differ in magnitude. The phenomenon can be related to the way that clay

232

S. XU & R. WHITE

particles affect electrical conductivity and permeability. As we discussed earlier, clay particles adhering in a pore throat may block the fluid flow path, whereas for electrical current flow, wet clay particles are effectively conductive. In other words, conductivity and permeability respond differently to clay content. Equation 20 was applied to the permeability prediction. The results are shown in Fig. 11, which should be compared with Figs 8 and 9. The unified model predicts permeability better than the KC model and multiple regression. It benefits from predicting the permeability percolation factor Fp from the percolation factor Fc determined from conductivity measurements. Application of the model to another dataset (Sen et al. 1990) for which no conductivity measurements were available showed only a slight improvement over the empirical models given by Sen et al.

Discussion

low concentrations, clay particles tend to be dispersed in the sand pores and their orientation and that of their micro pores is mainly controlled by the orientation and geometry of the original sand pores. As a result, there is initially no obvious increase in anisotropy with increasing clay content. Once the sand pores are filled by clay particles to the extent that the sand grains become separated, the clay particles become load-bearing and they and their micro-pores are aligned by overburden pressure due to their sheet-like nature. This results in a dramatic increase in anisotropy. The importance of the effect of clay on permeability can be seen from the strong correlation between permeability and clay content. More accurate prediction of permeability calls for: (1) a thorough understanding of the major factors controlling permeability; (2) developing models of its relationships with these factors; (3) a strategy for estimating the key parameters in practical applications.

It is well known that clay content has a major influence on the elastic and transport properties of sedimentary rocks but its effects are often complicated. The following are possible mechanisms.

The following show our concerns on these issues.

(1) Its effect on porosity. Because of their small size, clay particles tend to fill the pore space between sand grains as they are progressively introduced into the system. This reduces porosity. Once the sand pore space is filled by clay particles and their microporosity, the sand grains will be suspended in clay particles. Porosity then starts to increase with increasing clay content (Marion et al. 1992). (2) Its effect on pore geometry. The introduction of clay particles reduces pore sizes and, at the same time, creates micro-pore spaces between clay particles. This reduction in pore size affects permeability significantly more than conductivity. (3) Its effect on tortuosity. Pore throats are likely to be bridged or blocked by clay particles. This increases the tortuosity (or percolation threshold) of the fluid flow paths. Again this affects conductivity to a lesser degree than permeability since wet clay particles are effectively conductive. (4) Its effect on pore orientation and anisotropy. P- and S-wave velocity anisotropies in sedimentary rocks are strongly correlated with clay content (Shams et al. 1993). At

sensitive to many factors ranging from major, through moderate to minor but these factors are rarely linked to one another in a physical way--for example, linking porosity and clay content, grain size and pore size. To predict permeability accurately, it is essential to identify the key factors controlling fluid flow and, if possible, then relate other factors to the key ones. Published laboratory measurements and theoretical studies both suggest that permeability can be modelled in terms of the following five key factors: the sizes, shapes, orientation and interconnectivity of the gaps, cavities or pore space, and the porosity. There is scope to include other known factors, such as clay content, consolidation, cementation, pressure, and so on, indirectly through the way they modify the key five factors. The model described in this paper is a first attempt at this approach but we have found no published datasets other than that of Waxman & Smits on which to test our model. Further progress in predicting permeability requires more comprehensive experiments on representative suites of

(1) It is well known that permeability is

PERMEABILITY PREDICTION rock samples in order to compare and calibrate models. Measurements are needed of conductivity, permeability and elastic wave velocities, and their anisotropies from the same set of samples. (2) There is the question of what kind of model is best suited to predicting permeability. Our approach is to look for a physical basis for the prediction rather than the dubious practice of playing with variables and functions in the melting pot of multiple regression. The Kozeny-Carman model appears to be the most widely used physical model of permeability and, when adapted to use with N M R logs, this can predict permeability in clean sands very well (Fletcher et al. 1996). A major assumption of this model is that the total fluid flow is the sum of flows in individual tubes. Since this model, or any parallel flow model, is dominated by large tubes, it does not appear to be well suited to modelling permeability in clay-rich rocks. A model based on inclusions may also be more capable of accounting for the well-documented rapid decrease in permeability with an initial rise in differential pressure than a tube-based model. This phenomenon is commonly considered to be due to the closure of microcracks or flat gaps that act as channels between big pores. Our model is an inclusion-based model which embeds pores into a permeable host medium and, with the aid of Willis' formulae, relates permeability to pore shapes, sizes and orientation distribution. It tries to integrate permeability and conductivity measurements. Its use of pore aspect ratios connects the transport properties model to one employed in modelling elastic wave propagation. In the elastic wave modelling, pore aspect ratios provide a way of specifying pore compressibility but it is far from certain how relevant they are to modelling transport properties. Although one can postulate a connection with pore throat parameters, the connection is admittedly tenuous. However, there is a benefit in seeking an integrated model since fitting different measurements helps constrain its parameters better. (3) More accurate prediction of permeability needs detailed information about the pore space. Porosity itself is not a problem since it can be measured directly in the laboratory and estimated with a reasonable accuracy from logs. Since permeability is sensitive to the second power of pore size,

233

its determination is crucial. Currently there is no log that provides pore size information on a regular basis although in the laboratory something closely related can be obtained by mercury injection or image processing techniques. However, recent studies relating the N M R relaxation time and pore size may change this situation and it appears that N M R logs can improve permeability prediction considerably (e.g. Sen et al. 1990). The tortuosity or percolation of the fluid flow paths is normally estimated from resistivity measurements provided porosity is known. As we mentioned above, the tortuosity for electrical conductivity and that for permeability can be very different in magnitude. Consequently, tortuosity estimated from conductivity measurements should not be applied to permeability prediction without calibration. We use shale volume, a measurable parameter, as an indicator of pore geometry parameters and as an indicator of anisotropy. This use of shale volume is only a first order approximation in the absence of better measures from logs. Similarly, discussion of adapting the pore space specification to take account of variations from sedimentary environment, sorting, and cementation is premature until sufficient calibrating information becomes available to provide permeability prediction with a sound footing. Although the application of pore shape parameters (aspect ratios) estimated from velocity measurements to the prediction of transport properties is questionable, the model does highlight the potential of a unified physical model to integrate different measurements and it does simulate the effect of clay on permeability better than commonly used alternatives. This is important in view of the abundance of clay minerals in sedimentary rocks and their strong influence on permeability. Another advantage of the unified model over the commonly used conductivity and permeability models is the capability of predicting anisotropic rock properties. Anisotropy due to both aligned minerals and aligned pores can be modelled using Willis' formulae. The capability has been demonstrated (Xu & White 1995c) on a dataset containing porosity, clay content, P- and S-wave velocities measured at directions parallel and perpendicular to bedding of 68 sandstone samples (Shams et aL 1993).

234

S. XU & R. WHITE

Conclusions (1) There is a strong correlation between clay content and permeability. (2) We have d e v e l o p e d a unified effective m e d i u m model for simulating the electrical conductivity and permeability of anisotropic shaley formations. The effect of clay is modelled by means of pore parameters (size, shape, orientation) and the W a x m a n & Smits electrochemical model. (3) The model predicts electrical conductivity measurements slightly better than the wellk n o w n W a x m a n - S m i t s and the D u a l Water models when it is reduced to the isotropic case. (4) Permeability is m o r e difficult to predict than conductivity and elastic velocities since it is effected by more factors. In the case where resistivity measurements were available, the model simulated the permeability m e a s u r e m e n t s better than other models tested. In the case where there were no conductivity measurements, it worked at least as accurately as existing models. (5) The percolation factor determined from c o n d u c t i v i t y m e a s u r e m e n t s is different from that determined from permeability measurements in magnitude, probably due to the different ways in which clays affect permeability and conductivity. Wet clay usually acts as a barrier for fluid flow but a conductor for current flow. We are indebted to the sponsors of the London University Research Programme in Seismic Lithology, Amoco (UK) Exploration Company, Elf UK plc, Enterprise Oil plc, Fina Exploration Ltd, Mobil North Sea Ltd and Texaco Britain Ltd, for their support of this research. We thank B. Moss of Moss Petrophysics Ltd for helpful comments in his review of the paper.

References BEARD, D. C. & WEYL, P. K. 1973. Influence of texture on porosityand permeability of unconsolidated sand. American Association of Petroleum Geologists Bulletin, 57, 349-369. Bos, M. R. E. 1982. Prolific dry oil production from sands with water saturation in excess of 50%--a study of a dual porosity system. In: SPWLA 23rd Annual Logging Symposium, paper BB. CARMAN, P. C. 1956. Flow of gases through porous media. Academic Press Inc., New York CLAVIER, C., COATES, G. & DUMANOIR, J. 1984. Theoretical and experimental bases for the DualWater model for interpretation of shaley sands. The Society of Petroleum Engineers Journal, 24,

153-168. DE KUIJPER, A., SANDOR, R. K. J., HOFMAN, J. P.,

KOELMAN,J. M. V. A., HOFSTRA,P. & DEWAAL,J. A. 1995. Electrical conductivities in oil-bearing shaley sand accurately described with the SATORI saturation model. In: SPWLA 36th Annual Logging Symposium, Paper MM. DE LIMA, O. A. L. 1995. Water saturation and permeability from resistivity, dielectric, and porosity logs. Geophysics, 60, 1756-1764. GOODE, P. A. & SEN, P. N. 1988. Charge density and permeability in clay bearing sandstones. Geophysics, 53, 1610-1612. FLETCHER,J. D., COWPER,D. R. & HARDWICK,A. 1996. Analysis of reservoir quality using magnetic resonance logs for exploration and appraisal west of Shetlands. In: Expanded Abstracts of the 55th EAGE Meeting, Amsterdam, Paper E047. JUHASZ, I. 1979. The central role of Qv and formationwater salinity in the evaluation of shaley formations. In: SPWLA 12th Annual Logging Symposium, paper AA. MARION, D., NUR, A., YIN, H. & HAN, D. 1992. Compressional velocity and porosity in sand-clay mixtures. Geophysics, 57, 554-563. SEN, P. N., STRALEY,C., KENYON,W. E. & WHITXlNGHAM, M. S. 1990. Surface-to-volume ratio, charge density, nuclear magnetic relaxation, and permeability in clay-bearing sandstones. Geophysics, 55, 61--69. SHAMS, M. K., KING, M. S. & WORTHINGTON, M. H. 1993. Whitchester seismic cross-hole test sitepetrophysical studies of cores. In: Expanded Abstracts of the 55th EAGE Meeting, StanvangeT, Norway. THOMPSON, L. J. & CALLANAN, M. J. 1981. Overpressured marine sediment, Volume 1 - The prediction of hydrofracture and k0 during drilling. Texas A&M University, College Station, Texas, Project No. RF 3956. THOMSON,A. 1978. Petrography and diagenesis of the Hosston sandstone reservoirs at Bassfield, Jefferson Davis County, Mississippi. In: Transactions, Gulf Coast Association of Geological Societies, 28, 651-664. WAXMAN, M. H. & SMITS, L. J. M. 1968. Electrical conductivities in oil-bearing shaley sands. The Society of Petroleum Journal, 8, 107-122. WILLIS, J. R. 1977. Bounds and self-consistent estimates for the overall properties of anisotropic composite. Journal of Mechanics & Physics of Solids, 25, 185-202. Xv, S. & WHITE, R. E. 1995a. A new velocity model for clay-sand mixtures. Geophysical Prospecting, 43, 91-118. & 1995b. Poro-elasticity of clastic rocks: A unified model. In: Transactions of the 36th Annual SPWLA Symposium, Paris, Paper V. - &- 1995c. Comparison of four schemes for modelling anisotropic P-wave and S-wave velocities in sand-shale systems. In: Expanded Abstracts of the 57th EAEG Meeting, Glasgow, UK, Paper B2. - &- 1996a. A physical model for shearwave velocity prediction. Geophysical Prospecting, 44, 687-717.

PERMEABILITY PREDICTION - -

&- 1996b. Modelling transport properties of anisotropic formations. In: Expanded Abstracts of 66th SEG Annual Meeting, Denver. --, DOORENBOS,J., RAIKES, S. & WHITE, R. E. 1997. A simple but powerful model for simulating elastic wave velocities in clastic silicate rocks. In: LOVELL, M. A. &; HARVEY,P. K. (eds) Developments in Petrophysics. Geological Society, London, Special Publications, 122, 87-105.

Appendix A: Willis formulae Using the Hashin-Shtrikman variational principle, Willis (1977) derived a formula for calculating the generalized Hashin-Shtrikman bounds for a composite medium containing perfectly aligned multi-phase inclusions.

L = s crZr[l+ eo(tr--Zo)] -1 r=l

{s

-1

(A1)

r=l where Cr is the concentration of phase r, Lr is the property tensor (elastic, conductivity or permeability tensor) of phase r, I is the unit tensor, L0 is the property tensor of a hypothetical host medium having vanishing volume, and P0 is a tensor which is a function of pore geometry and the properties of the host medium. Willis demonstrated that if the most conductive phase is chosen as the host medium, one gets the upper bound for the effective conductivity whereas one gets the lower bound if the least conductive phase is chosen. The same applies to elasticity, permeability and thermal conductivity. For any physical property, the true response lies between the Hashin-Shtrikman extremes. In terms of equation (A1) the porosity enters the response through the concentrations cr and the pore geometry through the tensor P0. For spheroidal inclusions aligned perpendicular to the x3 axis,the composite is transversely isotropic. The host medium L~ may conseq u e n t l y be specified as d i a g o n a l , with L~ =L~ . The Po tensor for conductivity or permeability is also diagonal, with Pll =P22 (Willis 1977), and A2 (e2L033. . f A + 1 '~ Pl1=2--~101{1-2--~101 a l n L A _ l j }

where

A = (LOl L~ _ _e2L~ 3 ) 89

L=

crAr(O,~) r=l

crBr (0, fl)

(A5)

r=l

where

Aij) (0,/3 r

= jof27rf ~Wr(O,/3)KimKjnAmn(O,O sin( O)dOd/3,

B~(O,/3)= f27r f~Wr(O, /3)gim/~nBrn(0,

(A6)

0)

sin( O)dOd/3,

(A7)

Ar(0,0) = Lr[I+ P0(Lr - L0)] -1,

(A8)

B r(0,0) = [I+ P0(Lr -- L0)] -1.

(A9)

and

0 is the angle between global )(3 axis and local x3 axis and /3 is the azimuth, the angle between global X1 axis and the projection of the local x3 on the global XIOX2 plane. Kij is the matrix which transforms A r and B r from local coordinates to global co-ordinates: Kij =

e2A2 { 1 (A+I) P33= LlO---~ ~Aln\A_I

(A4)

and e is the ratio of long semi-axis to short semiaxis of the inclusions. One gets the self-consistent approach if L0 in equation (A1) is replaced by L and Po by P. This physically means that the effective medium itself is selected as the host medium. Willis' formulae are ready to apply to a composite containing perfectly aligned inclusions. Real pore fabrics are often aligned within a certain range of directions. The orientation distribution may be described by, say, a normal distribution. In this case, we calculate Lr[I+ Po (Lr - L0)]-1 and [I+ P0 (Lr - Lo)]-1 in a local coordinate system with x3 axis perpendicular to the inclusions and then transform from local coordinates to global co-ordinates. Equation (A1) can be rewritten as

(A2)

and

235

cos(0) cos(/3)-sin(/3) sin(0)cos(/3)-1 cos(0) sin(/3) cos(/3) sin(0)sin(/3) / - sin(0) 0 cos(/3) J (A10)

} --1

..

(A3)

Wr(0, /3) is the orientation distribution density

236

S. XU & R. WHITE

function for phase r such that f

f ~Wr(0,/3)sin(O)dOd/3 =1.

(All)

In our case the resulting effective medium is transversely isotropic. Thus w r is independent of azimuth/3 and equations (A6) and (A7) can be integrated explicitly over /3. This considerably speeds up the calculations.

Appendix B: The host medium and the percolation factor Xu & White (1996b) simulated the electrical conductivity of clean sands (two-phase medium) using various effective medium approaches and the results were compared with the lower and upper bounds from the generalized HashinShtrikman formulae. In the numerical simulation, pores and sand grains were assumed to be randomly oriented so that the effective properties are isotropic. It was found that both the selfconsistent and differential effective medium schemes provided results within the bounds but departed significantly from Archie's law for resistivity. This indicates serious problems in applying these schemes to simulate the effective properties of a composite with large conductivity or permeability contrasts among the phases. The problems with these effective medium models can be avoided by using the concept of a host medium. A host medium is a starting phase with no volume, representing a background

connectivity or percolation. Other phases have no inherent connectivity or percolation and are embedded in the host phase (de Kuijper et al. 1995). When brine (the most conductive phase) is selected as the host phase, Willis' approach gives a parallel-resistor model whereas it gives a series-resistor model when sand (the least conductive phase) is the host phase. Really the conducting phases are neither purely parallel nor purely series but somewhere between the two extremes. SC uses the effective medium itself as the host medium and consequently introduces too high a percolation threshold (de Kuijper et al. 1995). For clean sands, we assume that the property tensor of the host medium LH is a function of the property tensors of the two phases and a percolation factor F, L H = F LF+(I.O--F) Ls

(B1)

where LF and Ls denote the conductivity or permeability tensors of the fluid phase and solid phase, respectively. The percolation factor F describes the degree to which the fluid paths accord with the parallel model. It is therefore reasonable to call it a connectivity or tortuosity factor. F must be in the range of 0.0 to 1.0. When F is 0.0, the sand is the host phase and when F is 1.0, the fluid is the host phase. Willis' formulae apply at these upper and lower bounds; we apply them in between by selecting a host medium that matches observations. The formulae approach Archie's law when F=0.04 ~b, where 4 is porosity.

The integration of electrical image logs with core data for improved sedimentological interpretation T. M . G O O D A L L 1, N. K. M O L L E R 2 & T. M . R O N N I N G S L A N D

2

1Rider-French Consulting Cambridge Ltd, at Production Geoscience, North Deeside Road, Banchory, Kincardineshire AB31 3YR, UK (Present address: Production Geoscience Ltd, North Deeside Rd, Banchory, Kircardineshire AB31 5YR, UK) 2 Norsk Hydro ASA, P. O. Box 200, N-1321 Stabekk, Norway

Abstract: Electrical borehole images allow the direct integration of sub-surface well-log data with core data on a detailed visual level. For sedimentary interpretation, electrical borehole images are primarily used to obtain bedding orientations and to confirm core-derived sedimentology. The aims of this paper are two-fold: firstly, to discuss how the sedimentological information provided by electrical borehole image logs is integrated with that obtained from other wireline logs and from cores; and secondly, to show that despite the need to integrate these data electrical borehole images can provide the geologist with unique sedimentological information which can not be obtained from either the cores or the other wireline logs. In a case study from the Oseberg Syd oil and gas field, a Fullbore Formation MicroImager (FMI Mark of Schlumberger) log through a complex interval of shallow marine sediments has been investigated. The interpretation of the FMI log led to the acquisition of very detailed orientation data related to the attitudes of sedimentary bedding surfaces. When these data were integrated with the sedimentary facies, identified from core description, they proved fundamental for understanding the activity of different shallow marine palaeocurrents during sediment deposition.

Electrical image logs produce a 'picture' of the formation which allows geologically-trained interpreters to identify complex sedimentary structures within the well bore (e.g. crossbedding, bioturbation etc.). This kind of detailed geological information was previously only obtainable from cores. Whilst core provides the geologist with a physical sample of the formation, electrical image logs provide a unique dataset which, in particular, can be used to derive extremely detailed information on the orientation (dip magnitude and direction) of sedimentary structures which cut-across the borehole (Serra 1989). Good quality image logderived orientation data can be superior to that gathered by any other method, even outcrop measurements. Acquiring an image log, however, does not preclude the need to take core or vice versa; these data are complimentary. In an example from the Middle Jurassic sands of the Norwegian North Sea in the Oseberg Syd (South) oil and gas field it is shown that the integration of orientation data from electrical borehole image logs allied to both core data and other open hole logs has greatly enhanced the sedimentological understanding of the reservoir.

Fig. 1. A schematic diagram to show the differences in coverage of a 21.59cm borehole by the 4-pad and 8pad electrical imaging tools and by a whole core (after Adams et al. 1990 and Bourke 1992). Electrical image acquisition and processing The electrical imaging tool that is discussed in this paper is Schlumberger's Fullbore Formation MicroImager (FMI). The tool consists of four pads fixed to two orthogonal arms. The four pads each have a hinged flap to extend the area of electrical contact. The F M I tool can be run either in 8-pad mode, using both the pads and flaps, or in 4-pad (or FMS) mode, where only the four, main pads are used (Fig. 1). The raw data are sampled by an array of button electrodes on the tool's pads and flaps which, in the case of the FMI, collectively

GOODALL,T. M., MOLLER, N. K. & RONNINGSLAND,T. M. 1998. The integration of electrical image logs 237 with core data for improved sedimentological interpretation In. HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 237-248

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T. M. GOODALL ET AL.

generate 192 microresistivity curves. These curves form a matrix which is processed to produce a coherent, colour image. Under optimum conditions the F M I images can resolve features in the borehole down to 0.5cm (Schlumberger 1994), but it is more usual for F M I images to have a resolution of 2-3cm (Rider 1996). The quality of these microresistivity data collected by electrical image tools is, as with any other resistivity log, dependant on both the mud filtrate resistivity and the borehole conditions. Poor electrical images may result from the borehole being off-gauge, as a result of caving or borehole breakout. Electrical images can also contain both acquisition and processing 'artefacts' (Bourke 1989). Care must be taken to identify image artefacts otherwise there is a danger that they can be misinterpreted as geological features. A more detailed explanation of F M I acquisition and processing can be found in Schlumberger (1994) and Rider (1996).

Fig. 2. Illustration of the difficulties of matching both whole core and core slabs to borehole images (from Rider 1996).

Core and electrical images: the datasets compared Spatial position In a 21.59cm diameter borehole the F M I tool samples the formation from a circumferential surface area of 67.8cm, whilst in a 31.12cm diameter borehole the F M I tool samples the formation from a circumferential surface area of 97.8cm. Whole core in both 21.59cm and 3 1 . 1 2 c m d i a m e t e r b o r e h o l e s is t y p i c a l l y 10.16 cm in diameter, making the circumference of the core 32 cm (12.6"). Therefore, when direct visual comparisons are made between electrical image logs and whole or slabbed core it is important to appreciate that they are from different parts of the wellbore (Figs 1 and 2).

Geophysical differences of electrical borehole images and core photographs As a consequence of the spatial differences described above, problems can arise when comparing core data, particularly core photographs, with electrical borehole images. Spatial differences between these two sets of data are often not obvious over intervals with planar surfaces dipping at low angles relative to the borehole. Over intervals which have planar surfaces bisecting the borehole at high angles (Fig. 2), however, there can be depth offsets of

Fig. 3. The principle of static and dynamic normalisation. Static normalization can be used to compare images over an entire well. Dynamic normalization is used to bring out local detail. A full colour scale is used for limited data range or 'window' which can be from any chosen interval such as a bed of interest, or a preset small depth range. Note that for the purposes of this figure the colour scale is represented as blackgrey-white scale (from Rider 1996). up to 1.8 m due to the differences in sampling diameter between the two techniques (Adams et al. 1990).

DATA INTEGRATION FOR IMPROVED SEDIMENTOLOGICAL INTERPRETATION The comparison between core photographs and electrical borehole images is inevitable and often very important. However, it is necessary to appreciate not only the spatial differences but also that electrical borehole images are computer generated, based on measurements of electrical resistivity from around the borehole wall and therefore not directly comparable to core photographs. Electrical images can be displayed using two types of colour designation, one where the colour range covers the entire resistivity (or conductivity) population for the logged interval (static normalization) and the other where the sampled population is limited to the resistivity values over a specified depth interval (dynamic normalization) (Fig. 3). The two types of image display are complimentary for comparison with core data: dynamic normalization is used for detailed comparisons of sedimentary structures, whilst static normalisation is preferred for correlating lithological or facies changes at compressed vertical scales (e.g. 1:100 or greater).

Reasons for integrating core and electrical image data Feature recognition from image logs Core provides an indispensable tool for the calibration (mainly qualitative) of image logs. Core calibration can be applied in two ways: firstly, to establish the identity of poorly resolved, individual features on the image log (e.g. ripple laminae, different grain-size textures and bioturbation); and secondly, to determine if characteristic changes in image texture (on metre or decimetre scales) correspond to changes in lithological type or characteristic sedimentary facies (e.g. ripple-bedded sands or vuggy carbonates). Once calibrated to core these same features and image textures, when identified outside the cored interval, may be interpreted with added confidence.

Depth matching cores with other well data The image log provides a continuous record of the cored interval with respect to depth. This means that the image log can be used to accurately match the core to log depth and to identify if there are any intervals where core material may have been lost or damaged during the coring process. Once the core has been corrected to log depth then not only can the core be used to calibrate the image log but also the

239

other wireline logs. This may be particularly useful to check if computer generated lithological reconstructions from wireline logs for entire logged sections are at least accurate within the cored intervals.

Sedimentary orientation data from cores and electrical images In reservoir rocks which contain important palaeotransport (palaeocurrent/palaeowind) information (e.g. cross-bed orientations) it is critical that these orientation data are measured accurately. In order for core material to provide such information it needs to be correctly oriented relative to true North. Core orientation, however, can be prone to error (Nelson et al. 1987), particularly in highly deviated or horizontal wells (Skopek et al. 1992). If electrical image logs are available then their orientation information can be vastly superior and effectively replaces the need to reorient the core. The process of drilling a borehole through a cross-bedded, sedimentary formation exposes these structures in 3-dimensions. The orientation of such features can be recorded from borehole image logs and subsequently the image-derived dip data provide a unique record of internal bedform geometries. Although the principle of deriving the orientation of sedimentary structures using image logs is well-known (Serra 1989) the quality and quantity of dip data that can be obtained from these logs is not always fully appreciated. Where there is a sufficient resistivity contrast between cross-bed foresets, accurate dip measurements can be taken on a bed-by-bed scale (ca every 5-10cm) and the bounding surfaces can also be identified. This may allow the interpreter to identify complex bounding surface hierarchies in order to reconstruct cross-bed architecture. The level of detail that image log-derived dip data provide, can allow interpretations to be made of the depositional processes and subsequently the determination of local palaeocurrent directions and more regional palaeotransport trends. On an unwrapped FMI image (from a vertical well) horizontal sedimentary laminae appear as flat surfaces, whilst dipping foreset structures appear as sine waves (Fig. 4). The sine wave amplitude indicates the dip magnitude, and the low point of the wave describes the dip direction (or azimuth). An interpreter can interactively match a sine wave to the sedimentary structure shown on the image to derive a very accurate dip and azimuth value (Adams et al. 1990). Once the interactively picked dip data have been collected

240

T.M. GOODALL E T AL.

Fig. 4. Representation of borehole wall images on a fiat surface (from a vertical well). The images derived from the cylindrical borehole (a) are presented on a flat surface (computer screen or hard copy log plot) by 'unwrapping' onto a vertical depth grid and horizontal grid of compass bearings. (b) In this format, horizontal and vertical surfaces are unchanged but dipping surfaces become represented by a sinusoid. (c) Such dip and azimuth may be represented on a dipmeter tadpole plot (from Rider 1996).

it is possible to classify each dip according to the sedimentary feature that it represents. After the dips have been classified the dip types can be interrogated separately using statistical analyses (i.e. eigenvector methods) or they can be displayed on stereograms or rose azimuth plots. Interactively picked image log orientation data are often superior to dip data that can be derived by any other conventional method: outcrop measurements, core goniometry and dipmeter logs (i.e. the Schlumberger Stratigraphic High Resolution Dipmeter Tool or SHDT log). The reasons are outlined below:

for these data to be interpretable the dips need to be filtered, identified and classified. These steps cannot be achieved without integration of the dipmeter log with core sedimentology and standard log data.

Case study: electrical image log-derived sedimentary orientations from the Middle Jurassic Tarbert Formation, Oseberg Syd oil and gas field (Norwegian North Sea) Introduction

(1) Palaeocurrent data from outcrop analogue studies tends to be limited to a few orientations where the geologist has confidence in his/her measurements. The character of natural exposures is such that most sedimentary structures can only be observed, at best, in 2-dimensions. Subsequently it is usually difficult to take a high number of dip measurements on a density comparable to image log-derived dip data. (2) Core goniometry is a technique which measures the dip magnitude and dip direction of continuous, planar surfaces identified on the outside surface of whole, oriented core. Although the principle is sound it is dependant on whether the core can be correctly oriented and depth corrected. It is also necessary for the outersurface of the core to be smooth and clean enough to reveal any of its internal structures. It also has to be sufficiently coherent to allow measurements to be taken. (3) Dipmeter tools record a large amount of dip data. However, the geological or sedimentary origin of these recorded dips cannot be determined in isolation. In order

The Oseberg Syd oil and gas field lies within Block 30/9 on the Norwegian Continental Shelf, around 120 km west of Bergen (Fig. 5). Structurally, the region is characterized by elongate fault blocks which form a series of terraces between the Horda Platform and the Viking Graben. The main fault-planes strike N / N N W S/SSE. The reservoir sands are Middle Jurassic in age and comprise shallow marine sand bodies from the upper part of the Brent Group (Tarbert Formation) and fluvio
DATA INTEGRATION FOR IMPROVED SEDIMENTOLOGICAL INTERPRETATION

Fig. 5. Location map for the Oseberg Syd oil and gas field (from Fristad

sists of a number of cleaning-upward successions (Fig. 8). The changes in shallow marine facies types suggest that during the deposition of each succession the water became progressively shallower until there was an abrupt change to deeper water marking the base of the next, overlying succession. The sedimentary facies at the base of each succession are characterized by outer through inner shelf muds and silts, passing into sand-dominated shoreface and beach/coastal plain sediments. These gradationally-based successions are diagnostic of prograding shorefaces, produced by either wave and storm-dominated shoreface processes (Fig. 9a), tidally-dominated shoreface processes (Fig. 9b) or a combination of both storm and tidally-dominated processes. Identifying which processes were dominant during sediment deposition is important because

et al.

241

1997).

it would have had a significant control over the resulting sand body architecture. The core sedimentology suggested that the Tarbert sands were deposited in a shallow marine, shoreface environment but the evidence was equivocal regarding which depositional processes were dominant (Fig. 8). Subsequently, palaeotransport orientation data from electrical image and dipmeter logs from six wells were studied in order to assist with the Tarbert sand body characterization. For the purposes of this paper, FMI (in 4-pad mode) image-derived orientation data from only one well will be considered in detail. In Well 30/9-16 (Figs 5 & 7) the Tarbert Formation has been cored throughout, allowing full integration of the FMI image log with the sedimentary facies and corresponding sedimentary structures seen in the core.

242

T.M. GOODALL ET AL.

Fig. 6. Jurassic stratigraphy of the Norwegian Sector of the Northern North Sea (after Bowen 1992).

Sedimentary interpretation methods for the electrical image log-derived orientations from Oseberg Syd Palaeotransport (palaeocurrent) indications within the shallow marine sands of the Tarbert Formation in Well 30/9-16 were based on dipping, internal sedimentary surfaces identified from the electrical images. These included: highangle cross-bed foresets, hummocky cross-stratification (HCS) and low angle cross-bed foresets. The slabbed core allowed the image based measurements to be positively identified as coming from an interval with corresponding sedimentary structures. These dip and azimuth data derived from the electrical images were fully integrated not only with the core logs but also with the standard well logs and the orientations were extracted. These orientation data were then rotated to remove regional structural dip using the average values sampled from a structural analysis of orientation data from representative shale intervals of Middle Jurassic age (in the same well). Structural dip was carefully calculated and tested several times before the orientations were finally rotated. This was necessary because inaccurate structural dip rotation can produce a false, preferred orientation to the sedimentary orientations. These data were re-examined in their original sedimentary position and generally low angle dips, below 5~

Fig. 7. A palaeogeographic model showing the distribution of sedimentary facies during the deposition of the Tarbert Formation in the vicinity of the Oseberg Syd field (after Fjellanger et al. 1996).

were filtered out. The low angle dips were removed because they are not as reliable as the higher angle dips for deriving cross-bed orientations in sandstones (Cameron et al. 1993). The resultant azimuth rose diagrams were then considered to represent the best indication of cross-bed orientations. Although high-angle foresets were assumed to give the best source of sedimentary orientation information, the HCS and low angle foresets also had structures with consistent dip azimuths. These consistent dip azimuth values only became evident when orientation data from individual sand bodies were analysed in detail. Examples of the sedimentary palaeotransport indicators identified from dynamically normalized borehole images in Well 30/9-16 are given below.

Foresets. Foresets are not common in the Tarbert sands. They are generally recognized on the images as having well-marked lamination with

DATA INTEGRATION FOR IMPROVED SEDIMENTOLOGICAL INTERPRETATION GAMMARAY API

v~" 1.7 BULKyDENSITY ~ 15C ~

[cm3

~ 2.:

CORESEDIMENTOLOGY

O

G R A I N SIZE &

O NEUTRON POROSITY ~ 60 % a -~ . . . . . . . . . . . . . . .

SEDIMENTARYDEPOSITIONAL STRUCTURESENVIRONMENT i m u d st vf f m c v c

~

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243

~

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--floo~i,,9 v

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O~176 f

:: ::--------YJr~/'~

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SEDIMENTARYSTRUCTURES Cross~ Bioturbation stratification ~ : ~ Horizontal ~ Rootlets lamination Ripple [ ~ - ] Pebbles lamination Waveripple ~ Hummockycrosslamination stratification

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LITHOLOGY Coal ~

Sandstone

Silt

Conglomerate

~

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Cemented horizons

Fig. 8. The wireline log responses of the gamma ray and density-neutron combination through part of the Tarbert Formation in well 30/9-16, Oseberg Syd field. The integration of the core sedimentology demonstrates that the cleaning-up gamma trends correspond to upward coarsening and upward shallowing of the facies.

10~ to 20 ~ dip angles with unimodal azimuth variations. The designated image log colour ranges show little variation within sets but there are marked colour changes at set boundaries (Fig. 10a).

Hummocky cross-stratification (HCS) and low angle foresets. The core reveals that in the Tarbert Formation, low angle foresets and HCS are the most common sedimentary structures. The image log of both structures is similar

and shows pervasive, fine lamination. Some HCS intervals show low dips with random orientation, whilst others show low dips with a definite preferred azimuth (Fig. 11). Within the intervals of HCS, two d o m i n a n t types of stratification can be distinguished from the image logs. Firstly, there are the less abundant higher-angle erosion surfaces within HCS co-sets and secondly, there are the individual laminae which usually drape these erosion surfaces (Figs 11 & 12). The erosion surfaces are described as second-order bounding surfaces whilst the in-

T.M. GOODALL ET AL.

244

A

B Outer shelf muds

Outer shelf muds oOoOQOoOo

Oo o o

Marsh

Coal / Backshore I

_/~.~..A.~

Beach

///'v".

9' "

~ 9 ."

9 . .

"

Breaker zone ridge and runnel/ rip channels

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9

bar complex

".'.',.-,.~ .

.

~.~

.

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Jl

.

. . . .

. . . . . . .

. . . u

/~

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U~ , ~

Mid-shelf

-

bioturbated sandy

Mid-shelf bioturbated sandy siltstone

siltstone

Outer-shelf bioturbated

5m

mudstone

Outer-shelf bioturbated mudstone

Fig. 9. The progradation of clastic shorelines leads to distinctive gradationally-based, coarsening-upward successions. (a) Wave/storm-dominated shorelines are characterized by a gradual shallowing of sedimentary facies from outer through inner shelf deposits (with abundant HCS) into sand-dominated shoreface and beach sediments (after Walker & Plint 1992). (b) Tidally-dominated shorelines are also characterized by a gradual shallowing of sedimentary facies. However, the sands contain tidally-generated sedimentary structures (after Selley 1985). For key to sedimentology see Fig. 8.

dividual laminae are separated by third-order bounding surfaces (Cheel & Leckie 1993).

Sedimentary facies interpretation from the core The standard open hole logs (gamma ray, neutron and density logs) and the interpreted core sedimentology, linked to the electrical images in Well 30/9-16, were used to identify the sedimentary facies relationships within the Tarbert Formation (Figs 8 & 13). In Well 30/916 the Tarbert F o r m a t i o n comprises three cleaning-upward successions, which have been divided into the Upper, Middle and Lower Tarbert Formations. The Upper Tarbert For-

mation and part of the Middle Tarbert Formation are shown in Fig. 8. Cross-stratified intervals are associated with the upper half of the coarsening-up sand and silty sand successions. Foreset angles tend to be low (10~ ~ and channelization is rare. The lower part of the coarsening-up sand and silty sand successions usually contain ripple laminae and HCS.

Lower Tarbert Formation. (2799.5-2819.0m) the Lower Tarbert Formation in Well 30/9-16 consists of a gradationally based wave-dominated shoreface succession passing from lower shoreface silts into the wave-rippled, silty sands of the shoreface and beach sediments. HCS are absent.

DATA INTEGRATION FOR IMPROVED SEDIMENTOLOGICAL INTERPRETATION

245 ~ o,,...~

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o

~Z

Q . ,...~

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T.M. GOODALL ET AL.

246

Fig. 11. Dynamically-normalized4-pad FMI image log with interpreted sedimentary orientation data through an interval of hummocky cross-stratification (HCS). The integration of the schematic core photograph and image derived sedimentary orientation data demonstrates that second-order surfaces (shown in red) and third-order surfaces (shown in blue) within HCS can be discerned.

Hummock

2 ~'-~

",-...

~

3

Sole m a r k s 1 - Third-order

surface

2 - Second-order 3 - First-order

surface

surface

Fig. 12. The form of stratification and first-, secondand third-order bounding surfaces commonly found in scour and drape hummocky cross-stratified sandstone beds (from Cheel & Leckie 1993).

consists of thin sands, containing HCS, which are interbedded with silts. This type of succession, comprising interstratified silts and sharp based sands displaying HCS, is diagnostic of the lower shoreface/inner shelf transition (Fig. 9a). The interval reflects the alternation of fairweather siltstone deposition with storm lain sands between the fair-weather and storm-wave bases.

Upper Tarbert Formation. (2718.0-2739.0m) (Fig. 13)--the Upper Tarbert Formation shows a gradational transition from lower shoreface deposits into sand dominated mesotidal shoreface facies which pass upwards into beach and coastal plain sediments. The lower shoreface contains intervals of amalgamated HCS sandstones (Leckie & Walker 1982), whilst the mesotidal shoreface sands contain the higher angle cross-stratification.

Middle Tarbert Formation (2739.0-2799.5m)-the Middle Tarbert Formation in Well 30/9-16 comprises a coarsening upward, wave-dominated shoreface succession overlain by an erosive lag deposit marking the base of the Upper Tarbert Formation. The lower part of the Middle Tarbert Formation in Well 30/9-16

Interpretation o f electrical image-derived orientation data South-easterly orientations are dominant within the Tarbert Formation and they are mainly derived from the HCS and low angle foresets

DATA INTEGRATION FOR IMPROVED SEDIMENTOLOGICAL INTERPRETATION

247

Fig. 13. The 4-pad FMI image log with interpreted sedimentary orientation data through the Upper Tarbert Formation in well 30/9-16, Oseberg Syd field. The integration of the core and image derived sedimentology demonstrates that the prograding shoreface succession was produced by a combination of both storm- and tidally-dominated processes. For key to sedimentology see Fig. 8.

(Fig. 13). Although HCS would not be expected to have preferred orientations recent work has suggested that some hummocky cross-stratified sands may indicate preferred orientations related to unidirectional flow elements being dominant over the more usual, oscillatory flow during deposition of these shoreface sediments (Nottvedt & Kreisa 1987; Johnson & Baldwin 1996). The dip and azimuth of both erosion surfaces (first and second order surfaces) and laminae (third order surfaces) within the HCS can be accurately derived from electrical image logs (Fig. 11). These dip data from HCS are unique and cannot be obtained from surface exposures where the apparent hummocky nature of the laminae precludes the manual measurement of dip and azimuth by the geologist. The Upper Tarbert, shoreface sands in Well 30/9-16 contain cross-bedding (Fig. 13). The cross-bedding in core did not contain tidallyrelated sedimentary structures. However, imagederived orientation data from this interval indicate a bimodal dip azimuth oriented N W SE. The opposing palaeocurrent directions are interpreted to be from cross-stratification produced by both onshore flood (SE) and offshore ebb (NW) directed currents indicating deposi-

tion within a tidal inlet, which formed part of a mesotidal shoreface (Fig. 7).

Conclusions (1) Although the integration of core data with electrical image logs does lead to improved sedimentological interpretations of subsurface reservoirs it should be appreciated that there is not only a distinct difference in the amount of formation that is sampled by the imaging tools compared to the core, the direct comparison of the two sets of data involves coping with marked physical differences of depth and spatial position. (2) In a case study from Oseberg Syd, electrical image log-derived orientation data from the Tarbert reservoir sands were integrated with core data. Some of the sedimentary facies were difficult to characterize when they were first studied in core. The subsequent palaeocurrent interpretations derived from the electrical image logs provided unequivocal evidence for the correct identification of these sedimentary facies. (3) Electrical image-derived dip data cannot

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only assist in the interpretation of subsurface sediments, they are arguably of superior quality to orientation data that can be obtained by any other method. It is suggested that image-derived dip data may be used in future studies to provide important information regarding sedimentary bedform architecture. For example, the analysis of these orientation data obtained from cross-stratified units compared to their internal bounding surface (set boundaries) orientations can provide important clues for determining the dominant processes that were present during deposition. (4) In order to assist with the sub-surface sedimentological interpretation of electrical image-derived dip data, it is recommended that detailed, sedimentary orientation data should be collected from both well exposed rocks and from cores of modern sediments. These analogue, orientation datasets would provide models to help identify distinctive dip relationships, which might prove to be diagnostic of certain sedimentary facies or depositional processes.

The authors gratefully acknowledge Norsk Hydro ASA and their partners in Oseberg Syd: Conoco (Norway) Inc., Mobil Exploration Norway Inc., Saga Petroleum A/S and Statoil Oil Co. for permission to use data from well 30/9-16. M. H. Rider offered many useful discussions in the development of ideas during the writing of this manuscript. The software PC ImagePro (BPB Wireline Technologies Ltd) was used to produce the borehole images in Figs 10, 11 & 13.

References

ADAMS, J. T., BOURKE, L. T. & BUCK, S. G. 1990. Integrating formation images and cores. Sclumberger Oilfield Review, 2, 52-65. BOURKE,L. T. 1989. Recognizing artifact images of the Formation MicroScanner. Society of Professional Well Log Analyists 30 th Annual Logging Syposium, Denver, Transactions, Paper WW. - 1992. Sedimentological borehole image analysis in clastic rocks: a systematic approach to interpretation. In: HURST, A, GRIFFITHS, C. M. & WORTHINGTON, P. F. (eds). Geological Applications of Wireline Logs II. Geological Society, London, Special Publications, 65, 31-42. BOWEN,J. M. 1992. Exploration of the Brent Province. In: Morton, A. C., HAZELDINE,R. S., GILES,M. R. & BROWN, S. (eds) Geology of the Brent Group. Geological Society, London, Special Publications, 61, 3-14.

CAMERON,G. I. F., COLLINSON,J. D., RIDER, M. H. & Xu, L. 1993. Analogue dipmeter logs through a prograding deltaic sandbody. In: ASHTON,M. (ed.) Advances in Reservoir Geology. Geological Society, London, Special Publications, 69, 195-217. CHEEL, R. J. & LECKIE,D. A. 1993. Hummocky crossstratification. In: WRIGHT,V. P. (ed.) Sedimentary Review, 1, 103-122. FJELLANGER, E., OLSEN, T. R. & RUBINO,J. t . 1996. Sequence stratigraphy and palaeogeography of the Middle Jurassic Brent and Vestland deltaic systems, Northern North Sea. Norsk Geologisk Tidsskrift, 76, 75-106. FRISTAD, T., GROTH, A., YIELDING,G. & FREEMAN,B. 1997. Quantative fault seal prediction--a case study from Oseberg Syd. Hydrocarbon seals importance for exploration and production. Norwegian Petroleum Society (NPF) Special Publication, 7, 107-124. JOHNSON,H. D. & BALDWIN,C. T. 1996. Shallow clastic seas. In: READING, H. G. (ed) Sedimentary Environments: Processes, Facies and Stratigraphy. Third Edition, Blackwell Science, 265-266. LECKIE,D. A. & WALKER,R. G. 1982. Storm- and tidedominated shorelines in Late Cretaceous Moosebar-Lower Gates interval--outcrop equivalents of deep basin gas trap in western Canada. Bulletin of the American Association of Petroleum Geologists, 66, 138-157. NELSON,R. A., LENOX,L. C. & WARD, B. J., Jr 1987. Oriented core: its use, error and uncertainity. Bulletin of the American Association of Petroleum Geologists, 71, 357-367. NflTTVEDT, A. & KREISA, R. D. 1987. Model for the combined-flow origin of hummocky cross-stratification. Geology, 15, 357-361. RAvNAs, R., BONDEVIK, K., HELLAND-HANSEN,W., LOMO, L., RYSETH, A & STEEL, R. J. 1997. Sedimentation history as an indicator of rift initiation and development: the late Bajocian Bathonian evolution of the Oseberg - Brage area, Northern North Sea. Norsk Geologisk Tidsskrift, 77, 205-232. RIDER, M. H. 1996. Image logs. The Geological Interpretation of Well Logs. 2nd Edition, Whittles Publishing, 199-225. SCHLUMBERGER, 1994. FMI Fullbore Formation MicroImager. Schlumberger Educational Services. SELLEr, R. C. 1985. Ancient Sedimentary Environments. 3rd Edition, Chapman & Hall, London. SERRA. O. 1989. Formation MicroScanner Image Interpretation. Schlumberger Educational Services. SKOPEK,R. A., MANN,M. M., JEFFERS, D. & GRIER,S. P. 1992. Horizontal core acquisition and orientation for formation evaluation. Drilling Engineering, Society of Petroleum Engineers, March, 4754. WALKER, R. G. & PLINT, A. G. 1992. Wave- and storm-dominated shallow marine systems. In: WALKER, R. G. & JAMES, N. P. (eds) Facies models: response to sea level change. Geological Assocation of Canada.

How to characterize fractures in reservoirs using borehole and core images: case studies D. H A L L E R 1 & F. P O R T U R A S 2

1Elf Petroleum Norge as, P.O. Box 168, N-4001 Stavanger, Norway 2 Western Atlas Logging Services, P.O. Box 953, N-4040 Hafrsfjord, Norway Abstract: Microconductivity array and acoustic imaging of the borehole wall provide valuable multidatasets which are used to characterize the geological strata and especially the reservoirs in exploration activities and during development of producing zones. This paper presents a tutorial of the main applications and a methodology to follow when performing fracture interpretation. Borehole image interpretation should not be a routine work referring to 'a cook book'. It must rely on our geological and structural knowledge and experience, on basic notions about tool principles and image processing, and on geometrical calibrations to cores. Vatious examples will be given, showing: (a) natural fractures; how to distinguish them from drilling-induced fractures; (b) typical drilling-induced fractures and borehole breakouts; how to identify them; (c) a tricky case where cemented fractures might be confused with open ones. The match between interpretation of borehole images and production data appears now to be the most efficient way to manage fractures in reservoirs.

Fractures may affect reservoir behaviour in a drastic way. When open, they act as pathways for hydrocarbon production and may even transform a very low permeability reservoir into a highly productive zone. When cemented, they act as barriers to hydrocarbon flow, hindering the motion of hydrocarbons toward the well. In the case of a fault being sealed, either due to clay smearing, cementation or cataclasis, it can lead to compartmentalization of the reservoir with different pressure regimes, water tables or even fluid types in each individual panel. Therefore, identification and characterization of fractures have been always a major concern for reservoir geologists. All the information about fractures comes from wells. Fractures can be either observed directly on cores or inferred from wireline or production logs. Fracture detection from wireline logs has always been something speculative, until the middle of the eighties, when high resolution borehole images broke through on the market. They opened a new perspective for fracture characterization in reservoirs and gave birth to a new geological discipline: the ability to identify fractures and to characterize them, especially with regard to their influence on the reservoir behaviour. This requires both experience with fractures, based on field work and rock mechanics, and good understanding of the physical principles governing the functioning of

the logging tool. In other words, this discipline is not a simple exercise based on cookery books; it belongs to the world of geological interpretation, which can always provide tricky cases, where a quick-look interpretation seems so obvious but which may be proven to be definitely wrong. This will be elaborated on, based on several case study interpretations of high resolution borehole images dealing with fractures. All of them are taken from wells drilled in the Norwegian Continental Shelf. Not all the presented cases display phenomena of prime importance for the considered case study, but they all deal with problems which might be fundamental in other circumstances.

Fracture characterization using well data through the past Fracture identification and characterization was first conducted on cores, because cores are an ideal medium to observe fractures, where one can easely see if they are open or healed with diagenetic minerals or alternatively smeared or injected with sedimentary material. It is possible to measure the open width of the fractures and sometimes to observe slickenslides on their planes, which can indicate direction of tectonic motion. One can try to define fracturation frequency, based on simple mathematical means.

HALLER,D. & PORTURAS,F. 1998. How to characterize fractures in reservoirs using borehole and core images: case studies In: HARVEY,P. K. 8z LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 249-259

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But what remains more difficult to define is their orientation, since the orientation of the core itself is not preserved when bringing the corebarrel to the surface. In the past, a specific technique has been implemented in order to determine orientations of the fractures. For each segment of core pieces which could be matched together, all geological elements, both bedding and fractures, were oriented. This was achieved by 'unrolling' the core surface and plotting this elements on a stereonet. The re-orientation of the fractures was obtained by two consecutive rotations on stereonet, the criteria being to fit the bedding measured on cores with the bedding attitude deduced from dipmeter log. This technique was laborious, rather inaccurate, and it had its own limitations, when bedding could not be observed (e.g. massive limestones) or when bedding is perpendicular to the borehole axis. In a more general way, studies on cores have their own limitations too. Most of the time the reservoir is not cored entirely and by no means all wells are cored. Moreover, in the case of the well intersecting a fracture corridor, the core recovery might be drastically affected by lack of coherence of the rock. It therefore ends up with the situation where the zone of highest interest for fracture analysis is lost for observation. Besides cores, indirect means have also been used to identify fractures acting significantly in the drainage of a reservoir, such as identification of mud losses, open-hole injectivity tests with flow-meter and temperature surveys, pressure transient analysis, all three of which allow definition of the producing fractures, in the case of a low permeability matrix ( H a l l e r & Hamon 1993). However, it has always been evident that some specific tools were needed to achieve proper observation of fractures in reservoirs. But fracture intersection by a borehole being most of the time less than a centimetre thick, is really a tough challenge for logging tools, whose

resolution is most of the time above one centimetre. Besides the array acoustic tools whose processing of Stonely waves allows location of fracture zones but not the orientation of the fractures, it is obvious that only highresolution tools can really identify the elementary fractures. Therefore, the first attempts to identify fractures were made using conventional, four, six or eight electrode dipmeter recordings. Erratic peaks observed on one track with no lithological continuity on the other tracks were considered to correspond to fracture intersection and it was tempting to identify them automatically, the aim being the definition of fracture intensity. This approach was considered to be unreliable and anyway did not provide any orientation. So, when the borehole images appeared on the market, it was as if the geologist had gained the capacity for 'seeing' the fractures inside the reservoirs. These tools blended the characteristics required: high resolution retative to fracture size and high sampling density in giving an image of the borehole wall which allows the orientation of the fractures to be determined.

Description of the new imaging tools There are two main types of tool for acquisition of images, one based on microconductivity arrays and the other on acoustic imaging methods, both providing imagery of high resolution in the order of millimetres. Both types of tool currently operate on single passes and are equipped with similar orientation units, such as accelerometers and magnetometers which give navigation data for proper fracture geometrical orientation. The microconductivity array tools can carry between 16 and 32 buttons which are mounted on pad carriers, with 4, 6 or 8 arms. The amount of data acquired is huge and has influenced further development of new telemetry and data

Fig. 1. Example of natural open fractures developing in a brittle carbonate cemented layer (example 1). The FMI image (left-hand) shows mainly two steeply dipping conductive fractures (in black), whose extent stops sharply at the edge of the carbonate layer. This is typical of a fracture developing in a brittle layer interbedded with less porous brittle rocks. Interpretation of these fractures as natural open ones is sensible. Unrolled core photograph (right-hand) zoomed on this carbonate layer proves that this interpretation is correct. One large natural open fracture is observed; in the fracture plane, calcite crystals have been seen and strong hydrocarbon shows have been reported, indicating that this fractures acted as hydrocarbon pathways through this tight level. Fig. 2. Example of natural open fractures developing in a brittle carbonate rock (example 2). The CBIL amplitude image (left-hand) and core-like display (right-hand) show a dark sinusoidal feature at xx83.4 m (a). The minimum of the sinusoid shows an easterly dip. The fracture was interpreted as an open one and fits well with drilling losses. Note also a low reflective (black) fracture (b) subparallel to borehole axis, trending NNE-SSW, interpreted as a drilling-induced one. The well is deviated and the lowside of the borehole is revealed by the marks of the previous logging tools (c). The CBIL image was acquired in an oil-based mud.

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But what remains more difficult to define is their orientation, since the orientation of the core itself is not preserved when bringing the corebarrel to the surface. In the past, a specific technique has been implemented in order to determine orientations of the fractures. For each segment of core pieces which could be matched together, all geological elements, both bedding and fractures, were oriented. This was achieved by 'unrolling' the core surface and plotting this elements on a stereonet. The re-orientation of the fractures was obtained by two consecutive rotations on stereonet, the criteria being to fit the bedding measured on cores with the bedding attitude deduced from dipmeter log. This technique was laborious, rather inaccurate, and it had its own limitations, when bedding could not be observed (e.g. massive limestones) or when bedding is perpendicular to the borehole axis. In a more general way, studies on cores have their own limitations too. Most of the time the reservoir is not cored entirely and by no means all wells are cored. Moreover, in the case of the well intersecting a fracture corridor, the core recovery might be drastically affected by lack of coherence of the rock. It therefore ends up with the situation where the zone of highest interest for fracture analysis is lost for observation. Besides cores, indirect means have also been used to identify fractures acting significantly in the drainage of a reservoir, such as identification of mud losses, open-hole injectivity tests with flow-meter and temperature surveys, pressure transient analysis, all three of which allow definition of the producing fractures, in the case of a low permeability matrix ( H a l l e r & Hamon 1993). However, it has always been evident that some specific tools were needed to achieve proper observation of fractures in reservoirs. But fracture intersection by a borehole being most of the time less than a centimetre thick, is really a tough challenge for logging tools, whose

resolution is most of the time above one centimetre. Besides the array acoustic tools whose processing of Stonely waves allows location of fracture zones but not the orientation of the fractures, it is obvious that only highresolution tools can really identify the elementary fractures. Therefore, the first attempts to identify fractures were made using conventional, four, six or eight electrode dipmeter recordings. Erratic peaks observed on one track with no lithological continuity on the other tracks were considered to correspond to fracture intersection and it was tempting to identify them automatically, the aim being the definition of fracture intensity. This approach was considered to be unreliable and anyway did not provide any orientation. So, when the borehole images appeared on the market, it was as if the geologist had gained the capacity for 'seeing' the fractures inside the reservoirs. These tools blended the characteristics required: high resolution retative to fracture size and high sampling density in giving an image of the borehole wall which allows the orientation of the fractures to be determined.

Description of the new imaging tools There are two main types of tool for acquisition of images, one based on microconductivity arrays and the other on acoustic imaging methods, both providing imagery of high resolution in the order of millimetres. Both types of tool currently operate on single passes and are equipped with similar orientation units, such as accelerometers and magnetometers which give navigation data for proper fracture geometrical orientation. The microconductivity array tools can carry between 16 and 32 buttons which are mounted on pad carriers, with 4, 6 or 8 arms. The amount of data acquired is huge and has influenced further development of new telemetry and data

Fig. 1. Example of natural open fractures developing in a brittle carbonate cemented layer (example 1). The FMI image (left-hand) shows mainly two steeply dipping conductive fractures (in black), whose extent stops sharply at the edge of the carbonate layer. This is typical of a fracture developing in a brittle layer interbedded with less porous brittle rocks. Interpretation of these fractures as natural open ones is sensible. Unrolled core photograph (right-hand) zoomed on this carbonate layer proves that this interpretation is correct. One large natural open fracture is observed; in the fracture plane, calcite crystals have been seen and strong hydrocarbon shows have been reported, indicating that this fractures acted as hydrocarbon pathways through this tight level. Fig. 2. Example of natural open fractures developing in a brittle carbonate rock (example 2). The CBIL amplitude image (left-hand) and core-like display (right-hand) show a dark sinusoidal feature at xx83.4 m (a). The minimum of the sinusoid shows an easterly dip. The fracture was interpreted as an open one and fits well with drilling losses. Note also a low reflective (black) fracture (b) subparallel to borehole axis, trending NNE-SSW, interpreted as a drilling-induced one. The well is deviated and the lowside of the borehole is revealed by the marks of the previous logging tools (c). The CBIL image was acquired in an oil-based mud.

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transfer systems. The image coverage around the borehole varies with the bitsize of the drill. The basic operating principle of the tools consist in applying an alternating exciting voltage between the top electrode and the imaging electrodes located on the arms of the tool. An electric current proportional to the formation conductivity flows through the formation and is measured by each electrode (Serra 1989; Safinya et al. 1991). A few tools have extra built-in powered standoffs to maximize sensor-to-wall contact and centralization, particularly when operating in highly deviated wells. The acoustic imaging tools are mostly using rotating transducers and are operating in a pulse-echo mode, allowing simultaneous acquisition of both amplitude and travel time to the borehole wall. This scanner rotates from 6 to 12 revolutions per second and provides a full coverage of the borehole wall, regardless of the borehole diameter. Furthermore the travel time image represents an ultra sensitive 360 degrees borehole caliper. The diameter of the rotating transducers varies from 0.6 to 5cm. Acoustic tools have the advantage of operating also in oilbased mud, but they require low to moderate mud density because the solids in heavy muds hamper the ultrasonic wave propagation. A new generation of integrated imaging tools record the two images in one pass, by simultaneous electrical and acoustic illumination of the borehole wall. These integrated tools acquire twice as much information and provide a much more comprehensive borehole description of the formation.

Case studies

Example 1. Identification of natural open fractures on Fullbore Microlmager (FMI)* The first example is taken from a development well drilled in the Brent Middle Jurassic sandstone reservoir located in the Viking Graben. Inside the reservoir, carbonated layers have been encountered and the question has been raised concerning their barrier efficiency as far as production concerned. F M I logging having been run in this well, the analysis was carried out on these images, supported by core observation for

some intervals. On the F M I image (Fig. 1), it is observed that steep dipping conductive fractures develop inside the carbonated layer. One particular point to be noticed is that fractures stop sharply at the limits of the carbonated layer. This recalls field observations where brittle layers interbedded within more ductile rocks show the development of natural fractures, which very often terminate sharply at the boundary of the brittle layer. These fractures being conductive, they are assumed to be open and invaded by drilling fluid, which is more conductive than the rock matrix. Based on this observation, interpretation of these fractures as natural open ones is sensible. This interpretation is supported and confirmed by core observation. On the cores, natural open fractures are observed in front of these conductive fractures seen on the FMI. Figure 1 displays an unrolled photograph of the core surface, which bears strong similarities to the F M I image, the only difference between them being the fact that the pictures are not taken in exactly the same spatial location; the F M I picture is taken along the borehole wall plus a certain distance due to electrical penetration (approx. 2.5 cm), while the core picture has a smaller radius. In the example displayed in Fig. 1, an open fracture is observed on core, which stops sharply at the boundary of the carbonated layer. Inside the fracture plane, calcite crystallization is present, showing that this is clearly a natural fracture. Even more importantly, hydrocarbon shows are observed along the fracture plane, indicating that these fractures act as hydrocarbon pathways through this tight level. As natural open fractures are observed in several of the carbonate layers, it is clear that these tight layers are fractured and will not act as tight barriers during production, but will only degrade the vertical permeability.

Example 2. Identification of natural open fractures on Circumferential Borehole Imaging Log (CBIL)** This example comes from a deviated well and the image is taken from the overburden (Cretac-

Fig. 3. Drilling-induced features observed on FMI (a, b; example 3) and CBIL (c, d; example 4). Conductive en-bchelon fractures, observed on FMI in the upper part of 3a, are developed rather extensively along the whole logged reservoir with a very constant E-W orientation. They are similar to the drilling-induced fractures illustrated on Fig. 4a. A more continuous vertical fracture, branching at bed boundaries, observed on CBIL 3c, can be interpreted in the same way. The large conductive (black) bands observed in the lower part of 3a and b, with small scale rock chips, recall breakouts due to the present-day stress regime, as illustrated on Fig. 4b. They are oriented N-S. On the CBIL image (3d), breakouts appear as dark bands, due to the borehole ovalization, the ultrasonic tool working as an ultra sensitive caliper.

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eous limestones). Here a CBIL imager was logged, because this well was drilled with oilbased mud. Hydrocarbon shows were recorded in a 20m thick interval and since no core had been cut, borehole imaging was the only way for investigating the origin of the shows. The picture presented on Fig. 2 displays a natural fracture revealed by a dark sinusoidal feature, due to low acoustic impedance along the fracture. This open fracture fits well with drilling losses, recorded at this depth. Therefore it is believed that the hydrocarbon flows through open fractures, the matrix being almost tight.

Example 3. Evidences of drilling-induced features on FMI image The emergence of high resolution borehole imagers has led to the awareness of the effects of drilling on the borehole wall integrity, which previously were either ignored or poorly described. Identification of drilling-induced fractures is a vital challenge, because they can be mistaken for natural fractures and lead to a completely false estimation of the reservoir potential. The given example is taken from a wildcat drilled in the Viking Graben, with the Brent Middle Jurassic sandstone as a primary objective. An F M I image was acquired in this interval. O n t h e FMI picture, conductive fractures are extensively developed, with a constant orientation. They often show a typical en-&helon pattern (Fig. 3a), which recalls drilling-induced fractures described as 'petal fractures'. This type of fracture has been reported in cores taken in quartzites of Alberta, Canada (Fig. 4a), where fractures seen at the surface of the core do not fully penetrate it, demonstrating that they are induced by coring. It is commonly considered that these drilling-induced fractures develop when the hydrostatic pressure within the wellbore exceeds the hoop stresses around the hole and that they occur in the direction of the maximum normal stress (Lehne & Aadnoy 1992). Another type of drilling-induced features is also present in this example. On Fig. 3a below xxl0 and on Fig. 3b, borehole breakouts are clearly observed. They are revealed by reciprocal (180 degrees) conductive stripes, due to the accumulation of drilling mud in the extremities of the ovalized borehole. This phenomenon of breakouts has been studied by rock mechanics experiments under anisotropic stress conditions (Fig. 4b), and it has

been shown that these features develop along the direction of minimum normal stress. So the orientations of drilling-induced fractures and borehole breakouts are directly controlled by the in situ stress regime, with an orthogonal relationship (Fig. 4c).

Example 4. Evidence of drilling-induced features on a CBIL image This example is from the same geographical area as example 3. Both drilling-induced fractures (Fig. 3c) and borehole breakouts (Fig. 3d) are observed. Drilling-induced fractures here present a continuous shape, which is more classic, and their orientation is consistent with the one observed in the previous example. Borehole breakouts appear as dark bands, due to borehole ovalization, the tool working as an ultra sensitive caliper.

Example 5. Example of fracture identification on a FMI Image The following example illustrates the problems that an interpreter can face. It is taken from a deep exploration well drilled in the Viking Graben with the Brent Middle Jurassic sandstones as target. First, the left hand image displays large fractures subparallel to the borehole axis (Fig. 5). Their interpretation as natural cemented fractures is unambiguous, since these fractures display resistive (white) traces. More equivocal is the conductive (black) halo observed at each top and bottom of the sine wave defining the fracture plane, but this feature is in fact typical of cemented fractures. Secondly, the right-hand side image deals with conductive fractures, whose interpretation is more difficult. These fractures could have been considered as natural open ones, which may represent a major challenge for enhanced production, since the gas-bearing reservoir was found tight. Nevertheless they were interpreted as drilling-induced fractures, relying on their morphology and on the fact that they present a constant orientation, parallel to the direction of the maximum normal stress, as deduced from other wells in the same area. A drill-stem test was conducted in front of this interval, but did not flow, which tends to prove that the interpretation is correct.

Example 6. Clay smear&g associated with a normal fault observed on a CBIL image This example is taken from an exploration well

HOW TO CHARACTERIZE FRACTURES IN RESERVOIRS

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Fig. 5. Example of fracture interpretation based on FMI image (example 5). The left-hand picture reveals natural cemented fractures. They appear as resistive (white) features, with a typical conductive halo at the tops and bottoms of the sine wave figuring the fracture plane. The right-hand picture exhibits conductive (black) fractures. They are interpreted as drilling-induced ones since they have a consistent orientation, parallel to the direction of maximal normal stress, which is deduced from other wells of the same area (like the one shown in Fig. 3a,b).

drilled offshore Mid-Norway, with a watersediments show a fault drag pattern. based mud of moderate density. Notice that the edge enhanced CBIL image (righ-hand) shows all events as dark sinusoids On the CBIL image (Fig. 6), is a low acoustic impedance (dark) feature with high dip at , regardless of their nature. Edge enhancement is xx33.5m, cutting through the sandstone. This currently used as a complementary interpretais interpreted as a normal fault. Note the 25 cm tion technique. thick dark infill of the fault, which is believed to be the clay smearing, a heterogeneity which Example 7. Equivocal resistive response of might control transmissibility of the fault. The enhanced CBIL image (right-hand) assists in cemented fractures on a F M I image highlighting the fault plane. The arrow-plot This example is taken from a production well of (left-hand track) shows the results of the CBIL a field located in the Viking Graben, with gas interpretation, where the fault plane and assoreservoir in the Brent Middle Jurassic sandciated fractures have a consistent direction. Furthermore, the tadpoles in the overlying stones. The selected image is a dynamic normal-

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Fig. 6. Example of clay smearing associated with a normal fault (example 6). The CBIL image shows a steep dipping fault at xx33.5 m cutting through the sandstones. The fault appears to be wide and of low acoustic impedance (dark) due to a 25 cm thick clay smear of the fault plane. The edge enhanced image (right-hand side) highlights this fault plane. The arrow plot (left-hand track) resulting from the interpretation shows the orientation of the fault and associated fractures and displays a drag-fault geometry within the above-lying series.

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Fig. 9. Example of a CBIL image showing the benefits of having fullbore coverage available as opposed to conventional dipmeter logs (example 9). The arrow plot alone shows an apparent drag which might be interpreted as being associated with a fault. Both dynamic normalized and edge enhanced images instead show a sedimentary structure. Furthermore, notice truncation surfaces at xx42.5 m, and very thin laminated beds. Vertical light stripes are due to marks produced during previous logging operations and also by the metallic blades from the upper centralizer of the dipmeter tool, which were scraping the mudcake.

HOW TO CHARACTERIZE FRACTURES IN RESERVOIRS ized display and shows fractures in the reservoir a r o u n d the g a s - w a t e r c o n t a c t located at xx00.2m (Fig. 7). In the gas-bearing section, fractures appear conductive and might be mistaken for open fractures. In the water-wet section, they show a resistive facies indicative of cemented fractures. One core taken in the gasbearing zone revealed silica cemented fractures. So the apparently conductive fractures observed in the gas zone are in fact cemented fractures. Their misleading conductive facies is due to the fact that the image is dynamically normalized; the fractures are in fact resistive, but the matrix is even more resistive due to the very high gas content. Conversely, on a static normalized image, the whole gas-bearing zone appears blank. This example argues for a combined use of dynamic normalized images, where the best contrast is present, and static normalized images, where the exact resistivity of the geological features can be ascertained.

Example 8. Example o f a fault identified on a F M I image This example is taken from the same production well as example 7. This well crosses a fault, cutting away about 80 m of the uppermost reservoir section. The FMI image was utilized in order to identify and orientate this fault. On the dynamic normalized image (Fig. 8, left-hand), the identification of the fault is ambiguous. It is crystal clear on the static normalized image (Fig. 8, right-hand), which allows definition of the strike and dip of the fault plane. This is due to the fact that the static normalized display captures the strong resistivity contrasts between the conductive shales and the very resistive gas-bearing sandstones. The well was sidetracked as a consequence of this interpretation, avoiding this fault, and it found a complete reservoir section.

Example 9. Benefits of having images with fullbore coverage Conventional dipmeter analysis alone can be rather ambiguous especially when based only on computed tadpoles. If interpreting only the

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arrow plot (Fig. 9, left-hand track), the tadpoles with decreasing dip at xx43 m could be interpreted as drag fold associated with a normal fault. Full coverage CBIL image (Fig. 9, center) refutes this, showing clearly that the dip pattern reflects a sedimentary structure. Interpretation is made easier by the use of the edge enhancement display. The reliability of a dipmeter interpretation is greatly enhanced by the use of borehole images.

Conclusions The use of high resolution borehole imagers has led to a revolution for reservoir geologists, who now have an insight into the reservoir. It is now possible for them to discriminate between natural open fractures, cemented ones, and drilling-induced features. The main improvement is that it is now easy to orientate all these geological features. As a matter of fact, matching borehole image interpretations with production data appears to be the most efficient way to manage fractures in reservoirs. It is a pleasure to thank Elf Petroleum Norge and Statoil for allowing the borehole images to be used. The use of * throughout denotes a Mark of Schlumberger and ** denotes a Mark of Western Atlas Logging Services.

References HALLER, D. & HAMON,G. 1993. Meillon-Saint Faust gas field, Aquitaine basin: structural re-evaluation aids understanding of water invasion. In: PARKER, J. R. (ed.) Petroleum Geology of NW Europe, Proceedings of the 4'h Conference. Geological Society, London, 1519-1526. LEHNE, K. A. & AADNOY, B. S. 1992. Quantitative analysis of stress regimes and fractures from logs and drilling records of the North Sea Chalk Field. The Log Analyst, 33, 351-361. SAFINYA, K. A. LE LAN, P., VILLEGAS,M. • CHEUNG, P. S. 1991. Improved formation imaging with extended micro electrical arrays. Society of Petroleum Engineers, Special Paper 22726. SERRA, 0. 1989. Formation MicroScanner image interpretation. Schlumberger Educational Services, Houston Texas.

Measurement scale and formation heterogeneity: effects on the integration of resistivity data P. D. J A C K S O N l, P. K. H A R V E Y 2, M. A. L O V E L L 2, D. A. G U N N 1, C. G. W I L L I A M S 2 & R. C. F L I N T 1

1British Geological Survey, Keyworth, Nottingham NG12, UK 5GG 2 University of Leicester, University Road, Leicester LE1 7RH, UK Abstract: Core and downhole logging resistivity data gathered during Leg 133 of the Ocean

Drilling Program are used to illustrate the wide range of scales of resistivity data available for reservoir characterization. The differences in scale and sampling interval between quantitative log resistivity data and conventional core plug data is shown to be central to reconciling these two datasets. Resistivity images of fine scale sedimentary structures taken on half-round cores are presented (at the same resolution as the downhole borehole wall imaging tools) and these fine structures are shown to be 'lost' if investigated using conventional core plugs and downhole resistivity logging tools. The limitations of conventional measurements on core plugs are presented and contrasted with the benefits of logging all of the core in the laboratory at a resolution comparable to the borehole wall imaging tools. An example of integrating different scales of resistivity data using a modelling approach is presented and is shown to be applicable to both core and log data. Visualizing and comparing the scale content of different resistivity datasets has been achieved in an intuitive way using a spectral method which illustrates the 'data gap' in quantitative resistivities which exists between core and log data. Fine scale sedimentary structure is now accepted to be an important control when considering hydrocarbon reservoir modelling. Furthermore, the difficulty of quantifying heterogeneity over scales from mm to km is well known. While geological processes occur seamlessly over scales ranging from pores to sedimentary basins, difficulty is experienced in reconciling log and core data over the mm to m scales. This paper presents real data over a range of scales and highlights some problems encountered when moving from one scale to another, with particular reference to reconciling traditional core-plug resistivity measurements with higher resolution, similar data available both in the laboratory and downhole. In the direct current approximation, electrical resistivity measurements are scale independent and are increasingly being used as the method of choice for identifying different scales of heterogeneity. The development of the vast majority of downhole resistivity measuring technology has been in response to the needs of the oil industry, driven by the knowledge that electrical resistivity can be used as a primary estimator of the oilsaturation of reservoir rocks. Consequently, a wide range of resistivity logging tools which are sensitive to a wide range of scales of heterogeneity already exists. Resistivity variations are identified routinely over differing distances from the borehole, into the formation, using suites of

borehole logging tools having various resolutions. In addition to the degree of oil-saturation, electrical resistivity is sensitive to the total amount of fluids in the rock, how the fluids are distributed, the resistivities of the fluid phases and the rock matrix. Thus electrical resistivity is sensitive to sedimentological structures, lithology and pore-fluid composition (Archie 1942). The vertical resolution and depth of investigation of a suite of standard downhole logging tools is shown in Fig. 1. The act of drilling a borehole disturbs the electrical resistivity of the formation and the well-bore, and historically, different logging tools have been developed to assess different radial zones around the borehole. Ideally, constant resolution at different depths of investigation is required to assess radial changes in resistivity, due to invasion by fluids from the borehole. A general trend can be seen in Fig. 1, which is common to resistivity measurements in general; that increasing depth of investigation is associated with decreasing power of resolution. For example, in Fig. 1 the Formation MicroScanner (FMS) has the greatest resolving power (vertical resolution of 0.5 cm) at a depth of investigation of 2 cm, while the deep induction tool (ILD) has the least resolving power (vertical resolution of 1.5 m) at a depth of investigation ranging from 1.25 to 3.75 cm. Therefore it is clear that these

JACKSON,P. D., HARVEY,P. K., LOVELL,M. A., GUNN,D. A., WILLIAMS,C. G. & FLINT,R. C. 1998. Measurement scale and formation heterogeneity: effects on the integration of resistivity data In. HARVEY,P. K. ~z LOVELL,M. A. (eds) Core-LogIntegration, Geological Society, London, Special Publications, 136, 261-272

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lililliillillil Fig. 2. Schematic diagram of technology used for micro-resistivity imaging of core (after Jackson two extreme cases (FMS and ILD) 'sample' very different portions of a reservoir, making quantitative comparisons unsafe if 3-D heterogeneity is suspected, or if radial invasion processes cannot be allowed for. In addition, the 'style' of current flow between the two tools is diverse, making their responses to the same heterogeneity very

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different (e.g. anisotropy resulting from thin layers having alternating, contrasting resistivities). These downhole logs are 2-D and l-D, respectively, and 'visually-suggest' erroneous radial continuity. Core from the borehole itself samples a volume comparable to that of the pad-tool

MEASUREMENT SCALE AND FORMATION HETEROGENEITY

Fig. 3. Map of Northeast Australian margin showing Leg 133 drill sites (after Davies et

'micrologs' (Microlog, Microlaterolog, Micro Spherically Focused Log); however, these padtool measurements sample outwards from the borehole wall, while the core samples some distance 'inside' the borehole wall. In Fig. 1, the FMS can be seen to 'sample' an even smaller volume of the formation. Thus, with downhole pad-tools that sample a volume of the formation comparable to that of the borehole, there is still room for discrepancy between core and log data even when measured at identical scales and resolutions. For example in Fig. 1, while the 'Microlaterolog' and a 'core-plug' measurements have comparable resolutions and depths of investigations, they cannot sample the same piece of the formation. Laboratory measuring techniques have been developed which have a resolution similar to the borehole wall imaging tools (e.g. FMS in Fig. 1); one such technique is outlined in Fig. 2. This technique enables the electrical resistivity of fine scale geological structures to be assessed (for the first time) on core in a way which enables direct comparison with downhole images. This technique assesses resistivity quantitatively and unlike

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the FMS does not require empirical calibration. The technique has a resolution of 5 mm, unlike standard core plug measurements which are 'averaged' over volumes that can be larger than important small scale sedimentary structures (i.e. contravening statistical sampling practice). The aim of this paper is to study examples of heterogeneity at a variety of scales, using core and log data, to demonstrate how data may be presented in order to preserve the scale information.

Core and downhole logging data from the Ocean Drilling Program The Ocean Drilling Program (ODP) offers scientists the opportunity to study geological processes through access to both core and downhole measurements. Continuous core is collected for laboratory analysis and these measurements are complemented by an extensive suite of logs, which have been selected to suit the widest range of geological environments. The suite of resistivity logs available to ODP are

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the dual-laterolog/induction for greatest depth of investigation, the spherically focused log (SFL) and a 'slimline' version of the FMS. The logging tools are passed out through the drill bit into the borehole, which itself has been drilled with sea-water as the circulating fluid. These arrangements, while resulting in variable hole conditions, create little invasion in typical situations. Thus the resistivity logging data does not require 'mud corrections' but can be affected by very variable hole diameters. Leg 133 of the Ocean Drilling Program (Davies et al. 1991) drilled the northeastern Australian margin with the objective of studying sedimentary responses to global changes in sealevel within the last 10-20Ma, with particular reference to palaeo-climate, oceanography and the evolution of carbonate platforms. Site 823

Site 823 is situated in the centre of the Queensland Trough ENE of Cairns as shown in Figs 3 and 4. The sediments are a sequence of clastic turbidites within hemipelagic carbonates. The turbidites are shelf sediments derived from the Australian sub-continent. The bathymetry dis- Fig. 5. Photograph of a sandy turbidite seen in core played in Fig. 3 can be seen to indicate steep from Site 823. shelf slopes which will have facilitated the

Fig. 4. Marion plateau showing the positions ODP sites 815 and 823 (after Davies et al. 1991).

initiation of turbidity currents. The sediments were collected using the Advanced Piston Corer, developed by the Ocean Drilling Program for sampling soft sediments through the drill string. The sediments are inter-bedded sands and muds. The sands are dark grey in colour and fine upward, while the carbonate muds are bioturbated nanno oozes containing forams and bioclasts, and are grey in colour. Micro-resistivity core imaging (Jackson et al. 1990) was undertaken on a variety of turbidites from Site 823 (Jackson et al. 1991), one of which is displayed in Fig. 5. Here the visually bland carbonate sediments can be seen bounding the turbidite. Complex laminae can be seen which are typical of clastic turbidite deposits. The most striking feature to the naked eye is what appears to be a zone of more open pore structure at 97.5cm in Fig. 5. The micro-resistivity image, and corresponding micro-log (averaged at constant depth from the core image) can be seen in Fig. 6. The extent of the turbidite is well defined, as would be expected for a change in lithology from fine-grained hemipelagic carbonate sediments to a laminated sand. Fine scale 'laminae' are very prominent in the micro-resistivity image of the turbidite, particularly near its base. Many laminae, invisible to the naked eye, are promi-

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Fig. 6. Microresistivity data corresponding to the core in Fig. 5 in image and log format. The log illustrates the rapid changes in resistivity that can occur within the volume of a conventional core plug.

nent features in the micro-resistivity image. There is a general trend of increasing resistivity with depth within the turbidite, which is consistent with 'fining upward' which is typical of these deposits. The laminae in the uppermost half of the turbidite are less well pronounced and show evidence of bioturbation. The lower portion of the turbidite appears to be strongly laminated (25 and 110 mm) while the remaining upper part (110 to 190mm) could be described as being more disturbed, having poor lateral continuity at constant depth.

Such features are consistent with known turbidite facies and the trends seen in resistivity core image can be mapped to the classic 'Bouma sequence' used in the classification of turbidites (Bouma 1962; Lingen 1969). The micro-resistivity core data provide substantial additional information regarding small scale laminae that are invisible to the naked eye. As these data are quantitative they could be used in petrophysical calculation schemes (e.g. construct permeability predictors applicable to each lamina). The extent of a standard core plug is shown in

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Fig. 7. Photograph of a 0.5m of clayey nanno ooze core obtained from Site 815.

Fig. 6, and illustrates that a multitude of separate individual lamina may be contained within a single core plug. Consequently, standard core plugs provide 'averages' of fine scale structures which are unrepresentative because no sediment exists with these 'average' properties and simple volume averaging is unsafe because it is not consistent with the physics of the measurement. Site 815

Sites such as 815 were chosen in pure carbonates in order to sample sediments where the climatic and oceanographic signatures are likely to have the best chance of preservation. Site 815 is situated at the southern margin of the Townsville Trough, as shown in Figs 3 and 4. Hole 815A was drilled and sampled to a depth of 474m below sea floor (mbsf) through a 416 m thick package of hemipelagic carbonate sediments of Miocene-Pleistocene age which overlie Miocene (Lower to Middle) shelf carbonates. A sedimentary unit (II) was identified, extending from 72 to 280 mbsf, which is described by Davies et al. (1991) as an expanded section of greenish-grey to grey nanno-fossil sediments, ranging from slightly bioturbated oozes to

unlithified mixed sediments. The section of the hole within a deeper sub-unit (liB) extending fi'om 111 to 280 mbsf, characterized by partial lithification, was the first section in which the hole conditions were stable enough to allow logging. Repetitive colour changes were seen in the core of this sub-unit associated with rates of deposition as high as 38.5 cm ka 1. Davies et al. (1991) suggest this cyclic colour change was controlled by variations in both grain size and carbonate content, the latter varying between 10% and 60%. Micro-resistivity core imaging (Jackson et al. 1990) was undertaken on two 0.25 11"1sections of core from a depth of 201 mbsf. As this interval had been logged downhole by the SFL/induction suite of resistivity logs operated by ODP, we have datasets covering core and downhole over scales from 5mm to 2m. The two sections of core used for micro-resistivity imaging are shown in Fig. 7; the marks made by the electrodes are clearly visible, as is the general lack of visual sedimentary features, which is typical of this sub-unit. The results of micro-resistivity measurements on core in the interval 201.7 to 202.3 mbsf can be seen in Figs 8 and 9. The single-trace log plot is a single average of core micro-resistivity values at

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Fig. 8. Microresistivity data corresponding to the core in Fig. 7 in image and log format, showing marked vertical and lateral variability.

constant depth, while the micro-resistivity images can be seen to depict lateral heterogeneity in addition to vertical variability. The data are converted to Formation Factors (Archie 1942) where the sediment resistivity has been normalized with respect to the resistivity of the pore fluid (i.e. seawater). The dimensions of a standard core plug (38x80mm) have been superimposed on the single-trace log, showing that substantial heterogeneity occurs within this volume. Consequently, in these sediments, one can again see that traditional core plug resistivity measurements are both 'unknown' averages of finer scale structures, and that small changes in the location of the core plug can lead to substantial changes in the measured value. The averaging process relating to these core plug measurements is 'unknown' because a measurement designed for

homogeneous material is being applied to cylinders of rock which are heterogeneous, often in 3-D, as in the above case (Lovell et al. 1994). In order to reconcile such 'averaged' measurements with the micro-resistivity images, simulating the core plug measurement on the basis of the micro-resistivity image results would be necessary. A simple average is not suitable because the distribution of resistivity variability controls the measured value, in addition to the 'simple' average resistivity. For example, if there were fine scale horizontal laminae having alternating resistivity values, the lower values would dominate when the plug was 'drilled' horizontally (as is the case in Figs 8 and 9) and conversely the higher values would dominate if the core was 'drilled' vertically (assuming uniform current flow through the core plug using plate-electrodes on each cylindrical face and

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Fig. 9. Microresistivity data corresponding to the core in Fig. 7 in image and log format, showing similar variability to Fig. 8 but different 'average' value.

separate independent potential measuring electrodes). The corresponding downhole resistivity logs (SFL & ILD) are shown in Fig. 10 (Jackson & Jarrard 1993) where the SFL log can be seen to contain far more small scale features than the corresponding ILD log. The 'Rt' log in Fig. 10, uses a modelling approach (Jackson & Jarrard 1993) to combine the resolution of the SFL with the deeper penetration of the ILD. This log has been used as the basis of the 'SFL Formation Factor' shown in Fig. 11. Figure 11 displays the micro-resistivity core data along side the downhole log data, and illustrates the wide range of scales of resistivity measurements that are characteristic of one sedimentological unit. The resolution of the SFL & ILD are superimposed (from Fig. 1) and can be seen to be greater than the total

extent of the micro-resistivity images, and huge compared to the volume sampled by the standard core plug (a single SFL measurement 'samples' 10000 times more formation than a core plug). Thus, while the sampling intervals of the downhole logs are far smaller than their resolution (i.e. overlap and sample all the formation adjacent to the borehole), those of standard core plugs can be seen to be so sparse as to be unrepresentative, and contravene sampling practice (e.g. sampling at twice the max. 'frequency' is required). In addition, on the other hand, the micro-resistivity core data shows the core plug data to be unrepresentative of mm scale heterogeneity such as the fine scale laminae described above. These points are further illustrated in Fig. 12 where the micro-resistivity core data, the SFL and the ILD are displayed at the same scale.

MEASUREMENT SCALE AND FORMATION HETEROGENEITY

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Fig. 10. Site 815 downhole resistivity logs showing the corrections and errors associated with the calculation of

SFL core (after Jackson & Jarrard 1993).

Visualizing scales of heterogeneity using a spectral method A resistivity log may be considered to be a series of anomalies which can be described in terms of spatial frequencies using Fourier techniques, the inverse of the 'wavelength' of an anomaly being its spatial frequency. The relative 'weights' of each anomaly can be added in the frequency domain, generating a Fourier series of frequencies which can be used to re-construct the original resistivity log. While Fourier theory gives us a single step function containing a wide range of frequencies, the resolving power of the measurement systems act as 'high cut filters', defining a minimum wavelength for each resolution. While digital filtering and the use of correlation lengths can be used, the spectral method provides a means of visualizing scalerich resistivity logs which are intuitive, and whose strengths and weaknesses can be assessed knowing the basic principles of Fourier analysis and sampling practice.

An example of such a display is presented in Fig. 13 where core and downhole data from ODP Site 815 are displayed along side an example of the trace from a single button from an FMS image log. The characteristic wavelengths of the core data can be seen to be far smaller than the SFL log, while the FMS data appear to occupy an intermediate position. Thus there is a substantial gap in scale information between the core and log data in Fig. 13 which would be bridged by successive measurements at high resolution (e.g. 'FMS type' measurement displaying quantitative resistivities). Such laboratory measurements would span all the scales depicted and could be used to predict both core plug measurements and downhole log values of resistivity without loss of scale information. Thus a link could be made between core and downhole measurements to reconcile core plug and downhole measurements (e.g. one could conceive of thin laminae where equality between core plug and log data is indicative of a mismatch or error because they would not be expected to agree).

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Fig. 11. Comparison of electrical resistivity data from Site 815 (ODP): micro-log (core image) and the integrated SFL/ILD downhole log, from core to whole borehole.

Conclusions Resistivity measurements are inherently scale independent, and downhole logging datasets spanning scales from 5ram to 100m are routinely acquired by the oil industry. Relating these downhole, scale-rich datasets to core requires further research for the following reasons: (1) Core plug data provide measurements which relate to volumes that are greater than fine scale sedimentary structures, providing responses in the resistivity datasets that are important in reservoir modelling. (2) Core plug data provides an extremely 'sparse' spatial coverage of the core; the sampling interval, being of the order of 1 m, makes them unrepresentative of structures with characteristic lengths less than 2m. (3) Typically, core plugs sample less than 1% of the core recovered.

(4) Continuous laboratory resistivity logging of core is not routinely available. In the absence of high resolution continuous core data, visualizing the scales of resistivity heterogeneity could be a valuable tool for the petrophysicist to use when considering the match or mismatch between core and log data. The availability of core resistivity datasets comparable to those available downhole, in terms of resolution and continuous sampling, will underpin the understanding of the differences between core plug data and downhole logs. This in turn will enable the prediction of petrophysical properties over the whole range of scales available, including the fine scale sedimentary structures that cannot be adequately characterized at the present time, but are known to be important controls of reservoir behaviour.

The electrical resistivity imaging of cores forms part of a collaborative research programme between the British Geological Survey and Leicester University

Fig. 12. Comparison of core and downhole resistivity data over 0.59 m at Site 815 (ODP)

Fig. 13. Visualization of disparate scales of resistivity data: micro-log (core image), FMS and integrated S F L / ILD downhole log

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(LAMBDA). The core imaging technique was developed under a Natural Environmental Research Council Special Topic initiative (ODP). The LAMBDA Project aims to improve our understanding of electrical and fluid flows in reservoir rocks through the study of pore morphology and utilizes the imaging system in its research. LAMBDA is currently funded by Shell UK and Mobil North Sea Ltd. This paper is published with the permission of the Director of BGS (NERC). R e f e r e n c e s

ARCHIE, G. E. 1942. The electrical resistivity log as an aid in determining some reservoir characteristics, Journal of Petroleum Technology, 5, 1-8; Transactions of AIME, 146, 54-62. BOtrMA, A. H. 1962. Sedimentology of some flysch deposits. Elsevier, Amsterdam. DAVIES, P. J., MCKENZlE,J. A., PALMER-JULsON,A., et al. 1991. Proceedings of the Ocean Drilling Program, Initial Reports, 133: College Station, TX (Ocean Drilling Program). JACKSON,P. D., LOVELL,M. A., PITCHER,C. GREEN, C. A. EVANS, C. J. FLINT, R. 8~ FORSTER, A. 1990.

Electrical resistivity imaging of core samples. Advances in core evaluation, accuracy and precision in reserves estimation. In: WORTHINGTON,P. F. (ed.) Proceedings of the European Core Analysis Symposium (Eurocas I), Society of Core Analysts, Gordon & Breach Science Publishers. & Shipboard Party of Leg 133 of the Ocean Drilling Program 1991. Electrical resistivity core scanning: as new aid to the evaluation of fine scale sedimentary structure in sedimentary cores. Scientific Drilling, 2, 41-54. & JARRARD, R. D. 1993. Integration of SFL and ILD electrical resistivity logs during leg 133 of ODP: an automatic modelling approach. Proceedings of the Ocean Drilling Program, Results, 133. College Station, TX (Ocean Drilling Program), 687-694 LINGEN, G. J. VAN DER 1969. The turbidite problem. New Zealand Journal of Geology and Geophysics, 12, 7-50. LOVELL,M. A., HARVEY,P. K., JACKSON,P. D. BALL,J. K. ASHU, A. P. FLINT, R. F. & GUNN, D. A. 1994. Electrical resistivity core imaging : towards a 3dimensional solution, SPWLA 35th Annual Logging Symposium, Tulsa, OK. II: Paper JJ.

Aspects of core-log integration: an approach using high resolution images J C. L O F T S 1 & J. F. B R I S T O W 2

1Z & S Geoscience, Kettock Lodge, Aberdeen Science & Technology Park, Balgownie Drive, Bridge of Don, Aberdeen, AB22 8GU, UK 2 Schlumberger GeoQuest, Gatwick, R H 6 0 N Z , UK Abstract: Core-log comparisons are often not considered routinely enough within the exploration environment. This may be for a number of reasons, such as problems with depth-matching the core and log datasets, the environment of acquisition of both datasets, the lack of understanding of log and core acquisition or a lack of confidence in laboratory or log measurements. These problems are discussed as a preliminary step to the development of a strategy aimed at improving core-log integration. Using recent technological advances in the side-by-side presentation of core and high resolution image data from logging tools, a strategy is presented with the aim of making core-log integration more rigid and routine. Features in both core and images are correlated interactively--thus ensuring the best possible integration. This is a two stage process involving core-to-image matching, and then image-to-log matching. This strategy has the potential to make core-log integration more accurate and as a result enable the interpreter to realize the most from sub-surface data.

Borehole logging provides quasi-continuous (typically every 150mm), in situ measurements of a complete range of physical properties which, when integrated, both characterize a lithology and act as a complimentary dataset to the more traditional routinely acquired core measurements. Besides measuring properties at in situ conditions (i.e. temperature and pressure), logging measurements also represent the measurement of a much larger volume of rock than conventional core samples. In this way, measurements may be regarded as more representative of the lithology being logged. In contrast, core measurements do have the advantage of having a much higher accuracy and precision and lower limits of absolute detection than most logging devices, because of a less hostile environment of acquisition. Such differences complicate the direct comparison (or integration) of these two datasets. The question that must be asked is why is it necessary to integrate core and log data? This answer is a combination of factors: (1) To calibrate logging data, to understand the source of the measurements being made. Without any reference to core data we would be uncertain of the log value that is most representative of a particular lithology and/or fluid. (2) To acquire as much information as is

possible about a particular geological environment, a lithology, or a petrophysical property such as porosity, or even permeability. The most thorough assessments of the subsurface have come from joint core and log studies. (3) Integration of core and log data enables scientists to predict the likely lithology in a cored sequence where core recovery is incomplete, by reference to the logging data. (4) To appreciate the potential lithological bias that can exist in the acquisition of cores. This paper looks at the considerations that must be taken into account when comparing log and core datasets. While some problems are impossible to resolve completely we suggest one methodology to improve the integration of these data.

Considerations when comparing datasets When assessing the correlative agreement of two datasets, it is important to understand the circumstances in which the comparison is made. The main problems are the incompatibility of the two sampling methods (sampling disparity), incorrect depth assignment (between core and log data and between different log data), the parametric differences (measurements made under

LOFTS, J. C. & BRISTOW,J. F. 1998. Aspects of core-log integration: an approach using high resolution images In. HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 273-283

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different conditions), and problems relating to core acquisition, such as preferential core recovery. These are now discussed.

Sampling disparities Volume and heterogeneity. Of major importance when comparing core and log datasets is the sample size/volume difference between core and log data. Core plugs taken in laboratory analysis generally have a volume of the order of 10 mm 3 while logging measurements represent a volume in excess of 100mm 3, or many hundred times larger. The geochemical logging tool (GLT; Mark of Schlumberger) measurement, as an example, acquires data from a volume of approximately 0.3-1m 3 (Hertzog et al. 1989; Pelling 1992), whilst deep induction (resistivity) tools have a depth of investigation of greater than 1.5 m, with a vertical resolution in the order of 2m (Allen et al. 1988). Both measurements cover a large volume which will include any lateral or vertical heterogeneities. A core plug effectively measures the formation at one point and heterogeneities greater than the core plug are not considered. This leads to the problem of the core samples not being representative of the formation or rather, being incompatible with the log data. In addition, logging tools measure the formation in a dynamic sense, i.e., the fluids and matrix alike. While some indication of the fluids may be gained from laboratory measurements, they are not intrinsic to the direct measurement. Viewed statistically, these different datasets represent two different systems of geostatistical support (Clarke 1984), where volume and heterogeneity are most likely to be different between systems. Consideration of such a volume disparity is important especially when the lithology being measured is heterogeneous on a fine pore// lamination scale (Worthington 1989). Implementing techniques such as slabbed core sampling can reduce this problem. Slabbed core samples are samples taken along the axis of the core over a length of up to 0.5 m. The core is then homogenized into a bulk sample and the laboratory measurement is made on this sample. This procedure helps to minimize the disparity in the volume and vertical resolution of core and log data and makes them more suitable for integration. It is not a valid technique however, for volumetric measurements such as porosity and permeability. Sample density and averaging. The regularity and density of sampling of each dataset is a crucial consideration. Commonly, if not considered, the

Fig. 1. Figure showing the artefacts of the bed boundao' effect. Averaged estimates of matrix density appear on the boundary of a cemented 'calcite dogger'. The shaded area represents the difference between averaged log data (black line) and the actual core matrix density estimates (dots). The worst areas are bordering the cemented 'calcite dogger' boundary. Adapted from Lofts 1993. result will be incorrect depth assignment of the data (depth mismatching) and ultimately erroneous correlations. Types of sampling are explicit sampling (at discrete depths) and implicit sampling (following on at a constant sample rate from the previous value). Generally, the former is used for core data and the latter for log data. The way that data are manipulated during and after acquisition must also be considered. With logging measurements, it is common for one or two processes of vertical averaging to be performed. One is performed during acquisition because of the intrinsic physics and the method of acquisition of the measurement (especially when using nuclear devices where an element of randomness is present) and also because of the accumulation of data over a time interval as a device is moving upwards (data are collected and integrated over a vertical distance). A second vertical averaging is performed to reduce noise and to increase the precision of the measurement. Failing to consider the sampling density of both core and logging techniques (and vertical averaging of the logging data) will lead to corelog depth mismatch, as samples of a relatively low frequency are matched with abundant, highfrequency log data. An example is demonstrated in Fig. 1. This shows the averaging effect on log derived matrix grain density (produced from measurements made by the geochemical logging tool) over the boundary of a cemented 'calcite dogger' horizon. The shaded area represents the difference between the actual core-derived data and the log data. The discrepancy is caused by

ASPECTS OF CORE-LOG INTEGRATION the averaging of the log data up to the cemented horizon and reflects the different inherent resolution of each measurement. In this example, most samples over thick and consistent lithological units (most samples above '2802 m') have accurate estimates of matrix density. Core measurements on the calcite cement boundary indicate a sharp dro~ to more typical sandstone densities (2.65gcm-). The log derived matrix density values for sandstone samples surrounding the 'dogger' on the boundary, however, show a smooth drop in calculated matrix density; over 1-2 m, before a more typical sandstone density is recorded.

Incorrect depth assignment Well log data are identified by wireline depths; core data by drillers depths. The two are usually different but must be integrated. This process of depth matching is more complicated where there is incomplete core recovery, which is often the case. Before this is considered, log data from different logging runs must be free from depth mismatch.

Depth shifting--log data. One way to reduce depth mismatch is by 'depth shifting'. This is standard practice when comparing different logging tool 'runs', whereby log data curves are compared to a reference logging curve and shifted upwards or downwards accordingly. This involves establishing a reliable reference curve of good quality, considering this 'on depth', and comparing and shifting other curves to this reference curve by matching spikes and identifiable curve patterns. It is common practice to correlate the total natural gamma ray curve from the natural gamma ray tool with the equivalent gamma ray curve from the other logging runs. The reference run is usually chosen on the basis of the speed of the logging run and the degree of tool motion, notably caused by tool sticking. The speed of the logging run is of greater significance in shipboard logging operations where there is the additional problem of the ships motion ('heave'). Faster logging runs are usually chosen as they are less degraded by heave. Subsequent correlation of gamma ray curves between logging runs is accomplished by broad-scale visual correlation and 'manual' shifts, and/or by automatic correlation--to satisfy the log analyst that all log data are on depth.

Depth mismatch--shifting log data in the temporal domain. One aspect of depth matching

275

between separate logging runs that seems to have been largely overlooked in the available literature until recently is the application of datum shifts in the temporal rather than the depth domain. The depth shifts, as calculated from the comparison of a gamma ray curve against a reference gamma ray curve, are subsequently applied to all measurements from all the logging tools deployed on the tool-string during a particular run. A problem however, occurs when the depth shifts are events that have occurred at discrete points in time, as opposed to discrete depths. Single logging tools screwed end-to-end (commonly referred to as tool strings) are typically many tens of metres in length, (up to 30 m). Traditionally, logging data from the each tool measurement point on the tool string are recorded as a function of time and stored by the up-hole computer in a buffer. In turn, these were written to disk as a function of depth. If an event such as tool-sticking occurs, where the tool becomes stuck in the borehole for a few seconds then jumps up, the jump in the log record will occur as a function of time, not depth. Therefore, if a depth shift calculated from comparison of gamma-ray curves is subsequently applied to the other tools on the tool string, the depth disparity caused by the tool sticking event will be applied at the wrong depth. In a long tool string such as the one mentioned, the depth disparity of the applied shift could be 30m. For minor depth shifts caused by effects such as cable stretch this is not normally a problem. Figure 2 A and B illustrate the time~lepth disparity. The causes of such a disparity that occur commonly with shipboard logging are yoyo-ing, caused by ships heave, and tool sticking. Inevitably, ships heave must be taken into account if logging is performed in rough conditions. If ships heave is considerable, the up-going log will be compressed and stretched (yoyo-ed) by the effect of the ships heave on the logging tool. Any fine-scale gamma ray log comparison may take into account the expansion and compression of the log caused by ship heave; the subsequent application of these depth shifts to the other tools on that tool string would induce errors. One approach is to consider curve shifting in the temporal domain. The fine-scale depth compression and expansion caused by the ships heave can be calculated using an auto-correlative program and then applied to the other tools on the string. This is accomplished by shifting the individual tools up to the measure point of the gamma ray recording window, applying the calculated depth shifts and then depth shifting

276

J.C. LOFTS & J. F. BRISTOW

Fig. 2. Example of the problem faced with depth shifting. (A) 'Ladder diagram', no tool stick and depth increments are on depth with time increments. Tension increases to the right. (B) Situation with tool sticking. Two time increments are recorded in one depth increment as indicated by the tension. This will lead to inaccurate shifting of tools below the gamma ray (GR) tool. (C) Tool diagram showing the simplest depth shift of 1 m for all tools on a string. (D) If there is a tool stick for a length of time GR will be shifted correctly in the depth domain but tools A and B will not be on correct depth. (E) Shifting in the temporal domain. All tools bought to GR reference and depth shifted then returned to their respective depths.

ASPECTS OF CORE-LOG INTEGRATION the tools back down to their relative measure points (Fig. 2 C-E). This has recently been achieved by real-time speed correction using a high resolution accelerometer in the tool (Barber et al. 1996). Subsequently, post processing and shifting can be completed more successfully and is less reliant on cable depth measurements. Depth shifts between core and log data. Once all log data are 'on depth' they must be matched to core depths. To achieve an ideal depth match between core and log data we require a core measurement that is compatible with log data (and ideally of a similar sample frequency). A natural gamma-ray sampling measurement is now routinely used on core to allow a direct, although qualitative, comparison to be made between core and log depths. Jarrard & Lyle (1991) show how high-resolution sampling of core can significantly reduce the depth mismatch problem. As an alternative to the gamma ray core measurement, Bristow & deMenocal (1992) use a 'proxy' terrugineous curve produced from core data to match to the logged gamma ray curve (SGR). This terrugineous curve is calculated as the residual opal content (100% minus opal wt% content). Equally, a measure of organic carbon and carbonate can be used for such a curve. These are compared to the concentration of the naturally occurring elements K, U, and Th, derived from the spectral gamma-ray tool (Schlumberger 1989), all of which are primarily terrestrially derived. Such techniques allow a realignment of core depths to the more continuous, regular sampling of the log data. Parametric systems Another aspect relevant to successful data integration is the consideration of the parametric system of each measurement. The ideal parametric system would be one where a parameter is measured in the laboratory under the exact measurement conditions that exist downhole. Such conditions include pressure, temperature and acquisition frequency. Both core and log measurements would then belong to the same parametric system and we would be confident enough to integrate these measurements. This point may seem obvious, but, resistivity measurements on core samples, for example, should be made at similar frequencies to those used by the downhole tools, as for some lithologies, resistivity is frequency dependent. Measurement of the same parameters under the same measurement conditions such as pressure

277

and temperature, although scientifically desirable, are technically and financially prohibitive in most circumstances. Integration of incompatible parametric systems is possible when examining total and effective porosity. For example, core porosities measured by helium expansion (effective porosity) are often reported in the total porosity system with porosities from neutrondensity log combinations. Figure 3 shows the comparison of log and core resistivities that belong to different parametric systems; namely ones that involve 'dynamic' (logging data) and 'static' temperature and pressures (core data). Because the sampling frequencies of both measurements are not dissimilar, the single most important correction needed to make these two datasets compatible is to correct the core data for effects of temperature. In the case of a sonic velocity measurement there is a difference in measurement scale, this being largely due to measurement frequency. A velocity measurement in the laboratory will typically be at a frequency of MHz whilst in the borehole it will be over several metres and in the range of KHz. Problems relating to core acquisition Incomplete core recovery (vertical). Depth mismatch is perhaps the most common and largest source of error. Besides the problems mentioned, mismatch can be caused by incomplete core recovery. More often than not, core recovery is less than 100%, and when a core barrel is incomplete, it is usually impossible to determine where the section of core is located along the length of cored formation. Standard policy is often, to locate the uppermost core piece at the top of the core barrel. This assumes (arbitrarily) that the material was lost from the base, which is not necessarily the case and leads to depth mismatches that can span over the length of the core barrel. Preferential core recovery and induced lithological bias. Another contribution to depth mismatch is the problem of preferential core recovery. This occurs when the recovery is dependent on the lithology being drilled. Some formations are more competent than others and resist break-up and washout during drilling and coring. If they surround less competent units (shale layers, for example), they will tend to be preferentially recovered in the core barrel, ultimately leading to an incomplete recovery. Drilling parameters tend to be set to maximize the recovery of the dominant lithology in any one section, although the drilling process may be dictated by the most indurated lithology in the

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13o 170

J. C. LOFTS & J. F. BRISTOW

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lithological unit is significantly reduced during drilling (due to torque during coring) and compressed to a smaller size, often appearing as a cracked 'biscuit' texture. This can also lead to gross inaccuracies in core-log integration. Nothing can be done to remedy this except to implement a cautious coring strategy.

occur. This is common when a core sample reaches equilibrium at surface temperature and pressure. This degassing leads to core expansion, creating a full core recovery of greater than 100%. Depth discrepancies of greater than a metre are not uncommon in fully recovered core barrels. In addition to degassing effects, the subsequently aligning the top core piece with the top of the core barrel, will inevitably produce depth mismatch. This is more common in highrecovery, shallow, soft sediments which have been cored using air piston coring techniques.

Human bias and subjective sampling. Another interesting aspect is that of human bias. It is all too easy to select a biased sample by trying to get interesting, or homogeneous, 'clean' samples, that do not represent the true lithology. A typical bias we observe in the analysis of cores is that of subjective sampling, where sampling objectives may either be biased to a certain lithology or biased to extremes of lithology such as ash horizons or breccias. Specific sampling objectives may not be aimed at analysing the average core material. Un-representative samples like this are often the only samples available, so great care must be taken when these data are subsequently used for the purposes of integration or log calibration. A few techniques can be implemented to reduce this bias. These include requiring samples to be acquired at regular intervals which alleviates h u m a n bias and unrepresentative samples. Similarly, slabbed core sampling can reduce bias. Averaging a series of these core measurements over a similar sample interval to that of logging measurements will also remove such bias.

ter)/0.1405.

Additional considerations section. Simply 'hanging' the core from the top of the barrel will lead to gross inaccuracies in core-log integration.

Biscuiting. This can also be an additional problem not totally unrelated to the competency of a unit. This occurs when the volume of a

Ships heave and drill-pipe and logging-cable stretch. Ships heave must be taken into account if logging is performed in rough conditions, although most logging is performed with a wireline heave compensator. Wireline heave compensators were introduced to reduce the ships natural heave caused by sea state, during

ASPECTS OF CORE-LOG INTEGRATION logging operations. A simple modification to the wireline pulley set-up is made by the addition of a floating wheel. This floating wheel is then allowed to pitch-and-yaw forwards and backwards to compensate for the ships heave. It has been shown however, that this may be only 50% successful (Goldberg 1990) and compensation tends to be most successful in shallow, slight sea conditions. Stretch of the drill pipe, and more commonly noticeable, stretch of the logging cable also produce a depth mismatch especially if a new logging cable is used. A report from the logging engineer will generally indicate this.

True integration of core and log data It has been demonstrated that there are many potential problems associated with the integration of core and log data. Arguably, so long as the potential pitfalls are understood and considered, a meaningful integration is possible. Recent developments in software allow oriented core images to be presented side-by-side with conventional log and image data (high resolution microresistivity or ultrasonic images). This in itself has the potential to produce a more accurate 'tie' between core and log data. Features in the core can be directly correlated to features seen in images. This can be achieved because there is a platform to ensure that the right piece of core is matched with the right section of log, most importantly, at the correct scale. With this technique, cores can also be oriented on a routine basis by comparison with oriented image data. The direct result of this is that stratigraphic, sedimentary, and structural features in cores, once oriented, can be used to calculate dip azimuth and magnitudes. In fact, all the reasons for core-log integration, namely log calibration, enhanced geological interpretation, and predicting lithological bias, can be better addressed. Borehole resistivity imaging is a well established technique for the study of sedimentary features down to a scale of less than 20mm (Schlumberger 1989). Having followed a rapid evolution from a 2 pad tool--where 56 small electrical buttons (of 5 - 6 m m diameter) produced a high resolution image covering roughly 20% of the borehole by measuring microresistivity changes in a formation--there is now a 4 pad, 4 flap device, boasting 196 electrode buttons, which obtains a microresistivity image with almost 100% coverage in a 15.6cm borehole. Whilst beyond the remit of this contribution, the reader is directed to Ekstrom et al.

279

1987, Boyeldieu & Jeffreys 1988, Bourke et al. 1989, Harker et al. 1990, and Lofts et al. 1997, for further acquisition and interpretation details. A strategy f o r accurate integration

Conventionally, matching log curves from different logging runs has been performed by correlating the gamma ray curve from each tool run and re-aligning to one common depth. The gamma ray measurement however, has a relatively low vertical resolution (of the order of 200-300 mm) in contrast to the extremely high vertical resolution of microresistivity images. Tools such as the FMI (Mark of Schlumberger) and its predecessor, the Formation MicroScanner, FMS (Mark of Schlumberger) have a vertical resolution on the order of 5mm. Features seen in the images of these tools will not necessarily be detected by the gamma ray device, especially when statistical and averaging filters are applied. As a result, there could be discrepancies when trying to depth match gamma-ray to the core at a fine scale although visible on microresistivity images. One method of integration of core and log data therefore is to match core to high resolution images in one step and then match that image to log data in a second independent step. Thus, we have a core-to-image, image-to-log integration where image data can be thought of as the link between datasets (Fig. 4). Core to image matching. Core to image matching is achieved by projecting a photo-scanned, 360 ~ digital image of a core (or a digitized slabbed core photo or a hand drawn goniometry sleeve) in a fashion similar to that of borehole microresistivity image data. Once scanned, the core image is placed next to the borehole image representing the same interval. The core gamma ray and log gamma ray curves can then be used to put the datasets approximately on depth. Viewed at an expanded scale, features common to both image and core can then be identified and, where necessary, the core can be shifted to match the image precisely. In our experience, a scale of between 1:4 and 1 : 10 is most useful. If core recovery is less than 100%, patches of core can be moved and matched to the appropriate log depth. Orientation missfits of individual features will suggest where the core is not properly matched. Because a 360 ~ (or 180 ~ picture of the core has been acquired, the core image can easily be manipulated and oriented with respect to the logged image. The microresistivity image is itself oriented during acquisition by an inclinometry

280

J.C. LOFTS & J. F. BRISTOW

Fig. 4. Flow diagram summary of the strategy for improved core-log integration.

sensor within the logging tool. An example of a core image that has been scanned and displayed alongside an FMI borehole image is shown in Fig. 5. Here, the core has been oriented with respect to the image, and dip sinusoids representing sedimentary features have been computed. A comparison of the sinusoids computed from the images and the core confirms the correct match of the two datasets. Two tadpole plots representing two features, are displayed one with an azimuth of approximately 110~ SE with a magnitude of dip of 16~ the second has an azimuth of 090~ and a high magnitude of dip of 52 ~. Log data are displayed opposite in the right track. The low-frequency, almost flat curve is the gamma ray (displaying its poor vertical resolution) and the higher frequency curve is the core gamma ray measurement, with a much more appropriate resolution. The successful match and integration of core, log, and image data complement one another

and maximize the interpretation potential of the available data.

Image to log matching. In a concurrent step, it is necessary to match the rest of the logging tool data (from different logging runs) to the borehole microresistivity image. Logging tools on the same string can be shifted appropriately to that of the tool strings reference depth point and will therefore be on depth. Again, it is convention to match gamma ray logs from each separate string of tools to achieve a depth match but in order to obtain the best possible image-to-log depth match, a curve with as close a resolution to the borehole microresistivity image resolution is required. This can be achieved by matching an average button intensity from the imaging device to one of the new generation of shallow resistivity log curves which has a similar depth of investigation. Examples are high resolution azimuthal resistiv-

ASPECTS OF CORE-LOG INTEGRATION

281

Fig. 5. Example of a core image that has been scanned and displayed alongside an FMI borehole i~tage. The core is orientated with respect to the borehole image and features in the core have been picked (see text for description). ity measurements such as the LLHR 6' curve (Fig. 6) from the Azimuthal Resistivity Imager (ARI; Mark of Schlumberger) or a similar measurement from the High Resolution Laterolog Sonde (HALS; Mark of Schlumberger). One curve possible for correlation on conventional tools is the MSFL (Mark of Schlumberger) (with 50-100 mm vertical resolution). Once an image has been correctly matched with the appropriate shallow resistivity, all the other logs on the string can be depth shifted. Therefore a higher resolution depth match is possible by matching a resistivity device from each logging run to the resistivity of the imaging device. Care must be taken however, when there is a long period of time between logging runs as resistivity profiles do change. The final result is that oriented images, cores

and open-hole log data are accurately on depth. Standard resistivity curves in general, do not have the resolution to allow an accurate depth match (Fig 6).

Summary and conclusions Careful consideration and understanding of the problems that are faced when combining data from the borehole and laboratory will contribute to the successful integration of core and log data. These problems have been highlighted but perhaps the most important problems to consider are depth mismatches, sampling differences, and core acquisition problems. Implementation of a correct strategy such as the core-to-image-to-log strategy will limit some of the problems in the apparent 'mine-field' of

282

J.C. LOFTS & J. F. BRISTOW

Fig. 6. Comparison of the vertical resolution of the FMI (0.2") and the current generation Azimuthal Resistivity Imager tool (LLHR, ~6") and the traditional LLS and LLD curves. Curves such as the LLHR are suitable for the high resolution match of the micro-resistivity image (right hand image) to log data. A high resolution match will ensure that tools from different logging runs are accurately depth matched for comparison to core. The ARI image (centre and left image) is generated from 12 azimuthal laterolog resistivity readings and can be seen to match the resistivity profile of the FMI image.

core-log integration. Although demonstrated with micro-resistivity images here, it can also be applied to ultrasonic images. Such a strategy, aided by a software platform to compare the datasets will allow integration to become both more accurate and ultimately, routine. A f u l l implementation of this strategy (to exploit the core orientation benefits) would have to adopt the acquisition of core photos which require the photo-scanning of the rounded side of the core or hand drawn goniometry sleeves. Flat core photos can however, be used in a nonoriented qualitative fashion, but the full advantages of such a strategy will be achieved with rounded core photos. In addition, it would be desirable to run a

high resolution resistivity device (such as an ARI or a HALS) on each separate logging run to ensure good image-log depth match. Besides an accurate depth core-log match, the virtues of such a strategy would include a routine facility to orientate the core. With an oriented core, we can begin to perform studies with the objectives of a geographic or directional nature which are often left to the realms of goniometry, based on identification of sedimentological, crystalline, or structural features seen in the core. These studies can then be tied or 'upscaled' to large-scale seismic and tectonic features. They will also be able to help with palaeomagnetic fabric orientation, which for example, forms increasingly important core-

ASPECTS OF CORE-LOG INTEGRATION

based studies. Looking to the future, with possible developments of core based resistivity measurements (Jackson et al. 1997), it may also be possible to directly match core based resistivities to the high resolution images and then to log data, fully quantifying each step through correlating resistivity measurements. Ultimately, a more confident calibration of log data (using core) will allow us to understand and extend our knowledge of the features, or artefacts of the features, seen in borehole images. In turn, this will give us more confidence in the interpretation of images when the core is not available. Similarly, geologists will then be able to further refine the structural, sedimentological, and stratigraphic analysis of the subsurface. Schlumberger is acknowledged for permission to use Fig. 5.

References ALLEN, D., BARBER, T., FLAUM, C., HEMINGWAY,J., ANDERSON, B. & DES LIGNERIS, S. 1988. Advances in high resolution logging, The Technical Review, 36. BARBER,T., ORBAN,A., HAZEN,G., LONG, T., SCHLEIN, R. ALDERMAN,S., TABANOU,J. & SEYDOUX,J. A. 1996. Multiarray induction tool optimised for efficient wellsite operation. Society of Petroleum Engineers, SPE paper 30583. BOURKE, L., DELFINER,P., TROUILLER,J. C., FETT, T., GRACE, M., LUTHI, S., SERRA, O. & STANDEN, E. 1989. Using formation microscanner images, The Technical Review. 37, 16-40. BOYELDIEU, C. & JEFFREYS, P. 1988. Formation MicroScanner: new developments. Transactions of the llth European Evaluation Symposium, SPWLA. Paper WW. Later reprinted in 1990 in: Borehole Imaging reprint volume, SPWLA, pp. 175-190. BRISTOW, J. F. & DEMENOCAL,P. B. 1992. Evaluation of the quality of geochemical log data in Hole 798B. In. Proceedings of the Ocean Drilling Program, Scientific Results, 127/128, 1021-1036. CLARK, I. 1984. Practical Geostatistics. Elsevier Applied Science Publishers, New York.

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EKSTROM, M. P., DAHAN, C. A., CHEN, M. Y., LLOYD, P. M. & RossI, D. J. 1987. Formation imaging with microelectrical scanning arrays. The Log Analyst, 28, 294-306. GOLDBERG,D. G. 1990. Test performance of the Ocean Drilling Program wireline heave compensator. Scientific Drilling, No. 1, 1, 206-209. HARKER, S. D., MCGANN, G. J., BOURKE, L. B. ~r ADAMS, J. T. 1990. Methodology of Formation MicroScanner Image interpretation in Claymore and Scapa Fields (North Sea). In: HURST, A., LOVELL, M. A. & MORTONA. C. (eds) Geological Applications of Wireline Logs Geological Society Special Publications No. 48, pp. 11-25. HERTZOG, R., COLSON, L., SEEMAN, B., O'BRIAN, M., SCOTT, H., MCKEON, D., GRAU, J. A., ELLIS, D., SCHWEITZER, J. & HERRON, M. M. 1989. Geochemical logging with spectrometry tools. SPE Formation Evaluation, 4, 153-162. JACKSON, P. D., GUNN, D. G., FLINT, R. C., BEAMISH, D., MELDRUM, P. I., LOVELL,M. A., HARVEY,P. K. & PEYTON, A. 1997. A non-contacting resistivity imaging method for characterising whole round core at the well site. In: LOVELL,M., A. & HARVEY,P. K. (eds) Developments in Petrophysics, Geological Society Special Publications No 122, pp 1-10. JARRARD, R. D. • LYLE, M. L. 1991. High-resolution geochemical variations at ODP sites 723, 728 and 731: a comparison of X-ray fluorescence and geochemical logs In: Proceedings of the Ocean Drilling Program, Scientific Results, 117. 473-498. LOFTS, J. C. 1993. Integrated Geochemical and Geophysical studies of sedimentary reservoir rocks. PhD Thesis, University of Leicester. --, BEDFORD,J., BOULTON,H., VAN DOORN, J. A. & JEFEREYS,P. 1997. Feature recognition and the interpretation of images acquired from horizontal wellbores. In: LOVELL, M. A. t~ HARVEY, P. K. (eds) Developments in Petrophysics, Geological Society Special Publications No 122, 345-365. PEELING, R. 1992 Integrated Geochemical ,and Geophysical studies of the Earth's crust. PhD Thesis, University of Nottingham. SCHLUMBERGER. 1989. Natural Gamma-ray Spectroscopy. The Essentials of NGS Interpretation. Houston Tx. Schlumberger Educational Services, No 150. WORTHINGTON, P. F. 1989. Reservoir characterisation at the mesoscopic scale. SPWLA Annual Logging Symposium. Canada.

High-resolution core-log integration techniques: examples from the Ocean Drilling Program C. O. M A J O R , C. P I R M E Z , D. G O L D B E R G ,

& L E G 166 S C I E N T I F I C P A R T Y

L a m o n t - D o h e r t y Earth Observatory, Palisades, N Y 10964, U S A

Abstract: Cores offer the ability to describe lithological, physical, and chemical properties of rocks at the millimetre and smaller scale. However, continuous coring is expensive and only occasionally recovers 100% of the drilled interval. Microresistivity images of the borehole wall depict features down to the centimetre and smaller scale and can complement, or in some cases substitute for, core description as a means of geologic interpretation. This paper describes two techniques of integrating core data with borehole image and log data. Two case studies in carbonate rocks recovered during Ocean Drilling Program Legs 160 and 166 are presented. Microresistivity log images, grey-scale reflectivity from core photographs, and gamma ray logs are correlated at the centimetre scale over up to 300-metre cored intervals. Direct visual correlation of core photographs with borehole images and correlation of gamma ray measurements on core with downhole logs are shown to be complementary techniques. High-resolution core-log depth matching may be best achieved by correlating multiple datasets to reduce the error inherent in each and more precisely constrain depth matching. Depth matching of individual features allows a more accurate and consistent depth scale for use in quantitative stratigraphic analysis.

The advent of visual core-log integration software has opened the door to new possibilities for calibration and interpretation of borehole data. Accurate, high-resolution core-log integration allows the maximum information to be gleaned in cases where boreholes are both logged and cored. The comparison of cores to log images has been previously recognized as a valuable tool in stratigraphic analysis (Luthi 1990; Salimullah & Stow 1992). However, it has proven difficult and time consuming to systematically integrate and compare multiple datasets such as wireline logs, image logs, core images, and point measurements over long stratigraphic sections. A systematic analysis of these integrated data is highly desirable because it can provide a better understanding of a drill hole than is possible from the separate analysis of each of the individual datasets. Previous attempts at detailed core-log integration demonstrate the potential of integrated analysis of complementary core and log facies descriptions in intervals of incomplete core recovery (e.g. Hiscott et al. 1992; MacLeod et al. 1996; Pirmez et al. 1997). The potential benefits of such work also extend far beyond the scope of the present study, and includes orientation of core pieces for structural (Mathis et al. 1995), palaeomagnetic and palaeocurrent analysis (MacLeod et al. 1992), up-scaling of core

measurements, and improvement of vertical resolution in log data (Goldberg, 1997). With the development of logging tools capable of making high resolution measurements, particularly resistivity image logs such as the F o r m a t i o n M i c r o s c a n n e r (FMS; M a r k of Schlumberger) (Ekstrom et al. 1987), correlation on the scale of centimetres rather than metres or tens of metres has become possible. In addition to high-resolution measurements, imaging tools also provide a three-dimensional view of the borehole. This is essential for the identification and orientation of structural features such as fractures, dipping beds, and cross-beds. If these same features are recovered in cores, the log data can be interpreted in geological terms to determine sedimentary facies, regional stress patterns, and fluid migration paths. In lithified sediment, crystalline, igneous, and metamorphic rocks, core recovery is often incomplete. This is especially true in heterogeneous lithologies and in intervals where there is faulting or fracturing. In addition, deformation of core samples due to drilling (e.g. core discing and biscuit formation, drilling breccias) can result in incorrect interpretation of geological features if it is not properly identified (Kidd 1978). In such cases, image logs can be used to pinpoint the true depth and nature of bedding contacts and sedimentary structures while using

MAJOR, C. O., PIRMEZ,C., GOLDBERG,D. & LEG 166 SCIENTIFICPARTY 1998. High-resolution 285 core-log integration techniques: examples from the Ocean Drilling Program In: HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 285-295

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C.O. MAJOR E T AL.

Table 1. Logging runs and base logs used in processing for ODP Holes lO03D and 966F Hole

tool strings

base log

1003D

Sonic-DIT FMS IPLT GLT WST Quad combination FMS GHMT

HNGS from APS/HLDS/HNGS

966F

NGT from DIT/SDT/-LSS/HLDT/CNT/NGT

Tool string and log acronyms are as follows: DIT (dual induction tool), FMS (Formation MicroScanner), IPLT (Integrated Porosity Logging Tool), GLT (Geochemical Logging Tool), WST (Well Seismic Tool), GHMT (Geological Hi-resolution Magnetometer Tool), HNGS (Hostile-environment Natural Gamma Sonde), APS (Accelerator Porosity Sonde), HLDS (Hostile-environment Lithodensity Sonde), NGT (Natural Gamma Tool), SDT (Array Sonic Tool), LSS (Long-spaced Sonic Tool), HLDT (Hostile-environment Lithodensity Tool), CNT (Compensated Neutron Tool). core samples to constrain lithology. Indeed, one of the most important applications of detailed core-log integration is the ability to define a reliable, unified depth scale for all borehole data. This is particularly important for quantitative stratigraphic analysis and determining true event sequences.

The purpose of this study is to explore the uses and limitations of visual core-log integration for the Ocean Drilling Program (ODP), addressing the critical issues of core versus log resolution and depth constraints for poorly recovered lithologies. Logs and non-destructive physical property measurements on core samples provide high-resolution data which can be used to calibrate all datasets,~ High resolution data may also be used in conjunction with core photographs and visual descriptions to improve the geological characterization of a site. The examples discussed in this paper are from ODP Leg 160 in the eastern Mediterranean (Emeis et al. 1996) and ODP Leg 166 on the Bahama Bank (Eberli et al. 1997). Both examples comprise relatively undeformed carbonate lithologies in which core recovery ranged from zero to over 80%. Both sites display bedding on the centimetre to decimetre scale, which approaches the limit of the vertical resolution achievable with standard logs, but is well within the resolution of image logs. Unconformable surfaces, such as the one described at Site 966, are commonly characterized by dramatic changes in borehole properties within a few millimetres. These boundaries provide an important constraint on the depth of the recovered core. Although the lithologies from these two sites are similar, core recovery and log quality vary quite widely, allowing us to explore the useful-

ness of visual core-log integration techniques in a range of situations which are likely to be encountered in ODP drilling. We found that we could match features to the centimetre scale, and that with such detailed correlation, we could provide a much improved depth scale for core data. Data and methods The data used in this paper were collected in Hole 966F (ODP Leg 160) and in Hole 1003D (ODP Leg 166). Standard logs have undergone processing to remove borehole environmental and fluid effects. Corrections for varying tool speed have been applied to the FMS images. All logs, including images, have been depth-shifted to align manually picked correlation points between the gamma ray logs measured on different tool runs. Table 1 lists the tools deployed in each hole and the log chosen as the base log for depth matching between tool runs. All logs were depth-shifted by a constant amount to the ODP reference datum (sea floor). This shift was determined from the gamma ray log deflection at the sea floor at Site 966F and by comparing core and log gamma ray values just beneath the sea floor at Site 1003D. Further details of preliminary log processing are discussed in Emeis et al. (1996) and Eberli et al. (1997). Core images were scanned from black and white core photographs (split core) at 300 dpi (dots per inch) using 8 bits (grey scale). The digital image resolution corresponds to approximately 9x 10- 3 cm per pixel on the photograph (1:6 scale), or 5x10- 2 cm of actual core. This resolution proved adequate for the scale nor-

HIGH-RESOLUTION CORE-LOG INTEGRATION mally displayed on the computer monitor, as well as creating a manageable file size. Cores were assigned ODP drilling depths for the purposes of loading into the software. Standard ODP archiving procedure assigns the top of the core to the top of the interval advanced during coring, as measured by the length of the drill pipe. As the recovered core is generally assumed to be continuous, broken pieces are typically joined together and gaps are eliminated. Intervals of overlap (common in soft gassy sediments due to core expansion) are left uncorrected. The implications of this somewhat arbitrary depth assignment will be discussed later. After establishing a basis for visual correlation (explained in detail below), core sections were depth-shifted to attain the best visual match with FMS images in each cored interval. In addition to the log and image data, shipboard physical properties data, including natural gamma ray, G R A P E (Gamma ray Attenuation Porosity Evaluator) density, and plug porosity were used to compare with the wireline logs as an independent depth match. Uranium measurements (ppm) made by inductively coupled plasma mass spectrometry (ICP-MS) were also included for one sample per 1.5 m section of the cores from Hole 966F (Leg 160). In order to shift the physical property data with respect to the log data we treated physical and chemical property profiles in the same manner as core images, capturing the data into digital image format and assigning depths according to standard ODP conventions. The results from these two independent processes were then compared to evaluate the relative offset resulting from the two methods. All correlation and depth-shifting was done using the core-log integration software Diamage (trademark of Elf Aquitaine), which allows display of all core and log data, including images, at any scale, as well as differential depth-shifting of cores and logs for optimal correlation of geological features. Diamage also offers the ability to adjust image contrast in a dynamic fashion to highlight subtle lithologic changes. Details of the capabilities and methods of the Diamage software may be found in Mathis et al. (1995).

Results Hole lO03D, L e g 166, B a h a m a s Carbonate Bank Hole 1003D is located in 483 m water depth on the slope of the Great Bahama Bank. The

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scientific objectives for drilling the Bahama Bank were to establish a detailed carbonate stratigraphy and to study the effects of sea-level change in this slope environment. Hole 1003D was drilled to a depth of 1300 m, with moderate recovery over most of the section (average 44.7%) and excellent log quality. A 150m interval of cyclically-bedded Miocene pelagic carbonates 750-900 mbsf (metres below sea floor) was selected for use in this study on the basis of its fair-to-good core recovery and significant variations in physical properties on a fine ( < 1 m) scale (Fig. 1). Coring and logging results from Leg 166 are discussed in detail in Eberli et al. (1997). Gamma ray measurements in core indicated that darker, more bioturbated layers generally had a higher natural radioactivity, due almost exclusively to uranium, than the paler interbeds. Comparison of the FMS images with the gamma ray log showed a similar relationship in the logs: less resistive layers (dark in FMS image) correspond with relatively high gamma ray values, whereas more resistive layers (light in FMS image) correspond with relatively low gamma ray values (Fig. 2). The lower resistivity in the dark layers is likely related to the development of mouldic porosities by dissolution, possibly linked to degradation of organic carbon (Eberli, et al. 1997). To confirm the relationship of gamma ray to resistivity, we also compare the resistivity (SFL) and gamma ray (SGR) logs and find them to be anticorrelated. An increase in thorium in the less resistive intervals also indicates a higher clay content. These observations are consistent with less cemented, porous, clay- and organic-rich beds alternating with more cemented, less porous, and pale carbonate layers. Shipboard core descriptions indicated that burrows in the dark layers were commonly flattened, while those in the pale intervals were often well-cemented. Cementation in the pale layers was likely the result of early diagenesis that indurated this sediment and prevented further compaction (Eberli et al. 1997). Because core recovery was less than 100% in all cores, correlation between cores and log images requires depth shifting of core sections, each less than or equal to 1.5 m in length, downward within each 10m interval. In a few cases, a slight upward shift achieves the best fit; this may be explained either by debris filling in the borehole between successive cores or by errors in calculation of the depth. In cases where there is little apparent drilling disturbance, we keep adjacent core pieces together, assuming little to no loss of material between sections. In

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Fig. 1. Summary of lithostratigraphy for Hole 1003D (ODP Leg 166) showing core recovery, major rock types, stratigraphic sequences, and logs (caliper, gamma ray, shallow resistivity, and sonic velocity). Estimated ages of the sequence boundaries are: M-- 15.1 Ma, L-- 12.7 Ma). (Figure adapted from Eberli et al. 1997)

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Fig. 2. Best fit position for core 166-1003D-32R, showing the relationship of core grey-scale reflectance with FMS resistivity. Core colour corresponds well with the electrical resistivity as illustrated by the white-brown tones on the FMS image and by the shallow spherically-focused resistivity log (yellow curve). The best fit is confirmed by a good match of the core gamma ray with the downhole gamma ray (HSGR). Darker layers in the core and FMS have higher gamma ray counts. Note that best fit of core with log data requires splitting both the core and core gamma ray curves mid-section (between core biscuits), indicating that material was lost during drilling. 'X' marks in the core image columns indicate intervals with no corresponding core. cases where there is obvious drilling disturbance and biscuiting, we allow a greater amount of space between adjacent sections, assuming that some volume of material was likely to have been lost during the drilling process. In some cases, the best match requires that spaces be left between adjacent core sections (Fig. 2). The gradational nature of the contacts between cemented and porous beds introduces some uncertainty because the sediment colour is not linearly related to the change in resistivity between different layers. Where there is no clear visual correlation between FMS and core images, we rely on the core and log gamma ray comparisons to further constrain depth matches. By depth matching

gamma ray logs with the FMS, it is clear that not all gamma ray peaks are associated with dark layers, and that not all dark layers have associated gamma ray peaks. We find that independent depth matching of visual characteristics in the core photos and FMS images (e.g. bed thickness, degree of cementation) is the same (within a few centimetres) as shifting the logs based on gamma ray correlations alone, even in cases where core sections must be separated to correct for lost material. It is important to note that gamma ray measurements made near the core section edges and in fragmented intervals give unrealistically low readings because of their smaller sampling volume (Lyle et al. 1996). This may explain

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Fig. 3. Summary of the lithostratigraphy for Hole 966F (ODP Leg 160), including core recovery, major rock units, biostratigraphically determined ages, and logs (caliper, gamma ray, shallow resistivity, and sonic velocity). some mismatch between gamma-ray amplitudes from the core and logs.

Hole 966F, Leg 160, Eratosthenes Seamount Hole 966F was drilled in 923 m water depth on

the crest of the Eratosthenes Seamount. a prominent bathymetric high south of Cyprus in the eastern Mediterranean (Emeis et al. 1996). The Eratosthenes Seamount is currently in the process of entering the subduction zone beneath Cyprus. The primary scientific objective for this

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Fig. 4. Best fit position of core 160-966F-26R (shifted core) compared with unshifted core (drilled depth), FMS, and logs (porosity (NPHI) resistivity (SFLU), and spectral and computed gamma ray (SGR and CGR)). 'A' indicates a substantial peak in the resistivity log which correlates with the white area in the FMS immediately above the unconformity; 'B' indicates a substantial peak in natural gamma ray which corresponds with the bituminous limestone immediately underneath the unconformity (see text). Uranium measurements on core (blue bars in unshifted core column) indicate that the highest gamma radiation occurs within the bituminous limestones. 'X' marks in the core image columns indicate intervals with no corresponding core.

site was to determine the origin and geologic history of the seamount. Coring rocks as old as middle Eocene, Hole 966F penetrated a wide variety of depositional and diagenetic carbonate facies from Miocene shallow coralgal limestones to middle Eocene foraminiferal chalks (Fig. 3). Core recovery was quite poor over large intervals, averaging only about 23% for the interval over which FMS logs were run (77-356 mbsf), and often consisting of only a few pieces per core. In the latter case, cores provided only a general indication of the lithology with little or no information about sedimentary structure or tectonic fabric. Correlation of the recovered core material

with FMS images in a manner similar to that used for 1003D allows us to determine the nature of the transitions between various lithostratigraphic units. We are able to pinpoint the exact depth of the major unconformities and lithostratigraphic boundaries where core recovery is incomplete (Fig. 3). Visual integration proves most useful in pinpointing the depth of an unconformity that appears in the core as an abrupt change from a clean, Miocene biosparite to a middle-Eocene bituminous calcilutite. Figure 4 shows core 160-966F-26R placed next to an FMS image at the position of best fit visual correlation, placing the unconformity at 300.9 mbsf. The drilled depth for this core is nearly

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Fig. 5. Fractured core intervals from Hole 966F. Areas of more intense fracturing correlate with lighter coloured patches in the FMS image. These features are interpreted as cherts.

2 m above the level of this match, indicating that the recovered material can not be assumed to come from the upper part of the cored interval. The correct characterization of the unconformity requires identification of its depth, the nature of the change in physical and chemical properties (based on the core), and the hiatus represented by microfossil assemblages. It is important to note the lack of correlation between the uranium measured in core samples and the uranium log at this best fit position. The peaks in the resistivity log and the neutron porosity log occur 0.5m above a resistive (cemented) layer in the FMS. If these logs are shifted down 0.5 m, the large (8 ppm) peak in the gamma ray log correlates well with the darkest (most organic-rich) interval in the bituminous limestone just below the unconformity. By tying the top of the Eocene section to this unconformity and achieving a good match in the uppermost section of the bituminous limestones,

we are able to confidently match other features deeper in the hole. Below the unconformity, resistive layers and halos apparent in the FMS images are interpreted as lenses and nodules of chert. Although chert was only rarely recovered, the depths where it does appear correspond well with bright patches in the FMS images (Fig. 5). In addition, intervals with abundant bright patches in the FMS images correspond to highly disturbed and fractured intervals in the cores. This may be interpreted as the result of drilling disturbance caused by the large contrast between hard chert and soft micritic limestone.

Discussion In order to establish a depth match between core and log data it is necessary to be aware of several methodological limitations. Visual core-log integration is limited by the quality and percentage

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Table 2. Table showing the amount of core offset from drilled depth required to achieve the best correlation with the F M S images and the gamma ray log for Hole IO03D. Note that the drilled depth is a rather poor estimate of the true depth in this hole

core number

32 33 34 ' 35 36 37 38 39 40 41 42 43 44 45 46 average offset standard deviation

drilled depth (mbsf)

visually shifted depth (mbsf)

difference (visual) (metres)

gamma ray shifted depth (mbsf)

difference (gamma) (metres)

751.6 761.2 770.8 780.4 790.1 799.8 809.4 819.0 828.6 838.2 847.9 857.5 867.1 876.7 886.3

751.3 762.3 771.8 784.7 790.1 800.2 810.6 820.2 831.8 838.5 848.8 858.3 868.4 877.7 887.2

-0.3 1.1 1.0 4.3 0.0 0.4 1.2 1.2 3.2 0.3 0.9 0.8 1.3 1.0 0.9 1.15 1.17

751.2 762.3 771.8 784.4 790.6 800.2 810.5 820.1 831.9 842.2 848.7 858.1 868.3 877.7 887.3

-0.4 1.1 1.0 4.0 0.5 0.4 1.1 1.1 3.3 4.0 0.8 0.6 1.2 1.0 1.0 1.38 1.31

of core recovery, which varies widely between different lithologies, and in the worst case results in sparse or indeterminate data. The highest core recovery, in typical ODP drilling environments, is generally in soft cohesive sediments such as calcareous ooze. Logging, however, and particularly FMS logging, achieves maximum performance in more consolidated materials where borehole conditions tend to be better and resistivity contrasts higher. Low recovery can severely hamper depth correlation. Recovery of 10% of the cored interval will result in 90% (or up to -t-4.3 m) error in the assigned depth of the core. The default assumptions that the recovered rocks come from the upper part of this interval and that there is negligible loss of material between or within (disturbed) core sections must be considered carefully before depths are assigned. Based on our observations, these assumptions are often invalid. Any error associated with incorrect depth assignment in sedimentary environments has large implications for quantitative stratigraphy. The sedimentation rate and stratigraphy in holes with poor recovery, particularly in marine pelagic sediments, may be miscalculated by several hundred thousand years, due only to depth error. Agrinier & Agrinier (1994) developed a probabilistic approach to assign depth to cores and suggest that core material is most likely to come from the middle of a cored interval, contradicting the assumption that it comes from the upper part. Our results indicate

that the offset between the drilled and logcorrelated depth is likely to vary in a manner which is not predicted by the probabilistic models nor consistent with the current default assumptions. Table 2 shows the amount of offset from the drilled depth that is required to attain the best visual correlation with the FMS for Hole 1003D; in poorly recovered sections, the drilled depth is a rather poor estimate of the logcorrelated depth. Such depth shifts may also be applied to core physical properties data, which can then be used for the calibration of log responses. Routine log processing for the Ocean Drilling Program involves the creation of a single depth scale for different tool passes, usually by comparison of gamma ray logs among different tool strings. These gamma ray logs are aligned by means of peak matching with linear stretching or squeezing of the depth scale applied between tie points. The use of a linear operator may result in imprecise correlation between gamma ray peaks from different tools, particularly if tie points are widely spaced, because the tools travel at non-constant speeds due to stickslip motion and incomplete ship heave compensation. The FMS data are initially depth corrected using accelerometer and image-correlation algorithms (Serra 1989); these data are then treated the same as the other logs, i.e., they are shifted and resampled according to gamma ray tie-points. This peak matching method can result in an

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offset between logs and images, because the same depth corrections are not applied to each log. The maximum offset between peaks due to imprecise gamma ray correlations is on the order of a few tens of centimetres, which for most applications using low-resolution log measurements is negligible. For high-resolution core-log integration, however, it is important that all data have a consistent depth scale and that minor offsets due to processing not be taken to be geologically significant. In Hole 966F, for example, the 0.5m offset between the gamma ray log and the FMS suggests incorrectly that a gamma ray peak occurs above rather than below a major unconformity. This is probably the result of depth-shifting procedures during processing. The correct depth placement of the unconformity is constrained by core data (ICPMS) and comparison with the resistivity log. A processing procedure which consistently uses the gamma ray log from the FMS tool as the base log will help to minimize such processing-related offsets. We found that it is very useful to use standard logs to constrain depth matches in the cases of ambiguous visual correlations. The two sedimentary rock examples presented in this paper illustrate that the gamma ray log is most closely correlated with changes at the macroscopic scale in the core (e.g. colour and bedding). Dark colour in the core is often related to high amounts of organic material, which are in turn associated with concentrations of uranium due to locally reducing environments. Independent depth shifts from visual image correlation and gamma ray peak matching are equivalent within a relatively small margin of error (the difference in average offset is 23cm for Hole 1003D, and only 2 cm if the outlier value for core 41 is not included in the average). Other physical properties such as density, porosity, susceptibility, and velocity, which are routinely measured on core sections, are potentially a valuable means for measurement calibration of log responses once their depth scales have been adjusted. The integration of various log and core data to a common depth scale is the basis for ODP's CLIP (Core-Log Integration Platform) software (P. deMenocal pers. comm.). Peak matching within intervals of cyclic sediment deposition can often make use of amplitude as well as spacing between peaks as a means of correlation. This helps to avoid miscorrelation when the wavelength of variation is much shorter than the length of the cores. Comparison of amplitude variations in images, however, particularly between measurements of different sediment properties (colour reflectance for core images

and electrical resistivity for FMS images) are more difficult to compare on a quantitative basis. Visual core-log correlation, therefore, must be undertaken carefully to provide unequivocal results. Nevertheless, visual core-log integration is potentially the best means available for orienting cores and providing information about in situ stress. Split core image interpretation is limited because the true dips of sedimentary and structural features in unoriented cores, such as cross-bedding, faults, and fractures, cannot be calculated in two dimensions. Core orientation requires 3-D, circumferential scans of the outer core surface to be compared to borehole images (e.g. Mathis et al. 1995). Unfortunately, in unlithified or poorly lithified sediments it is difficult to image the outer surface of the core, and the majority of ODP sites encounter such unlithified materials in the upper few hundred metres below the sea floor. The high probability of disturbing sedimentary features while coring, as well as the coating of drilling mud and smeared sediment which cakes the outer surface of these cores, makes 3-D core imaging impossible. In competent rock, large open or cemented fractures can be seen using image logs, although cores drilled in such environments are commonly highly disturbed and fragmented. As a result, core orientation can only be achieved on a piece by piece basis in most ODP environments.

Conclusions We find that the application of two independent techniques is most successful in integrating core and log data in lithologies with moderate core recovery. Gamma ray and other standard logs may be used to constrain sediment properties in intervals of poor core recovery by calibration or core measurements to logs in intervals where a good match can be made. In order for visual core-log integration to be successful, it is essential to understand the relationship between the physical properties measured in situ by the logging tools and the colour and structural properties of the formation as represented in core photographs. In the carbonate lithologies discussed in this paper, core colour correlates well with the resistivity logs. This observation establishes the basis for matching the cores to FMS images. Both display centimetre scale information about the circumferential and vertical variability of the formations encountered in the drill hole. Our depth matches between core and log images indicate that incompletely recovered core

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Initial Reports of the Deep Sea Drilling Project, v. 42, part 1: Washington (U.S. Gov't Printing Office), 1143-1149. LUTHI, S 1990. Sedimentary structures of clastic rocks identified from borehole images. In: HtJRST, A., LOVELL, M. A. & MORTON,A. C. (eds) Geological Application of Wireline Logs. Geological Society Special Publications No. 48, 3-10. LYLE, M., BRISTOW,J. BLOEMENDAL,J. & RACK, F. R. 1996. Comparison of natural gamma ray activity profiles from downhole logging and the MST core logger at Site 911 (Yermak Plateau). In: THIEDE, J., MYttRE, A. M., FIRTH,J. V., JOHNSON,G. L. & RUDD1MAN,W. F. (eds) Proceedings of the Ocean Drilling Program, Scientific Results. Ocean Drilling Program, College Station, TX, 151, 369-376. MACLEOD, C. J., CELERIER, B., FRUH-GREEN,G. L. & MANNING, C. E. 1996. Tectonics of Hess Deep: A synthesis of drilling results from Leg 147. In: MEVEL, C., GILLIS, K. M., ALLAN,J. F. & MEYER, P. S. (eds) Proceedings of the Ocean Drilling Program, Scientific Results. Ocean Drilling ProWe gratefully thank the crew and scientific parties of gram, College Station, TX, 147, 461-475. ODP Legs 160 and 166 for their efforts in acquiring the PARSON, L. M., SAGER, W. W. • the ODP Leg datasets used here. The manuscript was improved by - 135 Scientific Party, 1992. Identification of the helpful comments of Lars Boldreel and an tectonic rotations in boreholes by the integration anonymous reviewer. Funding for this work was of core information with Formation MicroScanprovided by the Joint Oceanographic Institutions, Her and Borehole Televiewer images. In. HURST, inc., through grants USSSP 160-20912 to Major and A., GRIEFITHS,C. M. 8s WORTHINGTON,P. F. (eds) USSSP to 166-F342 to Pirmez. This is LDEO Geological Applications of Wireline Logs H contribution #5787. Geological Society Special Publications No. 65, 235-246. References MATHIS, B., HALLER, D., GANEM, H. 8~; STANDEN, E. 1995. Orientation and calibration of core and AGRINIER, P. & AGRINIER, B. 1994. On the knowledge borehole image data. SPWLA 36th Annual Logof the depth of a rock sample from a drilled core. ging Symposium. Scientific Drilling, 4, 259-265. NURMI, R., CHARARA,M., WATERHOUSE,M. & PARK, EBERLI, G. P., SWART,P. K, MALONE, M. et al. 1997. R. 1990. Heterogeneities in carbonate reservoirs: Proceedings of the Ocean Drilling Program, Initial detection and analysis using borehole electrical Reports. Ocean Drilling Program, College Staimagery. In: HURST, A., LOVELL, M. A. & tion, TX, 166. MORTON, A. C. (eds) Geological Application of EKSTROM, M. P., DAHAN, C. A., CHEN, M., LLOYD, P. Wireline Logs. Geological Society Special PubM. & Rossi, D. J. 1987. Formation imaging with lications No. 48, 95-111. microelectrical scanning arrays. Log Analyst, 28, PIRMEZ, C., HISCOTI', R. N. & KRONEN,J. O., Jr, 1997. 294-306. Sandy turbidite successions at the base of EMEIS,K.-C., ROBERTSON,A. H. F., RICHTER,C., et al. channel-levee systems of the Amazon Fan re1996. Proceedings of the Ocean Drilling Program, vealed by FMS logs and cores: Unraveling the Initial Reports. Ocean Drilling Program, College facies architecture of large submarine fans. In: Station, TX, 160. FLOOD, R. D., PIPER, D. J., KLAUS, A. & GOLDBERG, D. 1997. The role of downhole measurePETERSON, L. C. (eds) Proceedings of the Ocean ments in marine geology and geophysics, Reviews Drilling Program, Scientific Results. Ocean Drilof Geophysics, 35, 315-342. ling Program, College Station, TX, 155, 7-33. Hiscoa~r, R. N., COLELLA,A., PEZARD, P. A., LOVELL, SALIMULLAH,A. R. M. & STOW, D. A. 1992. ApplicaM. A. ~; MALINVERNO,A. 1992. Sedimentology of tion of FMS images in poorly recovered coring deep-water volcaniclastics, Oligocene Izu-Bonin intervals: examples from ODP Leg 129. In: forearc basin, based on formation microscanner HURST. A., GRIFFITHS, C. M. 8~; WORTHINGTON, images. In: TAYLOR,B., FUJIOKA, K. & JANECEK, P. F. (eds) Geological Applications of Wireline T. R. (eds) Proceedings of the Ocean Drilling Logs H. Geological Society Special Publications Program, Scientific Results. Ocean Drilling ProNo. 65, 71-86. gram, College Station, TX, 126, 75-96. SERRA, O. 1989. Schlumberger formation microscanner KIDD, R. B. 1978. Core-discing and other drilling image interpretation. Schlumberger Educational effects in DSDP Leg 42A Mediterranean sediment Services. cores. In: Hsu, K. J. & MONTADERT, L. et al.

material c a n n o t be assumed to come from the top o f the c o r e d interval, n o r can it be theoretically or statistically predicted to come from a particular location within the section. In addition, we find that core material is often missing within and between sections where drilling d e f o r m a t i o n and biscuiting exists. G o o d depth matching must be based on corescale features and requires accurately depth corrected logs. If such data are available, it is possible to attain more precise log response calibrations, log resolution-matching, core orientation, and up-scaling o f core measurements. A n integrated approach using visual core-log correlation supported by the quantitative comparison of core physical properties with logs is therefore r e c o m m e n d e d in similar O D P environments.

Multi-scalar structure at D S D P / O D P Site 504, Costa Rica Rift, I: stratigraphy of eruptive products and accretion processes M. A Y A D I l, P. A. P E Z A R D 1, C. L A V E R N E 1 & G. B R O N N E R 2 t P~trologie Magmatique, C N R S ( U M R 6635), CEREGE, BP80, 13545 Aix-en-Provence, France 2 Labora toire de G~ophysique- G{odynamique, Universitd d'A ix- Marseille III, Facult~ des Sciences de Saint-Jdrdme, 13397 Marseille cedex 20, France.

Abstract" Hole 504B is located about 200 km south of the Costa Rica Rift and constitutes the reference section for the structure of the upper oceanic crust. Compared to core, the continuous electrical resistivity (at m scale) and the high-resolution electrical images (at cm scale) recorded in Hole 504B, provide a continuous and detailed lithostratigraphic description of the effusive section at Site 504. Flow thicknesses measured from cm scale FMS images average 0.5 (• The massive units, known to bound fluid circulation at large scale into the crust, are constituted with a series of 20 to 50 individual flows. If Site 504 was created over two volcanic cycles, each volcanic cycle allows the emplacement of [0.60 (+0.30)] x106 m 3 of magma per m along the ridge axis. This computation leads to an estimate of magma volume for a single eruption of [0.003 (+0.001)]• l06 m 3 per m along the ridge axis, and eventually, a gradient in magma pressure within the magma chamber lens of 52 (+26) MPa, appropriate for one eruption.

The main objective of drilling at D S D P / O D P Site 504 is to study the nature of young oceanic crust. This was achieved with the gradual drilling of Hole 504B ( C R R U S T 1982; C a n n e t al. 1983; A n d e r s o n et al. 1985; Becker et al. 1989; Becket et al. 1992; Dick et al. 1992; Alt et al. 1993). The hole is located in 5.9 Ma old crust, about 200 k m south of the Costa Rica Rift, the e a s t e r n m o s t s e g m e n t o f the G a l a p a g o s or 'Cocos-Nazca' spreading centre (Fig. 1). The Costa Rica Rift spreads asymetricallyl with an intermediate rate of about 3 . 3 c m v r (a halfrate of 3 . 6 c m y r -1 to the south, and 3 . 0 c m y r 1 to the north; Hey et al. 1977). Hole 504B is located in the middle of a spreading segment, more than 70 km away from the nearest major E c u a d o r and P a n a m a fracture zones. After s e v e n D S D P a n d O D P legs, it e x t e n d s 2111.0m below sea floor (mbsf), in a water depth of 3460 m. The drilling goes through 275 m o f s e d i m e n t s , a b o u t 6 0 0 m o f volcanic products consisting of pillows, massive flows, breccias and a few dykes; a transition zone to a thick sheeted dykes complex (at least 1000m thick; Fig. 2). Hole 504B is the only well in ocean basins to penetrate through the entire volcanic section and into the underlying sheeted dyke complex. F o r this reason it has become a reference section for the physical and chemical

Fig. 1. Location of Site 504 on the south flank of the Costa Rica Rift, Panama basin. structure of the upper oceanic crust. D S D P and O D P efforts at this site have produced rock samples and d o w n h o l e measurements supporting the ophiolite model. Ophiolite sequences have been described on land by Gass & Smewing (1973), Coleman (1977), K i d d (1977) and others, as being composed by sediments (Seismic Layer

AYADI, M., PEZARD,P. A., LAVERNE,C. & BRONNERG. 1998. Multi-scalar structure at DSDP/ODP Site 297 504, Costa Rica Rift, I: stratigraphy of eruptive products and accretion processes In. HARVEY,P. K. ~fr LOVELL, M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 297-310

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Fig. 2. Schematic of Hole 504B drilling history and lithostratigraphy after Leg 148. 1), and basaltic pillows or flows (Seismic Layers 2A and 2B), underlain by sheeted dykes (Seismic Layers 2C and 3). On the sea floor, mid-ocean ridges are characterized by along-strike changes in morphology with the presence of fresh volcanic edifices contrasted with that of intensively tectonized regions. Such observations lead several authors (Robinson et al. 1973; Klitgord & Mudie 1974; Lewis 1979; Gente et al. 1986; Kappel & Ryan 1986; Gente 1987; Macdonald & Fox 1988) to conclude that on-axis volcanism is episodic, and that accretion processes correspond to volcano-tectonic periods. Such periods are composed of distinct cycles associated with the presence or absence of volcanic activity. The volcanic cycle is a time of construction within the axial valley, also refered to as 'neovolcanic zone' (NVZ) in the literature (Normark 1976; CYAMEX 1981; Ballard et al. 1981; Kappel & Ryan 1986). At an intermediate spreading rate (5.0 to 9.0 cm yr-1), active volcanism along midocean ridges is restricted to a relatively narrow

region (0.6 to 1.2km wide; Normark 1976; CYAMEX 1981; Ballard et al. 1981; Kappel & Ryan 1986). On both sides of the axial valley, a wider zone (from 1.0 to 2.0 kin) where the volcanism is absent is found occupied by older lava cut by numerous fissures. Each zone constitutes the inner part of an even larger region often described as the 'tectonic zone' (Lonsdale 1977; CYAMEX 1981; Choukroune et al. 1984; Edwards 1991). The present paper concentrates on the study of effusive products penetrated in Hole 504B, corresponding to the uppermost thousand metres of oceanic crust. During ODP Legs, an extensive suite of in situ experiments including the recording of electrical resistivity profiles with the Dual Laterolog (DLL) tool and highresolution electrical images by the Formation MicroScanner (FMS) tool was conducted in Hole 504B. First, the continuous downhole data are used here to improve the lithostratigraphy derived from core. As the accuracy of the corederived lithologic column is directly dependent upon recovery (up to a maximum of 25% in the extrusives of Hole 504B), it is naturally biased toward the characteristics of units which are more likely to be recovered. In this context, the continuous nature of downhole measurements is of particular importance and allows for a more accurate description of the penetrated structure. Second, the relative volumes of extrusives emplaced in a cyclic manner within the neovolcanic zone (NVZ) can be estimated. The ridge axial morphology and these estimated volumes may provide constraints on the magma chambers in terms of pressure and stress which regulate accretion processes.

Lithostratigraphic analysis At the spreading axis, a volcanic cycle starts with a large eruption characterized by the emplacement of massive flows (Pezard et al. 1992). Then, more viscous lavas equating to slower eruption rates erupt, forming pillows and thin flows (Bonatti & Harrison 1988). Pillows are a classic submarine lava form composed of 'elliptically' shaped pods of basalt. Upon eruption, the pillows are rapidly cooled which may partially or completely fragment the pillow, the debris of which eventually forms haloclastite breccias. Pillows are usually transected by numerous fractures which can be either radial or parallel to curved outer surfaces. These fractures are open or filled by low temperature alteration products (e.g. clay minerals, iron hydroxides, zeolites, carbonates). Both the base and top of massive flows present thin chilled margins,

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Fig. 4. FMS micro conductance derived from one pad for the interval spanning from 520 to 555 mbsf, where the different lithotypes (MF: massive flows, TF: thin flows, P: pillows and D: dykes) are well defined. The flow limits within each lithological unit level (22, 23, 24, and 25) are represented in this section. (a) Raw data; (b) interpreted data.

Fig. 3. (a) Electrical resistivity measurements (LLs and LLd) recorded with the dual laterolog (DLL) in Hole 504B (Alt et al. 1993).

although the tops often have a rubbly appearance resulting from fracturing and fragmentation during cooling. The internal part of the flow is massive, often crystalline and transected by planar fractures. Core description

In Hole 504B, four different lithological types have been defined using the parameters of changing grain size, occurrence of glassy margins, and fracturing (Adamson 1985). Besides pillows and massive flows, thin flows and dykes are defined from lithological core descriptions. Thin flows are recognized by homogeneous areas of core which are thicker than the average

of pillow, and formed by fine- to mediumgrained basalts. A dyke is a unit which shows one or two chilled intrusive margins. A series of constraints on the structure of the upper crust is derived from alteration features determined by mineralogical, petrological and chemical studies. The boundaries between the three main alteration zones described in the effusive section of Hole 504B (Honnorez et al. 1983; E m m e r m a n n 1985; Alt et al. 1985, 1986a,b; Laverne 1987; Laverne et al. 1989) are presented by Fig. 3. The upper pillow alteration zone (UPAZ) is characterized by oxidative alteration due to the reaction of basalt with seawater at high water-rock ratio and low temperature. The lower pillow section (LPAZ) is characterized by a non-oxidative alteration due to reactions at lower water-rock ratios and slightly higher temperatures (up to l l0~ The boundary between the LPAZ and the zone altered under greenschist facies (GFAZ) conditions has been located at 898 mbsf (Emmermann 1985; Alt et al. 1985, 1986a,b; Laverne 1987), with a transition zone to LPAZ alteration facies located above (Fig. 3). The abrupt transition from oxidative seawater alteration (UPAZ) to a

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M. AYADI ET AL. spanned by the core from which it was extracted (typically 9m), most of the units are readily identified on the electrical resistivity and FMS profiles purely from electrical properties. This identification is further constrained by sequences of events in the core which must be respected in the continuous dataset. A few examples of log signatures for well defined lithologic units (with good core recovery) are now described in an attempt to understand the small-scale signal recorded by the resistivity sensor, and that recorded by the FMS sensor. The main difficulty comes from units absent from the core, necessarily interpreted by default after comparison with the signature of nearby similar units. Downhole m e a s u r e m e n t s Electrical resistivity ( D L L ) . The average resistivity value measured with the DLL in the upper basement is about 10.0f2m (Fig. 3). As this crustal section corresponds to eruptive products, each interval with a resistivity of 10.0 9t m or less was associated with the presence of pillows (P), whereas intervals with higher resitivities were associated to a massive flow (MF) when the apparent thickness of the unit exceeds 4.0 m, and a thin-flow (TF) otherwise. The terminology 'dyke' (D) was used when a near-vertical margin was identified in the core, also for a unit with a resistivity larger than 10.0 f~ m.

Fig. 5. (a) Electrical resistivity measurements (continuous line corresponds to LLd and dashed line corresponds to LLs) and (b) FMS record for short intervals including Unit 2D (at the top), Unit 27 (in the middle) and Unit 34 (in the bottom). The Units 27 and 34 are composed of two massive flows parts separated by a thin pillow flow layers.

reducing environment (LPAZ) corresponds to a permeability barrier of significant lateral extent (Pezard 1990), which consists of the successive massive flows defining the lithological Unit 27 (Cann et al. 1983; Adamson 1985). In order to be able to discriminate the various lava types, we associated each particular signature in downhole measurements with a given lithotype: pillows (P), thin flows (TF), massive units (MF), or dykes (D). Since any unit described in core is present on the continuous geophysical record within the depth interval

Electrical microconductivity ( F M S ) . The data are recorded by the Formation Microscanner (FMS; Ekstrom et al. 1986; Luthi & Banavar 1988; Pezard et al. 1990) as a series of curves that represent relative changes in microconductance of the rock caused by either (1) varying electrolytic conduction as a function of fluid type, and/or pore volume topography, or (2) cation exchange on surfaces of clay and other conductive minerals. Data processing is required to convert the raw data into images representative of electrical resistivity changes. This includes conversion of current intensities to variableintensity grey or colour. In the former, black is the lowest resistivity and white the highest. The microconductance curves are used to identify lithological types or flow limits, and to obtain a detailed lithostratigraphic interpretation of the eruptive section of Hole 504B (Fig. 4). In this study, only records obtained from two FMS pads are used for the lithostratigraphic interpretation. The main advantage of the FMS, over traditional measurements such as the DLL, is to allow the identification at cm-scale of individual flow limits within each lithologic unit,

STRATIGRAPHY AT DSDP/ODP SITE 504 AND ACCRETION PROCESSES and then to deduce the thickness of each individual flow. The different lithotypes (MF, TF, P and D) are distinguished from FMS records on the basis of micro-conductance changes only. We chose here to describe the four lithotypes especially well defined from core over a short section located from 520 to 550 mbsf (Fig. 4): massive flows (MF) have a signature with little variability (units 2D, 24, 27 and 34; Figs 4 and 5); pillow lavas (P) have an irregular signal signature, due to a more fractured and brecciated structure; thin flows (TF) are characterized by signals intermediate between that of massive flows and pillows; dykes (D) are solely identified from core. Results Massive units. Lithological units 2D, 27 and 34, are located in the upper part of Hole 504B and used here as examples to characterize the lithological structure of massive units (Fig. 5). These three massive units are identified from nearby pillows due to a sharp resistivity increase, and abrupt changes in FMS signal signature (from irregular in pillows to more regular in the flow), in front of the chilled margins. Unit 2D was described as two 'sparsely to moderately phyric basalt flows' (Cann et al. 1983). The two separated flows are observed directly in the electrical resistivity by a slight decrease at about 320 mbsf. This depth also corresponds to a signal change in FMS profiles (Fig. 5a). Unit 27 was described as a 'massive flow with grain size changes from fine (at the top and bottom) to medium and coarse toward the center' (Fig. 5b). Unit 34 was described from core as a 'massive, coarse-grained basalt with a glassy margin at the top' (Fig. 5c). On FMS records, the latter two units appear as made of two major flows separated by 1.1 m (Unit 27) and 2.2 m thick pillows (Unit 34) not recovered by drilling. The thicknesses deduced from FMS data of massive flows (without pillow layers) constituting, respectively Units 2D, 27 and 34 are 11.7, 13.5, and 20.5m. The thin pillow layers located within Units 27 and 34 are also present in DLL data, with a sharp resistivity decrease (Fig. 5). This sharp resistivity decrease is opposed to that, more subtle one, obtained toward the centre of each major unit and interpreted as related to the observed increase in grain size centreward of the flow, related to cooling mechanisms after emplacement (Pezard 1990). From DLL data, Unit 34 was described by Pezard (1990) as an individual unit (from about 673 to 683 mbsf), as opposed to two individual units separated by a pillow layer. This

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latter description is confirmed by FMS and DLL data recorded during Leg 148 (Fig. 5). Such lithological structure is also observed within Units 27 and 39 (Fig. 8). The nature of the FMS signal and the resistivity value (about 10.0f~m) obtained in the thin layer between flows leads one to believe that pillows, undetected in the core, are present between each of the two major flows. This thin pillow unit is not observed within Unit 2D, located in the upper part of the extrusive section, although the limit between the two separate flows is found in both FMS and DLL records (Fig. 5). This may be explained by the fact that Unit 2D was emplaced near the end of the construction of Site 504, further from the axial domain where pillows originate, than deeper, thus earlier, Units 27 and 34. In conclusion, the identification of lithotypes from FMS and DLL in the massive Unit 27 and 34 allows the definition of new lithological unit boundaries with respect to that initially proposed from core or by Adamson (1985). Moreover, m scale DLL and cm-scale FMS data present details that were not observed in the core. Besides lithologic Units 2D, 27 and 34, other massive units such as 24 and 39 are detailed and described in the following in the context of axial volcanism and accretion episodicity. Lithostratigraphical logs. From the study of variations of electrical resistivity values with depth (Fig. 3), the analysis of FMS data (Fig. 4) and a comparison to core, it is possible to reconstruct a more detailed lithostratigraphic log of the penetrated basement (Fig. 6b, c). Each section of the continuous record is associated with a recovered unit, keeping the terminology defined in hand-specimen by Adamson (1985; Fig. 6a). The units missed in the core are identified in their lithotype, but are not given a new unit number. The DLL analysis method is based on absolute resistivity variations, whereas the FMS analysis method is based on the variation of signal only. For this reason, the DLL-derived log (Fig. 6b) and the FMS-derived one (Fig. 6c) are sometimes found to provide slightly different depths of unit boundaries. From DLL and FMS data, the analysed section appears to be composed largely of pillows (about 70% of the total thickness), whereas thin and massive flows contribute to the overall thickness by about 25%. Dikes contribute to the remaining 5%, a low value possibly due to poor core recovery. The resistivity-derived lithostratigraphy reveals about 20 % more pillows than the core, as a value of 57% was obtained (Alt et al. 1996). This result is

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~,<

o

~

=2

9

0

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Fig. 7. Histogram of basaltic flows thickness (MF: massive flow, TF: thin flow, P: pillows) derived from FMS. The thickness averages for MF, TF and P are 0.6m, 0.5 m and 0.4m, respectively.

related to the fact that massive units are recovered more easily than fractured and altered pillows. As a consequence, the most altered intervals are clearly less adequately sampled during coring. Individualflow thickness. In an attempt to obtain a quantitative interpretation of basement lithostratigraphy, we may consider individual flow thicknesses as a statistical series. These thicknesses are directly deduced from FMS profiles analysis. A distribution of thicknesses ranging from 0.1 to 3.0m, with an asymmetrical character and a mean thickness of about 0.5 m (• m; Fig. 7) is obtained. The mean thicknesses determined from FMS lithological analysis of individual basaltic flows are very similar in pillows (0.4m; Fig. 7b), thin flows (0.5m; Fig. 7c), and massive flows (0.6 m; Fig. 7d). Considering the thicknesses of Units 2D, 24, 27, 34 and 39, and the thickness of individual massive flows (0.6 m• m), each massive lithological unit seems to be constituted in Hole 504B with 20 to 45 individual flows. The few dyke units observed in the volcanic section of Hole 504B have an apparent thickness of 2 to 7m

from FMS data. The true thickness of a dyke depends on dip. From the FMS data, massive units 2D, 24, 27, 34 and 39 are often composed of thick individual flows (1.0 to 3.5 m) near the top, and of thinner flows at the base (0.1 to 1.0 m; Fig. 8). We infer that such a configuration is probably related to the volcanic process, as the emplacement of a massive unit starts with small volumes of lava, evolving later toward larger ones. Also, the two parts of massive units are separated in Units 27, 34 and 39 by a thin pillow layer which appears to be decreasing in thickness with decreasing depth. Within Units 2D and 24, both composed of two main flows, this layer of pillow is absent. The absence of pillows at the late stage of the emplacement of this crustal section can be related to the ridge evolution in space and time. While massive and some thin flows might reach a particular site hundreds of metres away from the eruptive axis (occasionally a few kin), pillows are emplaced within a much narrower zone, and hence are more sparsely sampled in upper crustal structures. In conclusion, such a cmscale description in vertical sequence of volcanic products leads indirectly to a detailed knowledge

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Fig. 8. The distribution of the flow thicknesses within the main massive units encountered in the Hole 504B (2D, 24, 27, 34 and 39) versus depth. Units 27, 34 and 39 contain pillow layers with thicknesses increasing with decreasing depth (mbsf: metres below sea floor). Site 504 was interpreted to be constructed in two main volcanic cycles associated with the emplacement of two volcanic sequences (Pezard et al. 1992).

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of accretion parameters, hence to a better understanding of crustal filtering of upper mantle liquids.

Accretion processes The concept of the magma chamber has been a critical element in geological models of crustal formation along mid-ocean ridges (e.g. Cann 1974; Kidd 1977; Nicolas et al. 1988). Most of these models consider that the magma chamber is a relatively large reservoir essentially occupied by melt. On the basis of more recent considerations concerning the size of crustal magma chambers (e.g. Detrick et al. 1987; Kent et al. 1990; Sinton et al. 1991) and recent geophysical data, Sinton & Detrick (1992) have proposed a model of mid-ocean ridge magma chamber at different spreading rates. Along a fast spreading ridge like the East Pacific Rise, this model consists of sill-like bodies of melt located 1.0 to 2.0km below the ridge axis, and grading downward into a partially solidified crystal mush surrounded by a transition zone to solidified, although still hot gabbros (Fig. 9). In this model, a lens constituted of melt is proposed to be 10 to 100 m in height. The shape and dimensions of the crustal magma chamber determined along the northern EPR are also probably typical of a wide range of intermediate and fast spreading ridges (Sinton & Detrick 1992). The accretion process episodicity at the ridge axis is believed to reflect the episodic activity of the magma chamber under the ridge. The lava volume emplaced during a volcanic cycle (Kappel & Ryan 1986; Gente 1987; Pezard et al. 1992) may be used to evaluate the magma flow through the magma chamber during this volcanic cycle. In the following, constraints from

Fig. 9. Cross-axis model of magma chamber along a fast spreading ridge proposed by Sinton & Detrick (1992).

Fig. 10. (a) Idealized view of the ridge axis and neovolcanic zone (Pezard et al. 1992). At a half-spreading rate of 35 mm yr-1, the NVZ is proposed to be 1 km wide. (b) Schematic representation of lava geometry erupted within the NVZ. It is a cross-axis section obtained at the end of emplacement of the two volcanic sequences (VS1 and VS2). (c) A cross-axis section showing the volume occupied by the massive flows at the beginning of the volcanic cycle. The massive flows fill the axial graben (ESD) and overflow on the ridge flank until the first block-bounding fault.

marine geophysical surveys and downhole measurements obtained in Hole 504B are used to estimate the basaltic volumes erupted during volcanic activity, and the constraints applied on the magma chamber under an intermediate rate spreading ridge, such as the Costa Rica Rift. At an intermediate rate, the massive flows are considered to be erupted on-axis in a graben called elongated summit depression (ESD; Kappel & Ryan 1986), yielding a lava plain at a large scale. If the eruption is large enough, the massive flows may fill the axial graben (ESD) and overflow on the ridge flank until the first great fault is encountered, often 2000 to 4000 m from the axis, acting like a dam to the lava. The axial graben (ESD) is generally 50 to 100 m deep, and 200 to 1500m wide. The volcanic and hydrothermal activity is, in most cases, limited to a 50

STRATIGRAPHY AT DSDP/ODP SITE 504 AND ACCRETION PROCESSES to 500 m wide interval located on axis (Macdonald 1982; Gente et al. 1986; Kappel & Ryan 1986; Gente 1987; Macdonald & Fox 1988). Site 504 was interpreted to be constructed in two main volcanic cycles associated to the emplacement of two volcanic sequences (Pezard et al. 1992). The 650 m thick volcanic pile observed in Hole 504B, was hence built over a time interval on the order of 15000 to 20000 years considering a NVZ half-width of 500 m, at a half-spreading rate of 35 mm yr -1. The first volcanic sequence is proposed to cover the interval from the transition zone to about 580 mbsf, near the base of Unit 27 (Fig. 8), and was constructed close to the axis (A to B; Fig. 10a). The second volcanic sequence built the section from 580 mbsf to 325, near the base of Unit 2D (Fig. 8), and was erupted further out from the axial graben, although still within the NVZ (B to C; Fig. 10a). The 50 m thick upper part of the basement is considered to be related to later episodes of accretion, when Site 504 was located out of the NVZ (beyond C; Fig. 10a). Flow volume evaluation. We estimate here the lavas volumes emplaced during a single volcanic cycle. In the following, we consider that all basaltic flows are pillows and massive flows, erupted at the begining of the volcanic cycle. The total volumes of lava estimated then correspond to that of massive flows, pillows and dykes emplaced during a given volcanic cycle. The volumes are computed per m of ridge length. The two other dimensions are that perpendicular to the axis (associated to the time scale), and thickness. Due to the presence of a blockbounding fault at about 800 mbsf in Hole 504B, the penetrated section has been somewhat truncated, and the thickness of eruptive products (about 650 m) is observed to be less than average thicknesses measured in ophiolites (Pezard et al. 1997). A more accurate evaluation of the thickness of the extrusive section may be estimated to be about 800 (+100)m. In addition, the volcanic products are emplaced within a kilometer-wide NVZ and the volcanic volumes which build a site such as ODP 504 c o r r e s p o n d to the h a l f - w i d t h N V Z (500 (+ 200) m; Fig. 10b). The massive flow lateral extent can be considered as corresponding to the distance (L) from the ridge axis to the first great fault. From the FMS- and DLL-derived lithostratigraphic study, the thickness of massive flows (T) is measured in Hole 504B to vary from 12 to 22 m. The axial graben size (wide and deep; Fig. 10c), directly derived from sea floor observations, are also considered for volumes estimates. The

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abbreviations used for the computing of lava volumes are shown in Table 1.

Table 1. Dimensions NVZ k T D W E Z

Definition

Values (m)

Neovolcanic zone Massive flow lateral extent Massive flows thickness Axial graben depth Axial graben width Extrusive thickness Sheeted dykes complex thickness

500+200 30004-1000 17-t-5 75+25 275-t-225 800+100 12504-750

The volume erupted within the NVZ is of the order of: VNvz = [NVZ.E] m3m 1. This volume, for a single volcanic sequences is hence Vvs(KR) = Vyvz/2, that is [ 0 . 2 0 + (0.03)x106]m3m 1 (Fig. 10b). The massive flow volume erupted at the beginning of each volcanic cycle and filling the axial graben, may be estimated to Vvs(ml)= [D.W]. If the massive flow overflows the axial graben, and with offaxis emplacement, a different volume is computed with: Vvs(m2)=[(L - W/2)xT]. Therefore, the t o t a l massive flow v o l u m e is V v s ( m ) = [ V v s ( m l ) + V v s ( m 2 ) . ] With a total thickness of 17 (+5) m, the massive flows constitute only about 5 (+2) % of a given volcanic sequence (400 (+50) m). The flows, other than massive ones are supposed here to correspond to pillows, which effectively constitute 95 (+2) % of the overall volcanic sequence. Then, the volume of pillow flows may be estimated to Vvs(P) = [(95)/100] x Vvs(KR). The width of basaltic flows erupted during one volcanic cycle [NVZ/2=250 (+100) m] corresponds to the same as the injected dyke width (Kidd 1977). In ophiolites, the dykes are observed to be 500 to 2000m in height (Z). The total dyke volume associated with a single volcanic sequence is then estimated to be Vvs(d)=[250xZ]. The total volume of lava flows corresponding to a given volcanic cycle becomes Vvs = Vvs(m)+ Vvs(P)+ Vvs(d). The volume estimates of massive flows, pillows, and dykes emplaced during a given volcanic cycle are summarized in Table 2. Magma chamber behaviour and constraints. The previous volumes are used here to understand the magma chamber behaviour under an intermediate rate spreading ridge such as the Costa

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308 Table 2.

Lava volumes VNvz

Definition

Volume estimates (106 m 3 m-1)

Flow volume erupted within the NVZ

0.404-0.05

Volume of extrusive products during a KR volcanic cycle (after Kappel & Ryan 1986)

0.204-0.03

Vvs(m0

Massive flow volume filling the axial graben

0.034-0.02

Vvs(m2)

Massive flow volume overflowing the axial graben and emplaced off-axis

0.094-0.08

Vvs(m)

Total massive flow volume erupted in the begining of a volcanic cycle

0.114-0.09

Vvs(P)

Pillow volume associated to a given volcanic cycle

0.194-0.03

Vvs(d)

Dyke volume associated to a given volcanic cycle

0.304-0.19

Vvs

Maximum lava volume corresponding to a given volcanic cycle

0.604-0.30

VvE

Lava volume emplaced during one volcanic event

0.024-0.01

Vvs(KR)

V•

Lava volume emplaced during one eruption

Rica Rift. The eruption process is directly related to the pressure and stress constraints applied on the magma chamber environment. An eruption of volcanic material may be associated with a single dyke injection (Nicolas 1988). Dykes were described in Hole 504B to be emplaced as multiples, each 4.0 m in width on average, hence composed of about 5 single dykes (Umino 1995). If a 4.0m wide dyke multiple is considered as produced as a serie of eruptions and associated to one volcanic event, then a 250 m width volcanic sequence is emplaced after about 60 volcanic events such as that proposed by Umino (1995). The lava volume emplaced on both sides of the ridge during one volcanic event is VVE= 2• [Vvs/60]. The lava volume emplaced during one eruption is then VE----[VvE/5] that may be estimated to [0.003 (•215 106] m3m -I (Table 2) This volume corresponds to the magma volume available in the form of an overpressure within the magma chamber lens before eruption. This volume (VE) may be expressed as equal to [TDAZ] per unit length of ridge, where TD is the dyke width and Az corresponds to a theoretical height of magma erupted during a given eruption. The volume (VE) estimated previously corresponds to erupted basalt. The basalt magma (at the temperature of magma chamber lens) is expressed as VE(magma)-- VE (basalt)/o~, where ~=[pm/pB]. Pm is taken as about 2.7• 103 kg m 3 (Hooft & Detrick 1992) and PB as about 2.95• 103 k g m 3. The estimated

0.0034-0.001

magma volume associated with a single eruption ( V E ( m a g m a ) ) is t h e n e q u a l to [0.0033 (+0.0011)]• m 3 1. Az may be estimated to 2.0 (1.0) kin, if TD is of 2.0 (+0.5) m. This volume VE(magma) may be expressed as a gradient in magma pressure within the magma chamber lens, appropriate for rupturing the cold lid constituted by the sheeted dykes complex, propagating a dyke and creating an eruption onto the sea floor: Ap = Ping• Az, where g is the gravity. For a given dyke eruption, this pressure Ap may be estimated to 52 (+26) MPa. This estimated pressure corresponds to that applied at the base of the sheeted dykes prior to eruption. Conclusions

Resistivity measurements (at m scale) and high resolution electrical images of the borehole wall (at cm scale) allow one to discriminate the largescale layers of the upper crust and to identify each of the lithologic units defined in the core. High-resolution electrical images (FMS) allow us, not only to discriminate individual lithological units, but also the sub-units which correspond to individual flows. C o n s e q u e n t l y , thicknesses of flows composing the effusive section at Site 504 can be determined and discussed in terms of accretion parameters. The average thickness of individual flows deduced from this analysis is 0.5m (+0.1 m). This study shows that massive units are often composed of

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70, and 83, and ODP Legs 11 I, 137, 140, and 148). In: ALT, J. C., KINOSHITA,H., STOKKING,L. B. & MICHAEL, P. J. Proceedings of the Ocean Drilling Program, Scientific Results, 148, 417-434. ANDERSON~ R. N., HONNOREZ, J., BECKER, K., et al. 1985. Initial Reports of Deep Sea Drilling Project. Washington (U.S. Govt. Printing Office), 83. BALLARD, R. D., FRANCHETEAU, J., JUTEAU, T., RANGIN, C. & NORMARK, W. 1981. The East Pacific Rise at 21~ the volcanic, tectonic and hydrothermal processes of the central axis. Earth and Planetary Science Letters, 55. 1-10. BECKER, K., SAKAI, H., et al. 1989. Drilling deep into young oceanic crust, Hole 504B, Costa Rica rift. Review of Geophysics, 27, 79-102. --, Foss, G., et al. 1992. Proceedings of the Ocean Drilling Program, Initial Reports, 137, College Station, TX (Ocean Drilling Program). BONATTI, E. & HARRISON, C. G. A. 1988. Eruption styles of basalt in oceanic spreading ridges and seamounts: effect of magma temperature and viscosity, Journal of Geophysical Research, 93, 2967-2980. We are grateful to Bernard Celerier and Bisger Hanson CANN,J. R. 1974. A model for oceanic crust structure for a very detailed and constructive review of the developed. Geophysical Journal of the Royal manuscript. This manuscript was improved by inforAstronomical Society, 39, 169-187. mal discussion with C. Coulon, A. Demant and J.-J. - - - - - - , LANGSETH, M. G., HONNOREZ, J., VON Cochem& This work was supported by the GroupeHERZEN, R. P., WHITE, S. M., et al. 1983. Initial ment de Recherche 'Physique et M&anique des Reports of the Deep Sea Drilling Project. WaRoches', and the 'G~osciences Marines' ODP support shington (US Govt. Printing Office), 69. program of CNRS in France. CHOUKROUNE, P., FRANCHETEAU, J. • HEKIN1AN, R. 1984. Tectonics of the East Pacific Rise near 12o50, N: a submersible study. Earth and PlaneReferences tar), Science Letters, 68, 115-127. ADAMSON, A. C. 1985. Basement lithostratigraphy, COLEMAN, R. G. 1977. Ophiolites. Springer Verlag. DSDP Hole 504B, In: ANDERSON,R. N., HONNOR- COSTA RICA RIFT UNITEDSCIENTIFICTEAM(CRRUST) EZ, J., BECKER, K., et al. 1985. Initial Reports of 1982. Geothermal regimes of the Costa Rica rift, Deep Sea Drilling Project, Washington (U.S. east Pacific, investigated by drilling, DSDP-IPOD Govt. Printing Office), 83, 121-127. legs 68, 69, and 70. Geological Society American ALT, J. C., LAVERNE,C. ~; MUEHLENBACHS,K. 1985. Bulletin, 93, 862-87. Alteration of the upper oceanic crust: Mineralogy CYAMEXSCIENTIFICTEAM: FRANCHETEAU,J., NEEDHAM, and processes in Deep Sea Drilling Project Hole H. D., CHOUKROUNE,P., JUTEAU,J., SEGURET,M., 504B. In: ANDERSON, R. N., HONNOREZ, J., BALLARD, R. D., Fox, P. J., NORMARK, W. R., BECKER, K., et al. 1985. Initial Reports of Deep CARRANZA, A., CORDOBA, D., GUERRERO, J. & Sea Drilling Project, Washington (U.S. Govt. RANGIN, C. 1981. First manned submersible dives Printing Office), 83, 217-248. on the East Pacific Rise at 21~ Marine , HONNOREZ, J., LAVERNE, C., & EMMERMANN, Geophysical Research, 4, 345-379. R. 1986a. Hydrothermal alteration of a 1-km DETRICK, R. S., BUHL, P., VERA, E., MUTTER, J., section through the upper oceanic crust, DSDP ORCUTT, J., MADSEN, J. & BROCHER, T. 1987. hole 504B: The mineralogy, chemistry and evoluMultichannel seismic imaging of a crustal magma tion of seawater-basalt interactions. Journal of chamber along the East Pacific Rise. Nature, 326, Geophysical Research, 91, 309-335. 35-41. , MUEHLENBACHS,K. 8~; HONNOREZ, J. 1986b. DICK, H. J. B., ERZINGER, J., STOKKING, L. B., et al. An oxygen isotopic profile through the upper 1992. Proceedings of the Ocean Drilling Program, kilometer of the oceanic crust, DSDP Hole 504B. Initial Reports. College Station, TX (Ocean Earth and Planetary Science Letters, 80, 217-229. Drilling Program), 140. , K1NOSHITA, H., STOKKING, L. B., et al. 1993. EDWARDS,M. H. 1991. The morphotectonic fabric of the Proceedings of the Ocean Drilling Program, Initial East Pacific Rise: implications for fault generation Reports, 148: College Station, TX (Ocean Drilling and crustal accretion. PhD thesis, Columbia Program). University. , LAVERNE, C., VANKO, D. A. et al. 1996. EKSTROM, M. P., DAHAN,C. A., CHEN, M.-Y., LLOYD, Hydrothermal alteration of a section of upper P. M. & ROSSl, D. J. 1986. Formation imaging oceanic crust in the Eastern Equatorial Pacific: a with microelectrical scanning arrays. Transactions synthesis of results from Site 504 (DSDP Legs 69, of the Society of Professional Well Log Analysts,

thick individual flows in the upper part, and of thinner flows at the base. Lithological massive units are observed to be constituted with two major flows separated by thin pillow layers, which seem to be increasing with increasing depth, probably associated to the distance of the site from the ridge axis while erupting. Site 504 is postulated to be created at an i n t e r m e d i a t e rate spreading ridge, with the emplacement of two volcanic sequences. Each sequence was built after emplacement of [0.60 (•215 106] m 3 m 1 of flows and dykes volume. During a single eruption, a [0.003 (+0.001)x 106] m 3 m -L of m a g m a volume is emplaced on the both sides o f the ridge. The m a g m a volume evaluation allows one to estimate the gradient in m a g m a pressure within the m a g m a chamber lens to 52 (+26) MPa, appropriate for propagating a dyke and during an eruption.

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27th Annual Logging Symposium, Paper 88. EMMERMANN, R. 1985. Basement geochemistry; hole 504B. In: ANDERSONR. N., HONNOREZ,J., BECKER

K., et al. Initial Reports of Deep Sea Drilling Project, Washington (U.S. Govt. Printing Office), 83, 183-200. GASS, I. G. & SMEWING, J. D. 1973. Intrusion, extrusion and metamorphism at constructive margin: evidence from the Troodos massif, Cyprus. Nature, 242, 26-29. GENTE, P. 1987. Etude morphostructurale comparative

des dorsales oc~aniques d taux d'expansion varies. PhD Thesis, University of Brittany. , AUZENDE, J. M., RENARD, V., FOUQUET,Y. & BIDEAU, D. 1986. Detailed geological mapping by Submersible of the East Pacific Rise axial graben near 13~ Earth and Planetary Science Letters, 78, 224-236. HEY, R., JOHNSON, L. & LOWRIE,A. 1977. Recent plate motion in the Galapagos Area. Geological Society of America Bulletin, 88, 1385-1403. HONNOREZ, J., LAVERNE, C., HUBBERTEN, H. W., EMMERMAN, R. & MUEHLENBACHS, K. 1983. Alteration processes in layer 2 basalts from Deep Sea Drilling Project Hole 504B, Costa Rica Rift. In: CANN,J. R., LANGSETH,M. G., HONNOREZ, J., VON HERZEN, R. P., WHITE, S. M., et al. 1983.

Initial Reports of Deep Sea Drilling Project. Washington (US Govt. Printing Office), 69. KAPPEL, E. S. & RYAN, W. B. F. 1986. Volcanic episodicity and a non-steady state rift valley along Northeast Pacific Spreading Centers: evidence from Sea M A R C I. Journal of Geophysical Research, 91, 13925-13940. KENT, G. M., HARDING, A. J. & ORCUTT, J. A. 1990. Evidence for a smaller magma chamber beneath the East Pacific Rise at 9o30 , N, Nature, 344, 650653. KIDD, R. G. W. 1977. A model for the process of formation of the upper oceanic crust, Geophysical Journal of the Royal Astronomical Society, 50, 149-183. KLITGORD, K. D. & MUDUIE, J. D. 1974. The Galapagos spreading center: a near-bottom geophysical survey. Geophysical Journal of the Royal Astronomical Society, 38, 563-586. LAVERNE, C. 1987. Les altdrations des basaltes en

domaine ocdanique, minOralogie, pdtrologie et gdochimie d'un systdme hydrothermal: le Puits 504B, Pacique oriental. PhD Thesis, University of Aix Marseille III. HONNOREZ, J. & ALT, J. C. 1989. Transition entre l'alt~ration fi basse temperature et le mOtamorphisme hydrothermal de la crofite oc6anique: &ude pOtrographique et gOochimique du puits 504B, Est-Pacifique. Bulletin de la Socidtd Gdologique de France, 8, 327-337. LEWIS, B. R. T. 1979. Periodicities in volcanism and longitudinal magma flow on the East Pacific Rise at 23~ Geophysical Research Letters, 6, 753756. LONSDALE, P. 1977. Deep-tow observations at the

- - ,

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mounds abyssal hydrothermal field, Galapagos rift. Earth and Planetary Science Letters, 36, 92110. LUTHI, S. M. & BANAVAR,J. R. 1988. Application of borehole images to three-dimensional geometric modelling of aeolian sandstone reservoirs, Permian Rotliegende, North Sea. American Association of Petroleum Geologist Bulletin, 72, 10741089. MACDONALD, K. C. 1982. Mid-ocean ridges: fine-scale tectonic, volcanic and hydrothermal processes within the plate boundary zone. Annual Review of Earth Planetary Science, 10, 155-190. & Fox, P. J. 1988. The axial summit graben and cross-sectional shape of the East Pacific Rise as indicator of axial magma chambers and recent volcanic eruptions. Earth and Planetary Science Letters, 88, 119-131. NORMARK, W. R. 1976. Delineation of the main extrusion zone of the East Pacific Rise at latitude 21~ Geology, 4, 681-685. NICOLAS, A., REUBER, I. & BENN, K. 1988. A new magma chamber model based on structural studies in the Oman ophiolite. Tectonophysics, 151, 87-105. PEZARD, P. A. 1990. On electrical properties of rocks, -

with implications for the structure of the upper oceanic crust. Ph.D. Thesis, University of Columbia. , LOVELL, M. A. & ODP LEG 126 SHIPBOARD SCIENTIFIC PARTY 1990. Downhole images : electrical scanning reveals the nature of subsurface oceanic crust. Eos, 71, 709. , ANDERSON, R. N., RYAN, W. B. F., BECKER, K., ALT J. C. & GENTE, P. 1992. Accretion, structure and hydrology of intermediate spreading-rate oceanic crust from drillhole experiments and seafloor observations. Marine Geophysical Research, 14, 93-123. , AYADI, M., REVlL, A., BRONNER, G. & WILKENS, R. 1997. Detailed structure of an oceanic normal fault; a multi-scalar approach at DSDP/ODP Site 504. Geophysical Reaserch Letters, 24, 337-340. ROBINSON, P. T., LEWIS, B. R. T., FLOWER, M. F. J., SALISBURY, M. H. & SCHMINKE, H. U. 1973. Crustal accretion in the Gulf of California: a medium-rate spreading axis, In: Initial Reports of Deep Sea Drilling Project, 65, 739-752. SINTON, J. M. & DETRICK, R. S. 1992. Mid-ocean ridge magma chambers. Journal of Geophysical Research, 97, 197-216. , SMAGLIK, S. M., MAHONEY, J. J. 8s MACDONALD, K. C. 1991. Magmatic processes at superfast spreading oceanic ridges: Glass compositional variations along the East Pacific Rise. Journal of Geophysical Research, 96, 6133-6155. UMINO, S. 1995. Downhole variations in grain size at Hole 504B; implications for rifting episodes at mid-ocean ridges. In: Proceedings of the Ocean Drilling Program, Scientific Results, 137/140, 1933.

Multi-scalar structure at D S D P / O D P Site 504, Costa Rica Rift, III: faulting and fluid circulation. Constraints from integration of FMS images, geophysical logs and core data M . A Y A D I l, P. A. P E Z A R D l, G. B R O N N E R 2, P. T A R T A R O T T I

3, & C. L A V E R N E 1

1Pdtrologie Magmatique, C N R S ( U M R 6635), CEREGE, BP80, 13545 Aix-en-Provence, France 2 Laboratoire de G(ophysique-Gdodynamique, UniversitO d'Aix-Marseille III, Facult~ des Sciences de Saint-J~rdme, 13397 Marseille cedex 20, France 3 Dipartimento di Geologia, Paleontologia e Geofisica, Universitgl di Padova, via Giotto n.1, 1-35137 Padova, Italia Abstract: Downhole geophysical logs and high-resolution electrical images (FMS) from

DSDP/ODP Hole 504B are analysed in combination with core data to obtain an integrated description of oceanic faults met in the hole. About 34 500 fractures were mapped from FMS images over 1672 m of basement. The fracture distribution from FMS confirms the presence of a main fault zone between 800 and 1100 mbsf (metres below sea floor), elsewhere detected from seismic data as well as magnetic, acoustic, and electrical resistivity measurements. The fracture density profile reveals the presence of two other highly fractured zones, (1) between 400 and 575 mbsf and (2) close to the bottom of Hole 504B (1700 to 2100 mbsf). Consequently, we infer that Site 504 was submitted first to an extensional stress regime near the ridge axis, with circulation of high-temperature fluids and pervasive alteration of the basalts. This initial phase is associated with the main fault met in Hole 504B. Similar but less developed deformation was generated off-axis, with lowertemperature parageneses, such as that cored between 400 and 575 mbsf. The present compressional to strike-slip stress regime is expressed in subhorizontal fracturing detected in discrete zones, such as within the main fault zone and the lower fracture zone (1700 to 2100 mbsf) in Hole 504B.

F a u l t i n g is a f u n d a m e n t a l process in the construction and evolution of the oceanic crust. Mid-oceanic ridges at all spreading rates are believed to be characterized by extension and the presence of ridgeward-dipping normal faults. Normal faulting is also observed in ophiolites, where discrete zones of fracturing spaced at intervals of 1.0 to 1.5kin and parallel to the sheeted dykes, and may become listric at depth (Casey et al. 1981; Rosencrantz 1983). These fault zones are generally highly altered and mineralized, indicating a preferential conduit for fluid circulation (Nehlig & Juteau 1988). In the Troodos ophiolite, highly-altered subhorizontal surfaces are observed to act as decoupling horizons, linked by planar normal faults (Agar & Klitgord 1995). The structure of the oceanic crust has often been described at km scale with marine geophysical data. D S D P / O D P Hole 504B ( C R R U S T 1982; Cann et al. 1983; Anderson et al. 1985; Becker et al. 1988; Becker et al. 1992; Dick et al.

1992; Alt et al. 1993) was drilled about 200kin to the south of the Costa Rica Rift. This drillhole provides a unique opportunity to describe, at m to cm scale, the evolution of the oceanic crust generated at the rift axis. D S D P and O D P efforts at Site 504 have produced rock samples and geophysical data supporting the ophiolite model (Anderson et al. 1982; Becker et al. 1989) and contributing to improving the understanding of the seismic structure of crustal layers (Detrick et al. 1994). While faulting in the upper oceanic crust is generally described to be normal, the analysis of borehole wall images recorded in Hole 504B provides evidence of a compressional stress regime in the upper basement (Moos & Zoback 1990). In addition, in situ, borehole instabilities advocate for a compressional stress regime above 1500 mbsf to a strike-slip stress regime below 1700 mbsf (Pezard et al. 1995). Faulting and fluid circulation in the crustal section penetrated by Hole 504B are here analysed in

AYADI,M., PEZARD,P. A., BRONNER,G., TARTAROTTI,P. & LAVERNE,C. 1998. Multi-scalar structure at DSDP/ODP Site 504, Costa Rica Rift, III: faulting and fluid circulation. Constraints from integration of FMS images, geophysical logs and core data In." HARVEY,P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 311-326

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Fig. 1. The upper crustal structure in the vicinity of ODP Site 504, as interpreted from the N-S single channel seismic section (Langseth et al. 1988) after migration with no vertical exageration (Pezard et al. 1997). The interpretations are represented with dashed lines. The hole seems to penetrate two tilted blocks seperated by a north-dipping normal fault met by the drilling. relation to the nature of the stress field. While core analyses are essential to study tectonic and hydrothermal processes, true fracturing estimates from core are grossly underestimated in this case. The approach used in this paper is based on the integration of the highresolution electrical images of the borehole wall (Formation MicroScanner, or FMS), downhole geophysical logs and core data. First, we present a global analysis of the integrated core-logsFMS images results. The first part presents the data. The results of the FMS analysis are then described in details, and interpreted in term of fracturing. Second, several intervals corresponding to identified fracture zones are analysed in details in term of faulting and fluid circulation, then related to the regional stress regime, present and past.

Structural setting In the vicinity of Site 504, the upper crustal structure was first imaged by a north-south single channel seismic (SCS) reflection profile (Langseth et al. 1988). More recently, a dense grid of single- and multi-channel seismic reflection was performed (Kent et al. 1996). From the earlier SCS section, the basement structure on the southern flank of the Costa Rica Rift was interpreted as constituted by km scale fault blocks, apparently tilted gently to the south. The depth conversion of the seismic section was performed in a point-wise manner by Pezard et al. (1997) using velocities measured on samples collected in the sediment and basement at Site 504 (Fig. 1). Hole 504B penetrates 274.5m of sediment, about 600m of extrusives (pillow lavas, massive flows, thin flows, and breccias), a transition zone from the extrusives to about 1000m, then the underlying sheeted dyke corn-

plex (Fig. 2). The extrusive thickness (about 600m) is considerably less than the average observed in ophiolite, where lava thicknesses are estimated to be 1.0 to 1.5kin (Kidd 1977). A block-bounding fault met at about 550m into basement probably provides part of the explanation for this reduced thickness (Pezard et al. 1997). This north-dipping fault appears as subvertical at the sediment-basement interface, with a mean dip of 45 ~ toward the ridge axis at 800 mbsf (Pezard et al. 1997).

Downhole

geophysical

measurements

(m scale)

Downhole measurements of rock physical properties recorded in Hole 504B during ODP Leg 148 (Alt et al. 1993) provide a continuous mscale description of the crustal structures. The electrical resistivity increases by nearly two orders of magnitude from highly porous and altered extrusives (Becker 1985; Becker et al. 1989) to the resistive sheeted dikes (Fig. 2a,b). In the dykes, the electrical resistivity increases continuously down to 1400 mbsf, and a more irregular pattern is observed below. Intervals with resistivity readings under 100 O H M m below 1400 mbsf are due to either an increase in fracture density or a change in clay mineralogy. Compressional-wave velocity values are greater than 5.0 k m s ~ in the dykes and occasionnally larger than 6.5 k m s ~ below 1500 mbsf (Fig. 2c). Detrick et al. (1994) concluded from such data that Hole 504B penetrates well into Layer 3, and that the Layer 2/3 boundary is not necessarily a lithological one. Natural radioactivity (GR) values obtained in the hole are overall higher, and display a more

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Fig. 2. (a) Schematic of Hole 504B drilling history and lithostratigraphy after Leg 148. (b) Electrical resistivity profiles (LLd and LLs) recorded in the hole during ODP Leg 148 with the Dual Laterolog (DLL) tool. (c) To the left, compressional- and shear-wave velocities (Vp and Vs) obtained in Hole 504B with Vp values greater than 5.0 km s-Lin the dykes and eventually larger than 6.5 km s 1 below 1500 mbsf; to tlae right, natural radioactivity (GR) profile with values obtained in the hole higher and with a more irregular pattern in the altered extrusives than in the dykes. (d) Magnetic field (to the left) and inclination (to the right) computed from tri-axial magnetometer data recorded with the orientation device of the FMS during ODP Leg 148.

irregular pattern in the altered extrusives than in the dykes (Fig. 2c). Higher G R values correspond to local potassium concentrations in secondary minerals due to fluid circulation in the upper crust (Tartarotti et al. 1988). High GR values may consequently be used in the extrusives as an indicator of palaeofluid circulation. The present trace of the fault detected from seismic data (Fig. 1) corresponds with the lowest values of electrical resistivity recorded in the basement at about 800 mbsf (Fig. 2; Pezard & Anderson 1989). This fault was initially detected from tri-axial magnetic field data (Kinoshita et al. 1989) with a 4 ~ step in magnetic inclination measured in the hole near 800 mbsf (Fig. 2d). The magnetic properties of the basalt were also found to be strongly modified over an interval spanning from 900 to 1050 mbsf (Pariso & Johnson 1991), suggesting hot fluid circulation in this zone close to the axis.

Borehole wall electrical images ( c m scale) Data acquisition. Formation MicroScanner T M (FMS) images of the borehole surface were recorded over 1672 m of the basement crossed by Hole 504B during ODP Leg 148 (Alt et al. 1993). The FMS creates an image of the borehole wall by mapping its electrical microconductivity using an array of small, pad-mounted electrodes (Ekstrom et al. 1986; Luthi & Banavar 1988). The slimhole configuration developed for ODP (Pezard et al. 1990) uses four pads, each with 16 buttons. Because of electrode geometry, the tool has a moderately shallow depth of investigation, in the order of a few centimetres. FMS data are recorded every 2.5 mm, and the vertical resolution of individual features is about one centimetre. The tool can, however, detect thinner features if a sufficient resistivity contrast to the surroundings matrix is present. FMS images

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Fig. 3. FMS plane density derived from raw data, expressed by the number of planes per metre. (a) Density for total planes mapped from FMS imges (34 500 planes over 1672m of basement); (b) Density for subhorizontal planes (dip < 30~ (c) Density for intermediate planes (30~ dip < 60~ (d) Density for subvertical planes (dip _>60~ Large dots indicate recording file boundaries, explaining the absence of data in places (1800 to 1884 mbsf, for example) and locating where the FMS sensor became stuck during logging due to hole restriction, obliging the operator to close the tool and interrupt the recording. show conductivity changes related to bed boundaries and fractures, either open or mineralized. Each electrode is oriented in space with three-axis accelerometers and flux-gate magnetometers, making it possible to derive the strike and dip of geological features. FMS data processing and analysis in Hole 504B is described by Ayadi et al. (1996). Images were analysed with Fracview TM,a Schlumberger interpretative software package that allows the interactive display and analysis of oriented images (Luthi & Souhait~ 1990). About 34 500 planes were identified and mapped over 1672 m of basement, yielding an average of 20 planes mapped per m. This dataset is analysed here in terms of raw fracture density versus depth (Fig. 3). In order to organize this large dataset, the planes were binned in terms of dip angle as subhorizontal (dip _<30~ intermediate (30 ~ _
lower than that for intermediate and subhorizontal planes (10 planes per m; Fig. 3). In fact, in near vertical holes, subhorizontal planes are better detected from borehole wall images than subvertical ones. The probability of encountering vertical features is lower than that of encountering horizontal ones ( N e w m a r k e t al. 1985; Dick et al. 1992). A correction of fracture density with dip value may thus be applied. Plane account is equal to zero, if dip is equal to 90 ~, and to one, if dip is equal to 0 ~ A correction coefficient equal to [1/cos(0)], where 0 is the mean dip value for a given bin, is usually applied to compensate for this sampling bias. On the other hand, the number of planes mapped from FMS images is affected by the small size of FMS pads in O D P (Pezard et al. 1990), implying a low relative surface coverage of the borehole wall. During O D P Leg 148, only three pads of the FMS were operating due to a sensor malfunction, and the surface coverage is only in the order of 12%. In comparison with FMS results derived from Hole 504B, the

FAULTING AND FLUID CIRCULATION AT DSDP/ODP SITE 504 Corrected FMS Total Planes

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i (a)

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Fig. 4. FMS plane density profiles derived from corrected data for both verticality and coverage effects. The density profile is expressed by an average number of planes per metre. (a) Density for total planes; (b) Density for subhorizontal planes (dip < 30~ (c) Density for intermediate planes (30~ dip < 60~ (d) Density for subvertical planes (dip 60~ Large dots indicate recording file boundaries.

fracture distributions derived from O D P Hole 896A (Alt et al. 1993) and ODP Hole 917A (Larsen et al. 1994), where the surface coverage is in the order of 22%, the bias due to the coverage effect is less pronounced (unpublished data). The two latter boreholes have a smaller diameter (25.4 cm) than Hole 504B (30 cm), and the four pads of the FMS were operating during logging of Holes 896A and 917A. For these reasons, surface coverage obtained in these two holes is greater than in Hole 504B. As a consequence, the FMS density should be corrected for a bias due to poor coverage. The correction method is briefly explained below. For the coverage correction in Hole 504B, the FMS planes were divided with dip into six classes (0-15 ~ 15-30 ~ 30-45 ~ 45-60 ~ 60-75 ~ and 75-90~ An histogram of plane orientations for each class is then plotted in three chosen intervals (500-550, 580-630 and 2000-2050 mbsf) of constant tool orientation. The azimuthal histograms show two clear modes (main and minor) separated by 180 ~ except for subhorizontal planes (0-30 ~ where the max-

imum are difficult to identify. It is assumed in the correction for this azimuthal bias that the main mode is representative of the true degree of fracturing at a given depth. This bias unfortunately forbids any azimuthal analysis beyond 30 ~ of dip. A correction coefficient for each of the six dip bins is computed for the three chosen depth intervals. A correction coefficient average is then computed by average for each dip class (1.3, 1.7, 2.1, 2.5, 3.1 and 4.4 are respectively the coefficients (+0.5) for classes 0-15 ~ 15-30 ~ 30~ 45 ~ 45-60 ~ 60-75 ~ and 75-90~ In the following, the fracture density discussed is exclusively that corrected for both verticality and azimuthal coverage effects (Fig. 4). This corrected density appears (Fig. 4) as five times greater (100m -1) than that of raw data (20m-l; Fig. 3). C o r e description

The core dataset used here is mapped and described by Tartarotti et al. (1998). This structural study was focused on the mapping of fractures, veins, breccia and rubble intervals

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Fig. 5. (a) Recovery in percent obtained in the volcanics (275 to 1000 mbsf; on the left) and core plane density (open fractures and veins) derived from corrected data for both verticality and recovery effects on the right (from Tartarotti et al. 1998). (b) The minimum estimates of fracture porosity (to the left), derived from the difference between the two DLL electrical resistivity measurements (LLs and LLd; Pezard & Anderson 1989), compared to breccia and rubble intervals found in the cored section. Horizontal fracture porosity is represented by grey line, vertical fracture porosity by black line, breccias by dark lozenges and rubbles by dark triangles. (c) FMS total plane density derived from corrected data for both verticality and coverage effects, obtained in the volcanic section (from 275 to 1000 mbsf). The number of planes is represented by crosses and the average density profile by a dark line.

in the volcanic section. A total of 1112 macroscopic fractures and veins were measured on core from the upper 1000m of the hole. The term fracture was restricted to open planar features without any mineral fill, and the term veins to filled fractures. Cooling and drilling features were excluded during mapping in order to include only tectonic data. Vein selection was adopted in order to avoid vein networks related to either incipient brecciation, or to contractional cooling of pillow lavas. Fracture and vein distribution plotted in Fig. 5a is the number of fractures and veins per metre over the considered depth interval. These data are also corrected for verticality, as described above. In addition, core data must be corrected for recovery (Tartarotti et al. 1998). Results

We analyse first of all, downhole variations in FMS planes density. Subhorizontal planes are

more abundant within the extrusive products (20m -1 on average) than in the sheeted dykes (10m -1 on average; Fig. 4b). This is probably related to the radial patterns associated with contractional cooling of the pillows. The intermediate and subvertical set dominate the section, with respective values of 30 m -1 and 50 m -1 planes on average. Between 800 and 1100 mbsf, the fracture density is found to increase with increasing depth (Fig. 4). An increase with increasing depth also obtained between 400 and 575 mbsf for planes with intermediate dips, seems to be less pronounced than that obtained between 800 and 1100 mbsf. Between 1100 and 1600 mbsf, the fracture density decreases slightly with increasing depth, probably in relation to dyke cooling during and shortly after emplacement. Another highly fractured zone is found towards the present bottom of the hole, in particular for subvertical fracturing. The presence of this zone may be related to drilling difficulties met during O D P Leg 148. These

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Fig. 6. (a) Schematic of Hole 504B drilling history and lithostratigraphy after Leg 148. (b) To the left, minimum fracture porosity of near-vertical conductive structures in the Hole 504B, derived from the difference between the two DLL electrical resistivity measurements (LLs and LLd). The data were obtained at the opportunity of ODP Legs 111 (1986; dark line) and 148 (1993; grey line). To the right, the estimates of apparent total porosity on the basis of Archie's formula (dark line) and the free-fluid porosity (grey line) deduced from accounting for surface conductivity due to clay minerals (Pezard 1990; Revil et al. 1996). (c) To the left, resistivity-derived open porosity fraction (ratio of free fluid pore space to total pore space) and to the right, Young's modulus computed from acoustic velocity measurements at dm-scale (and 20 kHz). The dotted line reflects an expected increase with increasing depth. (d) FMS total plane density derived from corrected data for both verticality and coverage effects. The number of planes is represented by crosses and the average density profile by a dark line.

results are compared in the following to the analysis of core data and geophysical logs. The FMS density (100 m l, on average) is four times greater than that from core (25m -~, on average) after correction. The planes mapped from FMS correspond not only to natural fractures but also to fractures induced by drilling. On the other hand, the FMS provides a nearly continuous image of basement structures, as opposed to the incomplete core which is affected by poor recovery (averaging 29.8% in the extrusive section in Hole 504B). When the recovery is under 30%, mapping fractures from core does not provide a representative dataset of basement fracturing, even after correcting for several potential sampling biases. Higher core recovery appears to be biased in this hole to less fractured intervals (Fig. 5). An increase in the number of planes mapped from FMS generally corresponds to an increase in fracture density in

the core (e.g. about 430, 575, 850 mbsf; Fig. 5), except at 500 mbsf where the core recovery is zero (Fig. 5). The highly fractured zone between 400 and 575 mbsf derived from FMS analysis (Fig. 4) is confirmed by the core fracture and vein analysis. Some of the planes, such as that located near 430 or 575 mbsf, and deduced from both core and FMS data, may be of tectonic origin, and thus may relate to the presence of fault planes. Below 800 mbsf, the fracture density increase confirms the existence of the main fault, elsewhere detected from seismic data, as well as magnetic and electrical resistivity measurements. Three methods to obtain porosity estimates from electrical resistivity measurements are detailed in Pezard et al. (1996) and provide a means to evaluate macroscopically the fracturing intensity and distribution in basement, as well as to compute the open porosity fraction

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Fig. 7. The upper fracture zone (from 400 to 575 mbsf). (a) To the left, the percentage of core recovery obtained in the Hole 504B and to the right, the deep electrical resistivity profile (LLd). (b) Minimum DLL-derived horizontal (grey line) and vertical (dark line) fracture porosity, on the right, and total plane density derived from corrected FMS data, on the left. (c) Natural radioactivity profile, on the left; distribution of K-rich minerals and zeolites derived from petrographic observation in thin sections, in the centre and right. (d) Intermediate plane density derived from corrected FMS data, on the left; distribution of red alteration halos derived from visual observation on cores and petrographic observations, on the right.

(ratio of open to total porosity). The porosity profiles derived from electrical resistivity measurements are in agreement with the fracture density distribution from FMS images (Fig. 6). Total and open porosity values obtained in the extrusives, averaging 10% and 3%, respectively, are higher than that obtained in the dykes, with average values of 2% and 1% (Fig. 6b). The fracture porosity (FP) deduced from the difference between the two D L L electrical resistivity measurements (LLs and LLd) is qualitative and a minimum estimate, and thus can be misleading. In Hole 504B, the FP profiles show intervals of high values (in particular for vertical features) in the extrusives, with a spacing of about 80 m . These intervals are interpreted to represent smaller blocks bounded by secondary faults and brecciated zones in the upper part of Hole 504B, with an actual spacing of about 10m. The main difference between the two fracture porosity (VFP) profiles recorded in 1986 and

1993 occurs near 800 mbsf (Fig. 6b), where a steep discontinuity is also inferred from electrical resistivity and magnetic data (Fig. 2). At this depth, a 20m thick interval was found in 1993 to be more extensively fractured than in 1986, possibly due to re-activation of the fault after 1986. On average, 25% of the pore space appears to be open to fluid circulation in the basement of Hole 504B (Fig. 6b). Departures from this average value are found in the 30-mthick aquifer located in the upper basement with values up to 70%, and between 800 and 1100 mbsf with values up to 40% (Fig. 6c). The mechanical characteristics of the basement, such as revealed by the Young's modulus profile (Fig. 6c), can be derived from full waveform acoustic measurements (Fig. 2). Departures from the expected trend are found in the 30m thick aquifer section in the upper basement, near the base of the hole, and between 800 to 1100 mbsf. In this zone, the reduced

FAULTING AND FLUID CIRCULATION AT DSDP/ODP SITE 504 Young's modulus is interpreted to correspond to a weakened crust, probably associated with repeated fracturing and mineralization from hydrothermal alteration. This decrease in Young's modulus also corresponds to fractured intervals derived from FMS images. From this geophysical dataset and core observations, three fault zones are distinguished in the crustal section penetrated by Hole 504B. These zones are here refered to as the upper fracture zone (400 to 575 mbsf), the main fault zone (800 to 1100 mbsf) and the lower fracture zone (1700 to 2111 mbsf) which are analysed and discussed in the following.

Fault analysis In this part, individual fracture zones are analysed in details on the basis of FMS images and in terms of fracture density and mean aperture. These results are compared to those obtained by Tartarotti et al. (1998) and other studies (e.g. Agar 1990, 1991; Alt et al. 1986, 1996; Pariso & Johnson 1991) which focused on the upper core section of Hole 504B (extrusives and transition zone, see Fig. 2). FMS results are also compared to results derived from downhole geophysical measurements (e.g. Kinoshita et al. 1989, Pezard et al. 1997). Upper f r a c t u r e z o n e ( 4 0 0 to 575 m b s f )

Within this interval, the FMS fracture density with intermediate dips increases linearly with increasing depth to reach average values of 55m 1 at about 520 mbsf, then decreases linearly down to 575 mbsf (Fig. 7). This depth corresponds to the top of lithologic Unit 27, a massive flow characterized by high electrical resistivity values. A similar density increase with increasing depth limited to fractures with intermediate dips is obtained in ODP Hole 896A (Alt et al. 1993) at the same depth into basement, about one km to the southeast of Hole 504B (unpublished data). Within this interval (400 to 575 mbsf), a maximum in red halo distribution (the percentage of alteration red halos for each core interval, after Alt et al. 1996), corresponds to high FMS fracture density and low electrical resistivity values (except at 500 mbsf where the core recovery is zero). In the core, such oxidized halos are observed to be parallel to fractures and exposed surfaces. This high fracturing intensity in the upper basement may be of tectonic origin, as a similar signal is found in the core (fracture and vein density, and red halo profile). Fracturing.

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Fluid circulation. The upper fracture zone is

located within the upper pillow alteration zone (UPAZ; Honnorez et al. 1983; Emmermann 1985; Alt et al. 1985, 1986; Laverne 1987). This fracture zone is characterized by the presence of zeolite veins, concentrated between 528 and 572 mbsf (Fig. 7d). This interval (from 528 to 572 mbsf) is characterized by high VFP values, high FMS plane density and the occurrence of breccias (Fig. 5b). These observations suggest that such an interval is highly fractured and porous, and may represent a preferential conduit for fluid circulation. Zeolites are interpreted to be derived from low temperature evolved fluids, due to late off-axis hydrothermal circulation (Alt et al. 1996). The natural radioactivity (GR) profile can be confronted to the FP profile. Low and high GR values are associated with low and high FP values, respectively (Fig. 7b,c). However, GR minima are frequently located at the boundary between domains of contrasting FP, i.e. dominant contrasting fracture orientation. Along such boundaries, metasomatic reactions, e.g. leaching of alkalis, may have occurred due to contrasting permeability values thus explaining the GR decrease (Fig. 7c). The GR profile can be also confronted by the presence of K-bearing minerals in core (including celadonite, phillipsite, and K-feldspar) detected in thin sections. Phillipsite mainly fills microfractures and replaces glass. Celadonite and celadonite-smectite mixtures are much more abundant than phillipsite, and occur in red and black alteration halos. Such oxidized halos are also parallel to fractures and exposed surfaces. The grey coloured internal part of the samples do not contain any celadonite but only saponite, which does not contain potassium. Recent study of the 504B core (Alt et al. 1996; Fig. 7d) reveals that red halos comprise at least 27% of the upper volcanic section . The highest percentage of red halos are not perfectly correlated with the GR signals. However, in some cases (e.g. from 510 to 585 mbs0 the correlation between GR profile and red halo distribution is good. Thus, it is possible that most of the GR peaks correspond to zones where alteration halos occur.

In conclusion, evidences from core-logs-FMS data indicate that the upper fracture zone located between 400 and 575 mbsf is of tectonic origin. This zone is constituted essentially, with intermediate dipping fractures (30 ~ to 60~ and characterized by the presence of several porous intervals associated with intense fluid circulation at low-temperature where celadonite, celadonite-smectite mixtures and saponite also occur.

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Fig. 8. Composite profiles of geophysical logs and mineralogical logs from the main fault zone (from 800 to 1100 mbsf). (a) To the left, core recovery percentage and to the right, interval studied by Agar (1991) with some fault planes presented (F). (b) Magnetic inclination computed from tri-axial magnetometre data recorded with the orientation device of the FMS, is opposed to the Cu ppm values obtained from core. (c) Total plane density derived from corrected FMS data, on the left; ppm values of Zn derived from core chemical analyses, on the right. (d) Subhorizontal plane density derived from corrected FMS data on the left and open porosity fraction (free fluid porosity over total porosity) on the right. (e) Apparent aperture in mm of planes directly mapped from FMS images, on the left; distribution of breccias (triangles) and rubbies (circles) identified on core (Tartarotti et al. 1998 volume), on the right. (f) To the left, minimum porosity of near-vertical conductive structures. The data were obtained seven years apart, at the opportunity of ODP legs 111 (1986, dark line) and 148 (1993, grey line). To the right, distribution of zeolites derived from petrographic observation in thin sections. To the left-hand side, the localization of the stockwork-like sulphide mineralization (Honnorez et al. 1983).

Zeolite (e.g. philipsite) veins are also found to be concentrated in the lower part, and interpreted to be associated with low temperature circulation. M a i n f a u l t z o n e ( 8 0 0 to 1100 m b s f ) Fracturing. Seismic data, d o w n h o l e geophysical logs, borehole wall images and core description obtained at Site 504 have led to infer the presence of a normal fault at about 800 mbsf (Kinoshita et al. 1989; Pezard & A n d e r s o n 1989; Pezard et al. 1997). This fault displays dip shallowing toward the ridge axis (Fig. 1), either gradually (lystric model) or abruptly. F M S data suggest a m e a n dip of about 45 ~ for a fault meeting the hole within the interval from 800 to 1100 m b s f (Fig. 4c). The fault zone spans from

800 to 1100 m b s f and is characterized by a high F M S fracture density, with a linear increase with increasing depth (Fig. 8b). This interval is highly brecciated, in particular in the upper basement section (Fig. 8e). A l t h o u g h the presently active part of the fault appears to be located within a 20 m thick interval below 800 mbsf, as deduced from the difference between two FP profiles recorded in 1986 and 1993 (Fig. 8f), it is difficult to identify a given fault trace. This rupture might in fact be a very recent one, and not representative of repeated deformation. As a consequence, this interval is discussed in the following in terms of fault zones rather than individual traces. Several discrete deformation traces or fault planes are identified between 800 and 1100 mbsf. The open porosity fraction (OPF) profile provides an average in the main fault zone of 40%,

FAULTING AND FLUID CIRCULATION AT DSDP/ODP SITE 504 and discrete m scale intervals with values exceeding 60% (Fig. 8d). These intervals often correspond to maxima in FMS subhorizontal fracture density (e.g. about 890, 1020, 1040, 1075 mbsf), and very variable values of apparent aperture of FMS planes (Fig. 8e). At 890 mbsf, a fault plane identified in core and described as dipping about 30 ~ lies at the base of a sequence of minor faults with decreasing dip with depth (Agar 1991). At this depth, an anomaly in temperature gradient profile recorded during Leg 148, is also observed (Guerin et al. 1996). The m scale zones (with OPF > 70%), such as that located at about 890 mbsf might be interpreted as present sites of deformation within the main fault zone (Pezard et al. 1997). Although the observation of vein filling at 890 mbsf suggests a normal component of movement accommodating extension in the hanging wall of the fault identified at seismic scale (Agar 1991), present deformation evidences are found directly above, probably in relation to the present compressional stress regime found in the upper basement (Moos & Zoback 1990). From about 850 to 1000 mbsf, the FMS subhorizontal fracture density and apparent aperture decrease with increasing depth, in a zone corresponding to intense brecciation (Fig. 8d,e). Within this interval, steeply dipping fault planes (about 70~ such as that at about 905 and 925 mbsf are identified in core, providing evidences of polyphase deformation (Agar 1991). The steeper planes (at about 905 and 925 mbsf; Fig. 8a) are interpreted as older fault planes developed in an extensional setting and later reactivated in relation to the present compressional stress regime in upper basement. In conclusion, the main fault zone spanning from 800 to 1100 mbsf is characterized by the presence of discrete deformation intervals, where fault planes are a result of the earlier extension and later (to/present) compression at Site 504. Fluid circulation. Within the main fault zone, the chemical properties are also found to be modified, as characterized in alteration mineralogy (e.g. Cu and Zn; Fig. 8b, c). Such changes are in relation with early normal faulting and hot fluids circulation. In addition, besides a step change of 4 ~ in downhole total magnetic field inclination at 800 mbsf (Fig. 8b), the magnetic properties are found to be strongly modified over an interval spanning from 900 to 1050 mbsf (Pariso & Johnson 1991). The intensity of magnetization and the magnetic susceptibility of samples in this interval are one to two orders of magnitude lower than that obtained in the crust above and below. These low values are

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explained by a rapid cooling and alteration after the circulation of hydrothermal fluids at high temperatures (from 200 up to 400 ~ characteristic of axial hydrothermal processes (Pariso & Johnson 1991). This 150m thick interval is one of low frequency signal in downhole measurements (Fig. 8) of total magnetic field and inclination, due to the weak intensity of remanent magnetization. The main fault zone located between 800 and 1100 mbsf is mineralogically characterized by the occurrence of greenschist facies (e.g. actinolite, chlorite, epidote, quartz). This zone is also characterized by the presence of zeolite-rich veins, in particular between 880 and 1000 mbsf. Zeolite veins are interpreted by Alt et al. (1996) to result from low temperature evolved fluids, due to off-axis hydrothermal circulation. The occurrence of minerals of the greenschist facies together with zeolites further suggests that the crust underwent successive stages of alteration due to varying circulating fluids (Alt et al. 1986; Agar 1990, 1991). Within the main fault zone, the stockworklike sulphide mineralization (Honnorez et al. 1983) occurs between 900 and 920 mbsf, and is characterized by a network of mineralized veins, mainly composed of greenschist facies minerals and large sulphide crystals. This interval is interpreted to have served as a conduit for hot fluids (Agar 1991). The same depth interval (from 900 to 920 mbsf) is observed to be very brecciated (Fig. 8e) and characterized by the presence of zeolite veins (Fig. 8f). In conclusion, the hydrothermal alteration developed initially in the main fault zone under greenschist facies conditions then, later, was overprinted by zeolite facies conditions. These two superimposed metamorphic facies are then attributed to hydrothermal alteration that took place, respectively, on-axis at temperatures from 200 ~ to 400~ and off-axis at lower temperatures lower than 250~ Within this main fault zone, the stockwork-like sulphide mineralization interval is interpreted to have probably served as a preferential conduit for both hot and, later, much colder fluids. L o w e r f r a c t u r e zone (1700 m b s f to 2111 mbsf) Fracturing. The lower part of Hole 504B, between 1700 mbsf and the present bottom of the hole (2111 mbsf), is characterized by a global increase in FMS fracture density with increasing depth (Figs 4 & 9b), as opposed to the gradual decrease with depth observed above, between

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Fig. 9. Composite profiles illustrating geophysical logs in the lower fracture zone. (a) core recovery percentage. (b) To the left, the deep electrical resistivity profile (LLd) and to the right, the total plane density derived from corrected FMS data. (c) Open porosity fraction (free fluid porosity over total porosity), on the left; subhorizontal plane density derived from corrected FMS data, on the right. (d) Apparent aperture in mm of planes directly mapped from FMS images, on the left; temperature gradient recorded at the end of coring operations during ODP leg 140, on the right. Distribution of zeolites derived from petrographic observation in thin sections are presented on this diagram (closed circles).

1100 to 1700 mbsf (Fig. 4). This zone is also characterized by discrete m-scale intervals with high values of open porosity fraction, in places exceeding 30% (Fig. 9c). These intervals often correspond to maxima in FMS subhorizontal fracture density and large although scattered values of apparent aperture of FMS planes (e.g. about 1720, 1925, 2000 mbsf). These m scale zones are similar to the one found in the main fault zone (at about 1005, 1020, 1035, 1065, and 1080 mbsf; Fig. 8), and may indicate the present sites of deformation, in relation to the compressional (above 1500 mbsf) to strike-slip (below 1700 mbsf) stress regime proposed by Pezard et al. (1995) from the base of Hole 504B. No FMS data were recorded over the section spanning from 1890 to 1800 mbsf, as the sensor was closed, hence inoperative, to avoid it getting stuck. This interval with rapid changes in hole size and numerous restrictions corresponds to a

low recovery zone (Fig. 9a). The poor recovery at this depth (1890 to 1800) may be associated with a very fractured and/or brecciated zone. Broken material is naturally less recovered than massive material in the core. Several m scale intervals appear to be characterized by a decrease in electrical resistivity values (at about 1825, 1860 and 1925 mbsf; Fig. 9b). The low electrical resistivity values often correspond to high OPF and FMS density values for horizontal planes (Fig. 9c). These discrete m scale intervals may be interpreted as resulting from the present deformations, in relation to compressive to strike-slip stresses. The deformation zone located at about 1925 mbsf corresponds to an abrupt decrease in electrical resistivity. The FMS plane azimuths and dips show a maximum of subhorizontal fracture density between 1920 and 1940 mbsf (Fig. 10a), which corresponds to a subhorizontal

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Fig. 10. (a) Plane azimuths and dips mapped from FMS images over the interval spanning from 1900 to 2100 mbsf represented versus depth. Subhorizontal (dip < 30~ closed triangle), 'intermediate' (30~ dip < 60~ plus sign), subvertical (60~ dip < 85~ vertical bars), and steep fractures (dip 85~ closed circles) are discriminated with different symbols. (b) Schmidt equal area projection in lower hemisphere of 998 plane poles derived from FMS images over 20 metres (from 1920 to 1940 mbsf) of basaltic crust crossed by Hole 504B.

fault zone located in the lower part of Hole 504B (Ayadi et al. 1996). The geometry distribution suggests that the average of subhorizontal planes is dipping 25 ~ to the south (Fig. 10b), thus away from the ridge axis. In conclusion, the lower fracture zone is characterized by the presence of several m scale faulting intervals which correspond to the present sites of deformation, associated with the compressive to strike-slip stresses. This stress regime generated a subhorizontal fault zone located between 1920 and 1940 mbsf, with average dips of about 25 ~ to the south. Fluid circulation. Although the interval spanning

from 1700 to 2111 mbsf is mainly characterized by amphibolite and greenschist facies alteration, local zeolites crystallized at much colder temperatures are also identified (Dick et al. 1992; Alt et al. 1993; Laverne et al. 1995; Tartarotti et al. 1995; Fig. 9d). The zeolites fill the veins together with amphiboles, and are considered as the most recent alteration stage (Alt et al. 1993). The occurrence of zeolites at this depth can be explained by:

(1) sea water circulation caused by drilling; (2) hydrothermal fluid circulation at low temperature ( < 250~ within the fault planes.

The temperature gradient profile obtained in Hole 504B during O D P Leg 140 within this lower fracture zone, suggests the presence of intervals with high permeability corresponding to an increase of temperature gradient values, (1825 and 1860 mbsf). One of these intervals (near 1825 mbsf) is characterized by the presence of zeolite rich veins (Fig. 9d). These intervals may correspond to active fault planes which constitute a preferential conduit to fluid circulation at a relatively low temperature. In conclusion, we infer that this lower fracture zone was submitted first to an alteration due to fluid circulation at high temperatures resulting in greenschist facies minerals crystallization, and later a cooling due to low temperature fluid circulation and/or sea water circulation due to drilling. Active fault planes were probably the preferential place of cooling and occurrence of low-temperature minerals, such as zeolites.

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Conclusions

While core analyses are essential to study tectonic and hydrothermal processes, true fracturing estimates from core are grossly underestimated in this case. A detailed description of fracturing and fluid circulation throughout three fracture zones (400 to 575, 800 to 1100 and 1700 to 2111 mbsf) is derived from the integrated analyses of core-logs-FMS images obtained in Hole 504B. Besides structural description, dynamic of faulting processs, at present and in the past, is deduced from this study. We infer that Site 504 was submitted first to an extensional stress regime near the ridge axis, with circulation of high-temperature fluids (200 ~ to 400~ and pervasive alteration of the basalts along steep planes. This initial phase is associated with the main fault zone (800 to 1100 mbsf) met in Hole 504B. Within the main fault zone, the interval between 840 and 958.5 mbsf lying close to the lithological transition zone between pillows and dykes, is one of the most deformed intervals in Hole 504B (Agar 1991). Similar but less developed normal faulting was generated slightly offaxis, with the development of lower temperature (<250~ parageneses (e.g. zeolites), such as between 400 and 575 mbsf in Hole 504B. The present compressional stress regime in the upper basement (Moos & Zoback 1990) and strike-slip one in the lower basement (Pezard et al. 1995), are expressed in subhorizontal fracturing present in discrete zones, such as within the main fault and below 1700 mbsf in Hole 504B. If horizontal decoupling horizons appear as a crash-zone in ophiolite (Agar & Klitgord 1995), then the compressional to strike-slip stress field might be the origin of block rotations about a ridge-parallel axis along one or several decoupling zones. This result is coherent with block tilting to the south observed at the sediment/basement interface at Site 504. We infer here that the main fault zone and the lower fracture zone crossed by Hole 504B could be of similar origin to the crush zones described in ophiolites. Carlos Pirmez and one anonymous reviewer are thanked for detailed and helpful comments on the manuscript. The FMS images were analysed with the Fraciew software of Schlumberger. This work was supported by the Groupement de Recherche 'Physique et M6canique des Roches', and the 'G~osciences Marines' ODP support program of CNRS in France. References

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KNIPE, R. J. & RUTTER, E. H. (eds), Deformation mechanisms, rheology and tectonics, Geophysical Society, 54, 41-50. - 1991. Microstructural evolution of a deformation zone in the upper ocean crust: evidence from DSDP Hole 504B. Journal of Geodynamics, 13, 119-140. - & KLITGORD, K. D. 1995. A mechanism for decoupling within the oceanic lithosphere revealed in the Troodos ophiolite. Nature, 374, 232-238. ALT, J. C., LAVERNE,C. & MUEHLENBACHS,K. 1985. Alteration of the upper oceanic crust: Mineralogy and processes in Deep Sea Drilling Project Hole 504B. In: ANDERSON, R. N., HONNOREZ, J., BECKER, K., el al. 1985. Initial reports of the Deep Sea Drilling Project, 83. Washington (U.S. Govt. Printing Office), 217-248. , HONNOREZ,J., LAVERNE,C. 8r EMMERMANN,R. 1986. Hydrothermal alteration of a 1 km section through the upper oceanic crust, DSDP hole 504B: The mineralogy, chemistry and evolution of seawater-basalt interactions, Journal of Geophysical Research, 91,309-335. , KINOSHITA, H., STOKK1NG, L. B., et al. 1993. Proceedings of" the Ocean Drilling Program, Initial Reports, 148. College Station, TX , TEAGLE,D. A. H., LAVERNE,C., VANKO,D. A., BACH, W., HONNOREZ,J., BECKER,K., AYADI, M. & PEZARD, P. A. 1996. Ridge flank alteration of upper ocean crust in the Eastern Pacific: synthesis of results for volcanic rocks of Holes 504B and 896A. In: ALT, J. C., KINOSHITA,H., STOKKING,L. B., et al., Proceedings of the Ocean Drilling Program, Scientific Results, 148. College Station, TX (Ocean Drilling Program), 435-450. ANDERSON, R. N., HONNOREZ,J., BECKER, K., ADAMSON, A. C., ALT, J. C., EMMERMANN,R., KEMPTON, P. D., KINOSHITA,H., LAVERNE,C., MOTTL, M. J. & NEWMARK, R. L. 1982. DSDP Hole 504B, the first reference section over 1 km through Layer 2 of the oceanic crust. Nature, 300, 589-594. , --, et al. 1985. Initial Reports of the Deep Sea Drilling Project, 83, Washington (U.S. Govt. Printing Office). AYADI, M., PEZARD, P. A. & LAROUZIORE,F. D. DE 1996. Fracture distribution from downhole electrical images at the base of the sheeted dike complex in DSDP/ODP Hole 504B. In: ALT, J. C., KINOSHITA, H., STOKKING, L. B., et al. (eds) Proceedings of the Ocean Drilling Program, Scientific Results, 148, College Station, TX (Ocean Drilling Program), 307-315. BECKER, K. 1985 : Large-scale electrical resistivity and bulk density of the oceanic crust, DSDP Hole 504B, Costa Rica Rift. In: ANDERSON, R. N., HONNOREZ, J., BECKER, K., et al. (eds) Initial Reports of the Deep Sea Drilling Project, 83. Washington (US Govt. Printing Office), 419-427. , SAI
FAULTING AND FLUID CIRCULATION AT DSDP/ODP SITE 504 Foss, G., et al. 1992. Proceedings of the Ocean Drilling Program, Initial Reports, 137, College Station, TX (Ocean Drilling Program). CANN, J. R., LANGSETH, M. G., HONNOREZ, J., VON HERZEN, R. P., WHITE, S. M., et al. 1983. Initial Reports of the Deep Sea Drilling Project, 69. Washington (US Govt. Printing Office). CASEY, J. F., DEWEY,J. F., Fox, P. J., KARSON,J. A. ROSENCRANTZ, E. 1981. Heterogeneous nature of oceanic crust and upper mantle: A perspective from the Bay of Islands Ophiolite complex, In: EMILIANI, C. (ed.) The Sea, 7, 305-338. COSTA RICA RIFT UNITED SCIENTIFICTEAM (CRRUST) 1982. Geothermal regimes of the Costa Rica rift, east Pacific, investigated by drilling, DSDP-IPOD legs 68, 69, and 70. Geological Society American Bulletin, 93, 862-87. DETRICK, R., COLLINS, J., STEPHEN, R. & SWIFT, S. 1994. In situ evidence for the nature of the seismic layer 2/3 boundary in oceanic crust. Nature, 370, 288-290. DICK, H. J. B., ERZINGER, J., STOKKING, L. l . , et al. 1992. Proceedings of the Ocean Drilling Program, Initial Reports, 140. College Station, TX (Ocean Drilling Program). EKSTROM, M. P., DAHAN, C. A., CHEN, M.-Y., LLOYD, P. M. & RossI, D. J. 1986. Formation imaging with microelectrical scanning arrays. Transactions of the Society of Professional Well Log Analysts, 27th Annual Logging Symposium, Paper 88. EMMERMANN, R. 1985. Basement geochemistry; hole 504B. In: ANDERSON, R. N., HONNOREZ, J., BECKER, K., et al. (eds) Initial Reports of the Deep Sea Drilling Project, 83, Washington (U.S. Govt. Printing Office), 183-200. GUERIN, G., BECKER,K., GABLE, R., & PEZARD, P. A. 1996. Temperature measurements and heat-flow analysis in Hole 504B. In: ALT, J. C., KINOSHITA, H., STOKKING, L. B., et al., Proceedings of the Ocean Drilling Program, Scientific Results, 148, College Station, TX (Ocean Drilling Program), 291-296. HONNOREZ, J., LAVERNE, C., HUBBERTEN, H. W., EMMERMAN, R., ~r MUEHLENBACHS, K. 1983. Alteration processes in layer 2 basalts from Deep Sea Drilling Project Hole 504B, Costa Rica Rift. In: CANN, J. R., LANGSETH,M. G., HONNOREZ,J., VON HERZEN, R. P., WHITE, S. M., et al. 1983. Initial Reports of the Deep Sea Drilling Project, 69, Washington (US Govt. Printing Office), 509-546. KENT, G. M., SWIFT, S. A., DETRICK, R. S., COLLINS,J. A. & STEPHEN, R. A. 1996. Evidence for active normal faulting on 5.9 My old crust near Hole 504B on the southern flank of the Costa Rica Rift. Geology, 24, 83-86. KIDD, R. G. W. 1977. A model for the process of formation of the upper oceanic crust, Geophysical Journal of the Royal Astronomical Society, 50: 149-183. KINOSHITA, H. T., FURUTA, T. & PARISO, J. 1989. Downhole magnetic field measurements and paleomagnetism, Hole 504B, Costa Rica Ridge, In: BECKER,K., SAKAI,H., et al. 1988. Proceedings of the Ocean Drilling Program, Initial Reports, --,

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111, College Station, TX (Ocean Drilling Program): 147-156. LANGSETH, M. G., MOTI'L, M. J., HOBART, M. A. & FISHER, A. 1988. The distribution of geothermal and geochemical gradients near Site 501/504: implications for hydrothermal circulation in the oceanic crust. In: BECKER, K., SAKAI, H., et al. (eds) Proceedings of the Ocean Drilling Program, Initial Reports, 111: College Station, T X , 23-32. LARSEN, H. L. SAUNDERS,A. D. CLIFT, P. et al. 1994. Proceedings of the Ocean Drilling Program, Initial Reports, 152, College Station, TX (Ocean Drilling Program). LAVERNE, C. 1987. Les alterations des basaltes en domaine oc6anique, min+ralogie, p&rologie et g6ochimie d'un syst+me hydrothermal: le Puits 504B, Pacique oriental. PhD Thesis, University of Aix Marseille III. , VANKO, D. A., TARTAROTTI, P. & ALT, J. C. 1995. Chemistry and geothermometry of secondary minerals from the deep sheeted dike complex, Hole 504B. In: ERZ1NGER,J., BECKER, K., DICK, H. J. B. & STOKKING,L. B. (eds) Proceedings of the Ocean Drilling Program, Scientific Results, 137/ 140. College Station, TX (Ocean Drilling Program), 167-189. LONSDALE, P. 1977. Deep-tow observations at the mounds abyssal hydrothermal field, Galapagos rift. Earth and Planetary Science Letters, 36, 92110. LUTHI, S. M. t~ BANAVARJ. R. 1988. Application of borehole images to three-dimensional geometric modelling of eolian sandstone reservoirs, Permian Rotliegende, North Sea. American Association of Petroleum Geologist Bulletin, 72, 1074-1089. - t~ SOUHAIT6, P. 1990. A method for fracture extraction and width determination from electrical borehole scans, Geophysics, 55, 821-833. Moos, D. & ZOBACK, M. D. 1990. Utilisation of observations of wellbore failure to constrain the orientation and magnitude of crustal stresses; Application to continental DSDP and ODP boreholes. Journal of Geophysical Research, 95 (B6), 9305-9325. NEHLIG, P. & JUTEAU, T. 1988. Deep crustal seawater penetration and circulation at ocean ridges: Evidence from the Oman ophiolite. Marine Geology, 84, 209-228. NEWMARK, R. L., ANDERSON, R. N., Moos, D. & ZOBACK,M. D. 1985. Sonic and ultrasonic logging of Hole 504B and its implications for the structure, porosity and stress regime of the upper 1 km of the oceanic crust. In: ANDERSON, R. N., HONNOREZ,J., BECKER,K., et al. (eds) 1985. Initial Reports of the Deep Sea Drilling Project, 83, Washington (U.S. Govt. Printing Office), 497510. PARISO, J. E. & JOHNSON, H. P. 1991. Alteration processes at Deep Sea Drilling Project/Ocean Drilling Program Hole 504B at the Costa Rica Rift: implications for magnetization of oceanic crust. Journal of Geophysical Research, 96, 11 70311 722. PEZARD, P. A. 1990. Electrical properties of MORB,

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and implications for the structure of the oceanic crust at DSDP Site 504. Journal of Geophysical Research, 95, 9237-9264. 8r ANDERSON, R. N. 1989. Morphology and alteration of the upper oceanic crust from in situ electrical experiments in DSDP hole 504B. IN: BECKER, K. et al. (eds) Proceedings of the Ocean Drilling Program, Scientific Results, 111, College Station, TX (Ocean Drilling Program), 133-146. , LOVELL, M. A. & ODP LEG 126 SHIPBOARD SCIENTIFIC PARTY 1990. Downhole images: electrical scanning reveals the nature of subsurface oceanic crust. EOS, Transactions of the American Geophysical Union, 71, 709. , CORROTTI, P., AYADI, M., REVIL, A., MOOS, D. & WILKENS, R. n . 1995. Fracture, faults and tectonic stresses in the Upper Oceanic crust from ODP Core and downhole Measurements. EOS, Transactions, American Geophysical Union, 1995 Fall Meeting, 76, F325. - - , AYADI, M., REVIL, A., BRONNER, G. & WILKENS, R. H. 1997. Detailed structure of an -

oceanic normal fault; a multi-scalar approach at DSDP/ODP Site 504. Geophysical Reaserch Letters, 24, 337-340. ROSENCRANTZ,E. 1983. The structure of sheeted dykes and associated rocks in North Arm massif, Bay of Islands ophiolite complex, and the intrusive process at oceanic spreading centers. Canadian Journal of Earth Sciences, 20, 787-801. TARTAROTTI,P., ALLERTON,S. A. & LAVERNE,C. 1995. Vein deformation mechanisms in the sheeted dike complex from Hole 504B. In: ERZINGER, J., BECKER, K., DICK, H. J. B. & STOKKING, L. B. (eds) Proceedings of the Ocean Drilling Program, Scientific Results, 137/140, College Station, TX (Ocean Drilling Program), 231-241. , AYADI, M., PEZARD, P. A., LAVERNE, C. • DE LAROUZIERE, F. D. 1998. Multi-scalar structure at D S D P / O D P Site 504, Costa Rica Rift, II: fracturing and alteration. An integrated study from core, downhole measurements and borehole wall images. This volume.

Quartz cement volumes across oil-water contacts in oil fields from petrography and wireline logs: preliminary results from the Magnus Field, Northern North Sea S. A. B A R C L A Y & R. H. W O R D E N

School o f Geosciences, The Queen's University o f Belfast, Belfast, B T 7 I N N , Northern Ireland Abstract: Quartz cement is a significant porosity-reducing mineral cement in many

sandstones and thus affects economically significant reserves calculations and flow-rate (through its effect on permeability). The presence of oil in a reservoir is commonly assumed to retard quartz cement precipitation and thus early oil emplacement is often thought to preserve porosity and permeability. A combined petrographic and wireline log approach was utilized to investigate whether quartz cement volumes and the total quantity of quartz do indeed vary across the oil-water contact in a sandstone reservoir. Thin-section pointcount data and bulk density, neutron porosity and sonic transit time wireline log data were obtained across the oil-water contact from three wells in the Magnus field, an Upper Jurassic turbidite sandstone reservoir in the Northern North Sea. Reported oil filled inclusions in quartz overgrowths in this reservoir show that quartz cementation occurred either during or after oil emplacement. Point count data were used to determine quartz cement and total quartz volumes across the oil-water contact, whilst wireline data were transformed to reveal the total quantity of quartz across the oil-water contact. Preliminary results seem to show that the volume of quartz cement and the total volume of quartz show little or no variation across the oil-water contact. These data seem to imply that the presence of oil in the reservoir had no appreciable effect on the component processes involved in quartz cementation in this field: a paradox that will be further investigated. The distribution of quartz cement is a major control on sandstone reservoir quality (Coskun et al. 1993). Quartz cement can reduce porosity by occluding pores, thus reducing oil volume. It can also have an effect on permeability by the general reduction in porosity and specifically by reducing the diameter of pore throats. One of the major factors that has widely been assumed to either retard, or halt quartz cementation in a reservoir is the prior emplacement of oil (e.g. Glasmann et al. 1989; Robinson & Gluyas 1992). It is commonly assumed that replacing water in the pores by oil must halt inorganic geochemical processes including those involved in quartz cementation. This assumption however does not take account of either the preferred wetting state of the reservoir or the source of the silica in the quartz cement. Although a reservoir may have reached maximum oil saturation, it can be water wet and the pore network can be filled with approximately 20% water. There exists, therefore, the possibility of continued silica transport, quartz dissolut i o n a n d silica p r e c i p i t a t i o n a f t e r oil emplacement. The possibility of quartz cementation continuing in the presence of oil is strongly influenced by the water saturation of the

reservoir and wettability (Worden et al. 1998). Oil inclusions are not u n c o m m o n in quartz cements although these are often considered to form early in oil-filling history before maximum oil saturation was achieved (Larter & Aplin 1995). The oil trapped in the inclusions is often less mature than the oil in the reservoir (Larter & Aplin 1995). In areas where the oil source rock was progressively being buried and heated (e.g. the North Sea), migrated oil to the reservoir became more mature with time. This suggests that oil inclusions are typically formed during the early stages of reservoir filling and do not permit the implication of continued quartz cementation at maximum oil saturation. Gluyas et al. (1993) illustrated an inverse relationship between quartz cement volume and oil inclusion abundance, with the greatest abundance of oil inclusions at the crest of the field. Assuming reservoirs fill from the crest to the flank (England et al. 1987), this inverse relationship allows us to infer that earlymigrated oil was trapped to form oil inclusions at the crest and that as the oil saturation of the reservoir decreased towards the flanks, the growth of quartz cement was less inhibited towards the flanks of the field (Gluyas et al. 1993).

BARCLAY,S. A. & WORDEN,R. H. 1998. Quartz cement volumes across oil-water contacts in oil fields from petrography and wireline logs: preliminary results from the Magnus Field, Northern North Sea In." HARVEY,P. K. • LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 327-339

327

328

S.A. BARCLAY & R. H. WORDEN

Fig. 1. Maps showing location of Magnus field in Northern North Sea and well locations.

Quartz is usually preferentially water-wet (Schlangen et al. 1995; Barclay & Worden 1997). One of the key problems with the formation of oil inclusions is the mechanism of trapping a non-wetting fluid. Macleod et al. (1993) showed that oil in inclusions is enriched in polar compounds compared to the oil in the reservoir. Brown & Neustadter (1980) and Schlangen et al. (1995) have both demonstrated that polar compounds can act as surfactant for the water-oil~tuartz system, altering the wetting preference of quartz from hydrophilic to oleophilic. This might permit quartz to trap droplets of oil thus forming oil inclusions although how an oil-wet system allows quartz cementation to occur remains to be understood. The evidence presented above implies that oil inclusions form in conditions that are potentially unrepresentative of the reservoir at maximum oil saturation with a mature oil, and therefore

should not be used as proof that quartz cementation continues after reservoir filling. However, oil inclusions may still be used to show that oil emplacement and quartz cementation occurred synchronously in a reservoir as oil inclusions cannot form in the absence of oil in the reservoir. In this paper, we describe the distribution of quartz cement across the oil-water contact (OWC) of a submarine fan sandstone hydrocarbon reservoir: the Magnus Field in the Northern North Sea, UKCS. We have used a combination of point-count data and wireline analysis methods to examine the distribution of quartz cement and the bulk distribution of quartz in the reservoir. The results obtained allow us to draw conclusions on; (1) the possibility of quartz cementation continuing after oil emplacement in Magnus;

QUARTZ CEMENT VOLUMES ACROSS OIL-WATER CONTACTS

329

Fig. 2. Geological cross-section of the Magnus field. (2) the likely preferred wetting-state of the reservoir during oil emplacement; (3) the potential source of the silica in the quartz cement; (4) the use of wireline logs to obtain information about total quartz and quartz cement volumes in reservoirs.

Chronostratigraphy

Kimmeridgian 140 Magnus Ssi Mmbr Lower Kimmeridge Clay Fm Oxfordian 150 Upper Heather Fm Callovian

Regional setting

Structural and stratigraphic evolution Deposition of the Magnus sandstone and Upper Kimmeridge Clay Formation (Fig. 3) was followed by a depositional hiatus during the early Cretaceous (late Cimmerian) when the reservoir was faulted, uplifted, exposed and eroded. Deposition of the overlying Cretaceous

Lithostratigraphy

Portlandian 130 Upper Kimmeridge Clay Fm

The Magnus Field

The Magnus field lies 160km northeast of the Shetland Islands and is within UKCS exploration blocks 211/12a and 211/7a (Fig. 1). The field occurs at the southern margin of the North Shetland Basin, typified by easterly dipping fault blocks and Upper Jurassic reservoir sandstones (Figs 1 & 2; De'Ath & Schuyleman 1981). The main reservoir, the Magnus Sandstone Member, occurs stratigraphically between the Lower and Upper Kimmeridge Clay Formations (Fig. 3).

Ma.

Bathonian ~ ~

Bajocian Aalenian

160 Lower Heather Fm

170

Ness Rannoch & Etive

Broom

Fig. 3. Stratigraphic column showing the Magnus Sandstone Member. sediments was further interrupted by a second break in deposition in the mid-Cretaceous (Cenomanian-Turonian) with erosion locally removing Lower Cretaceous and Jurassic sediments from the northern area of the field. Rapid subsidence and deposition occurred during the Tertiary and Quaternary, with tilting of the reservoir towards the northeast during the early Tertiary (De'Ath & Schuyleman 1981).

330

S.A. BARCLAY & R. H. WORDEN

Table 1. Reservoir lithofacies in the Magnus field. Lithofacies IV is the most important in terms of the volume of trapped petroleum

Lithofacies

Description

Depositional environment

0

Laminated mudstones of hemipelagic turbiditic origin Various sediments remobilizd during mass flow processes Interlaminated thin mudstone and very fine/fine grained sandstones Thickly-bedded fine- to coarse- grained sandstones

Basin plain

I II & III IV

R e s e r v o i r characteristics a n d oil source

Maximum closure of the Magnus Sandstone Member is 350 m and the field covers an area of 34 km2, the maximum vertical thickness of gross oil sand is 140m, although it thins both eastwards and westwards. The mean porosity of the Magnus sandstone ranges from 25% in the western half of the field to 19% in the eastern half of the field (De'Ath & Schulyeman 1981). Oil was sourced from both the Lower and Upper Kimmeridge Clay Formations. The source is thought to be downflank to the north and east of the reservoir. The oil is contained within a combined stratigraphic and structural trap. The seal is a combination of unconformably overlying Cretaceous marls and the conformably overlying Upper Kimmeridge Clay Formation mudstones (De'Ath & Schuyleman 1981; Emery et al. 1993).

Geological characteristics of the Magnus Sandstone Member S e d i m e n tology

The Magnus sandstones are predominantly submarine fan, sub-arkosic to arkosic, fine- to coarse-grained and generally poorly sorted sediments (De'Ath & Schulyeman 1981). Sedimentological analysis of the Magnus Sandstone Member revealed that five distinct depositional lithofacies were present (Table 1). In terms of the volume of oil in the reservoir, lithofacies IV is the most important (Emery et al. 1993). All of the data presented in this paper are from lithofacies IV thus negating facies-dependent control on cementation. Diagenetic history

Early diagenesis of the Magnus Sandstone Member occurred during and shortly after

Outer-fan and basin plain Mid-fan (interchannel, lobe) or outer fan Channelled or unchannelled fan lobes in mid-fan

deposition, with the precipitation of non-ferroan calcite and pyrite (Emery et al. 1993). The next phase of diagenesis occurred when the Magnus Sandstone Member was subaerially exposed during the early Cretaceous, allowing entry of meteoric water causing K-feldspar dissolution, and precipitation of kaolinite (Emery et al. 1990). The Magnus Sandstone Member was subsequently re-buried and compacted, although negligible cementation occurred until the first oil reached the sandstone. The movement of oil into the Magnus sandstones coincided with the onset of deep burial diagenesis. The cements formed are (in paragenetic order) K-feldspar, quartz, kaolinite, illite, siderite and ankerite (Emery et al. 1993).

Samples and methods Wireline and point-count data were obtained from across the oil-water contact (OWC) for three wells in the Magnus field: 211/12a-ll (depth range=3140m to 3220mTVD), 211/ 12a-09 (depth range=3180m to 3380mTVD) a n d 211/7-1 ( d e p t h r a n g e = 3 1 6 0 m to 3220mTVD). The locations of these wells are shown on Fig. 1. Wireline data

Sonic transit time, neutron porosity and bulk density wireline log data for each well were used to derive three mineral components ('quartz', 'clay' and 'dolomite') and porosity using the methodology introduced by Savre (1963), and described by Doveton (1994) and Hearst & Nelson (1985) for each depth interval. There are clearly more than these three mineral components present in the reservoir (e.g. various feldspar types), but these three were chosen because previous work on the Magnus Sandstone has shown that these three components are volumetrically dominant (De'Ath & Schuyleman

QUARTZ CEMENT VOLUMES ACROSS OIL-WATER CONTACTS

331

Table 2. Definition of terms used in equations (1) to (4) Term

Definition

At Atminx

sonic transit time recorded by log (sec ft 1) sonic transit time of mineral X (sec ft-~) sonic transit time of fluid in pore space (sec ft 1) density recorded by log (gcm 3) density of mineral X (gcm 3) density of fluid in pore space (gcm-3) neutron porosity recorded by log (porosity units) neutron porosity of mineral X (porosity units) neutron porosity of fluid in pore space (porosity units) proportion of mineral X (as a fraction of total rock volume) porosity (as a fraction of total rock volume)

Ato P

Pminx pO On

Onminx Ono minX O

1981), and the three wireline logs used could differentiate between them successfully. The rationale behind the mineralogy-derivation method is that different minerals have different characteristic responses to the sonic, neutron and density tools. The signals from the sonic transit time, neutron porosity and bulk density logs can be integrated and resolved for three mineral types and total porosity using three algorithms relating each separate log signal at any given depth to solid grain volume (occupied by the three minerals) and the assumption that the sum of the three mineral fractions plus porosity equals unity. This approach also assumes a linear relationship between mineral proportions and their contribution to the response on any of the logs used. Therefore, with four equations and four unknowns (the proportions of the three minerals and porosity), the following algorithms (equations 1 to 4) can be solved simultaneously at each depth. At = minl .Attain1 + min2. A tmin2 + min3.Atmin3 + At. O (1) p = minl .Pminl

+

min2.Prnin2 + min3.Pmin3 + p.O

(2) On = minl .Onminl -+-min2.0nmin2 + min3.0nmin3 + On.O (3) 1 = m i n l +min2 + min3 + O

(4)

The terms used in equations (1) to (4) are defined in Table 2. The sonic transit time, neutron porosity and bulk density wireline responses for the three minerals were taken from Rider (1986). The value of'quartz' derived from the wireline data used includes all types of detrital and

diagenetic quartz as well as feldspars. This is because the three wireline logs used are insensitive to changes in quartz type (i.e. the difference between detrital and authigenic quartz), also quartz and feldspar have a very similar response on these logs. Therefore the wireline 'quartz' data actually represents a 'pseudo-quartz' value which may be defined: ' p s e u d o - q u a r t z ' = m o n o - and poly-crystalline detrital quartz + quartz cement + feldspar (5) The gamma log has often been used in the past to quantify the clay content of reservoir sandstones (Hearst & Nelson 1985). The composite gamma log records the total potassium, thorium and uranium content of the rock; the spectral gamma log differentiates between the gamma radiation from the three elements. As most clay minerals do not contain any potassium, the composite g a m m a log and the potassium spectral gamma log indiscriminately record the total abundance of potassium feldspar, illite and mica. Potassium feldspar is not uncommon in the Magnus Sandstone (classified as sub-arkosic; De'Ath & Schuyleman 1981) which also contains smaller quantities of mica and potassium-bearing clays such as illite. Using the composite gamma log to attempt to estimate the clay content of the Magnus Sandstone will produce artificially high estimates of the clay content. The problem is compounded because many detrital feldspars contain variable and unpredictable quantities of potassium. The thorium gamma signal (derived from the spectral gamma log), has been used in the past to identify and quantify the amount of kaolinite in sandstones (Serra et al. 1980; Quirein et al. 1982). However, Hurst & Milodowski (1996) have recently shown that the thorium gamma signal reflects the abundance of thorium-bearing

332

S.A. BARCLAY & R. H. WORDEN

Quartz fraction

Quartz fraction

0.5

0.5 I

3140 3150 3160

3180 3200

D D ~>

=, ~"

3190 3200

3240

-~

o

3260

>

3280

9~ "~

3300

~

~> E>

[> E>

OWC

WDt

B[] 2

owc

Q~

3320

E>

3210

~?

r

3170 0

3180

3220 C>

~JP

3340

3220

3360

3230

~

3380 9 Wireline pseudo-quartz [] Point-count pseudo-quartz

9 Wireline pseudo-quartz [] Point-count pseudo-quartz

A Point-count quartz cement

zxPoint-count quartz cement

Fig. 4. Variation of point-count quartz cement fraction (open triangles), point-count pseudo-quartz (open squares) and wireline pseudo-quartz (black circles) across the oil-water contact (OWC) for 211/12a-11.

Fig. 5. Variation of point-count quartz cement fraction (open triangles), point-count pseudo-quartz (open squares) and wireline pseudo-quartz (black circles) across the oil-water contact (OWC) for 211/12a-09.

heavy minerals (e.g. monazite) within the sandstone, and bears no genetic relationship to the amount of kaolinite. It was therefore not possible to use the gamma wireline logs to quantify potassium feldspar or clay in the reservoir, because of the ambiguities inherent in the allocation of the radioactive potassium or thorium signal to feldspar and clay minerals.

the basis of 200 solid grain counts (with porosity counted on a separate channel) per section. The point count data were also converted into fractional values of point-count pseudo-quartz using equation (5) to facilitate comparison between wireline and petrographic data.

Petrography

The point-count quartz cement fraction, pointcount pseudo-quartz fraction and wireline-derived pseudo-quartz fraction are plotted as a fraction of the rock volume as a function of depth for wells 211/12a-11,211/12a-09 and 211/ 7-1. (Figs 4, 5 and 6, respectively). The other lithological data derived from the wireline logs

Thin-sections were prepared using blue-dyed epoxy impregnation and stained for feldspars and carbonates using standard techniques (Hayes & Klugman 1959; Dickson 1965). Petrographic data were originally collected by BP on

Results

QUARTZ CEMENT VOLUMES ACROSS OIL-WATER CONTACTS

333

Table 3. Calculated means (+ standard deviations) for the point-count quartz cement fraction, point-count pseudoquartz fraction and wireline pseudo-quartz fraction in each of the studied wells. Values shown are on a scale of O to I

Point-count quartz cement fraction

Point-count pseudo-quartz fraction

Wireline pseudo-quartz fraction

Well

Position

211/ 12a-09

oil leg water leg

0.08 (• 0.08 (•

0.60 (• 0.61 (•

0.60 (-+-0.05) 0.59 (-+-0.06)

211/12a-11

oil leg water leg

0.09 (+0.02) 0.08 (•

0.57 (+0.04) 0.58 (•

0.60 (+0.05) 0.59 (-4-0.04)

211/7-1

oil leg water leg

0.04 (+0.02) 0.07 (+0.01)

0.62 (+0.08) 0.58 (-t-0.06)

0.65 (+0.04) 0.65 (-1-0.05)

Quartz fraction 0 3160

0.5

>

(i.e. 'dolomite', 'clay' and 'porosity') are omitted to simplify the data presentation. Mean and standard deviation of the wireline pseudo-quartz fraction, point-count pseudo-quartz fraction and quartz cement fraction are presented in Table 3.

3170 Discussion

The timing o f oil e m p l a c e m e n t on quartz

3180

r

c e m e n t a t i o n - p r e v i o u s w o r k on M a g n u s [>

O

[>

3190 gl,

D

0

[>

~r 3200

3210

~>

O0

D

3220 D

go

3230 9 Wireline pseudo-quartz [] Point-count pseudo-quartz A Point-count quartz cement

Oil emplacement and quartz cementation are thought to have occurred synchronously in Magnus (Emery et al. 1993). The burial and thermal history of the Magnus field source rock, the Kimmeridge Clay Formations, indicates that oil started migrating into the Magnus reservoir at approximately 80 Ma. Primary aqueous fluid inclusions reveal a relatively restricted range of homogenization temperatures (approximately 90 to 120~ Emery et al. 1993). This range of temperatures was equated to quartz cementation occurring between 80-65 Ma for quartz cementation (from thermal history models). The coincidence of oil emplacement and quartz cementation was used by Emery et al. (1993) to infer that the two events were synchronous. The presence of primary oil inclusions in quartz cement within Magnus was used by Emery et al. (1993) as supporting evidence for the coincidence of oil emplacement and quartz cementation. Distribution o f quartz in the M a g n u s f i e l d

Fig. 6. Variation of point-count quartz cement fraction (open triangles), point-count pseudo-quartz (open squares) and wireline pseudo-quartz (black circles) across the oil-water contact (OWC) for 211/7-1.

The mean amounts of wireline and point-count pseudo-quartz do not vary by a significant amount across the oil-water contact (OWC) in

334

S.A. BARCLAY & R. H. WORDEN 1.0

0.20

__J.. (a) 211/12a-ll 1:1 correlation line]

(a) 211/12a-ll L * * * ~

0.8

0.15-

/

0.6

#*

['i:1 correlation line

0.2

0.05-

J

~o

l_

0.10-

J

0.4

O

(b) 211/12a-09

0.8

."

/

J

/

0.15

8

,~ 0.6 O

(b) 211/12a-09

1"1 correlation line]

""

0.10 0.4 cl,

8

0.2

.~.

'i:1 correlation line I

0.05

J

o

O

(c) 211/7-1

0.8

(c) 211/7-1

1:1 correlation line]

0.15.

/....

0.6

/

o

9

0.10. 0.4 0.2

[1:1 correlation line I

J,

0.0 0.0

0.05. O

0.2

01.4

i 0.6

01.8

1.0

Wireline pseudo-quartzfraction

0.00 0.0

'

'

0.2

0.4

0.6

9

*, 0.8

1.0

Point-count pseudo-quartzfraction

Fig. 7. Cross-plots of point-count pseudo-quartz and wireline pseudo-quartz for wells 211 / 12a- 11, 211 / 12a09 and 211/7-1, respectively.

Fig. 8. Cross plots of point-count quartz cement and point-count pseudo-quartz for wells 211/12a-11, 211/

any of the three wells (Figs 7-9; Table 3). This suggests that the total amount of quartz is uniformly distributed about the OWC in Magnus. The mean amount of point-count quartz cement also does not vary significantly across the OWC (Table 3). There is a slight difference in well 211/7-1, where the amount of point-count quartz cement increases from 0.04 in the oil leg to 0.07 in the water leg (both values on a scale of 0 to 1), but this change falls within the range of the standard deviation.

the l:l correlation line on each plot, suggesting that there is a reasonably good correlation between petrographically- and petrophysicallydetermined total quartz volumes. These results indicate that the wireline pseudo-quartz value can be used to estimate the total quartz volume in a reservoir.

Comparison of point-count pseudo-quartz and wireline pseudo-quartz data The point-count pseudo-quartz and wireline pseudo-quartz data for wells 211/12a-11, 211/ 12a-09 and 211/7-1 are plotted in Fig. 7a to c. The data for all three wells falls on, or near to,

12a-09 and 211/7-1, respectively.

Comparison of point-count and wireline pseudo-quartz with point-count quartz cement data The point-count and wireline pseudo-quartz versus point-count quartz cement data for wells 211 / 12a- 11, 211 / 12a-09 and 211/7-1 are plotted in Figs 8a-c and 9a-c. The data for these three wells do not plot near to the 1:1 line and do not correlate. The pseudo-quartz values are composed of more than one quartz type (quartz

QUARTZ CEMENT VOLUMES ACROSS OIL-WATER CONTACTS 0.20 0.15

___J_ (a) 211/12a-ll 1:1 correlation line

335

Quartz % (a)

Oil leg

L

0.10

OW(

0.05

Waterleg

O

0.15

t.

/

;:':.

0.05

o

~ o

/ 0.15

Oil leg OWC

0.10

= =

Co)

/ (b) 211/12a-09 !: 1 correlation line[

r

(c) 211/7-1

1 :_1 correlation line I

0.10

Waterleg

~

(c)

OWC

Waterleg

0.05 0.00 0.0

Fig. 9. Cross plot of point-count quartz cement and wireline pseudo-quartz for wells 211/12a-11, 211/12a09 and 211/7-1, respectively.

Fig. 10. Effect of petroleum emplacement (a) before quartz cementation on cement distribution across the oil-water contact (b) after quartz cementation on cement distribution across the oil-water contact (c) synchronous with quartz cementation on cement distribution across the oil-water contact. Assuming quartz cementation is halted by oil emplacement.

cement, mono- and polycrystalline detrital quartz) and includes feldspar (see equation 5), whilst the point-count quartz cement value represents quartz cement only, with no contribution from detrital quartz or feldspar. However, these data (Figs 8a-c and 9a-c) are useful because they show that the total quartz volume does not increase with increasing quartz cement contents. This suggests that the occurrence of quartz cement in Magnus was not associated with an increase in the overall amount of quartz in the reservoir.

(1) the source of the cement---external or internal to the reservoir; (2) the specific effects of oil emplacement on quartz cementation--whether oil emplacement halts quartz cementation (e.g. Gluyas et al. 1993), or allows continued quartz cementation (e.g. Bjorlykke & Egeberg 1993); (3) the relative timing of quartz cementation and oil emplacement control the distribution of quartz cement across an OWC (Emery et al. 1993).

0.2 0.4 0.6 0.8 1.0 Wireline pseudo-quartz fraction

Effects of oil emplacement on quartz cementation in the Magnus field There are three major controls on quartz cementation in oil fields:

If the timing of quartz cementation could be fixed relative to the timing of oil emplacement, then it should be possible to quantify the likely effects of oil emplacement on quartz cementation in the three wells studied Firstly, assuming that the rate of quartz cementation is adversely

336

S.A. BARCLAY & R. H. WORDEN

affected by emplacement of oil within a reservoir, then theoretically there are three possible distributions of quartz cement across the OWC: (1) early oil emplacement would halt quartz cementation in the oil leg and have no effect on the water leg resulting in an abrupt change in quartz cement volume (Fig. 10a); (2) oil emplacement after quartz cementation would have no affect on quartz cement volumes in either the oil or the water legs (Fig. 10b); (3) oil emplacement during quartz cementation would lead to progressively less quartz cement passing up into the oil leg (Fig. 10c). Secondly, assuming that the rate of quartz cementation is not affected by the emplacement of oil in the reservoir, then whenever quartz cementation occurred, there should be equal amounts of quartz cement in the oil and water legs. In this last case, cement distribution should be independent of the relative timings of oil emplacement and quartz cementation (Fig. 11). Emery et al. (1993) asserted that quartz cementation and oil emplacement occurred synchronously in Magnus at 80 Ma through the use of fluid inclusion petrography and burial history modelling. Thus in Magnus we should witness either (1) progressively less quartz cement passing up into the oil leg if quartz cementation is halted by oil emplacement (i.e. as shown by Fig. 10c) or (2) uniform quartz cement volumes in the oil and water legs if quartz cementation is not affected by oil emplacement (i.e. as shown by Fig. 11). The results from all three wells (Figs 4, 5, 6 and Table 3) seem to show that the volumes of quartz cement, point-count pseudo-quartz and wireline pseudo-quartz do not change significantly across the OWC. Thus, despite previous assertions about inhibition of quartz cementation during and following oil emplacement within the Magnus Field (Emery et al. 1993), the data seem to show that the distribution of quartz cement in Magnus was unaffected by the presence of oil in the reservoir. P o s s i b l e sources o f q u a r t z f o r c e m e n t a t i o n in the M a g n u s f i e l d

In some quarters it is still fashionable to assume that silica is imported into reservoirs from an external source to supply the quartz cementation process (e.g. Gluyas & Coleman 1992). An alternative source of quartz cement is from

Oil leg ~" OWC .

.

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Water leg

Quartz % ~ Fig. 11. Distribution of quartz cement across the oilwater contact is unaffected by the relative timing of oil emplacement and quartz cementation. Assuming that quartz cementation is unaffected by oil emplacement.

within the reservoir itself. Possible internal sources postulated include pressure solution between quartz grains in the reservoir (e.g. Saigal et al. 1992), dissolution of quartz grains at stylolites (e.g. Oelkers et al. 1992; Walderhaug 1994) and dissolution of silicate sponge spicules (e.g. Vagle et al. 1994). If the source of quartz is external to the reservoir, then transport of silica into the reservoir must occur by advective processes (Worden et al. 1998). Advection of fluids in the subsurface is driven by fluid potential (in this case water potential, England et al. 1987). To setup a water potential difference between two points, requires a pressure gradient. The velocity of water flow (v) is given by Darcy's law (equation 6). v = (kr

(6)

Where kerr is the effective permeability of the rock, is the viscosity of the fluid and A P / L is the pressure gradient. Effective permeability can be defined as: keff---kr.k

(7)

Where kr is the relative permeability and k is the intrinsic permeability of the rock. Relative permeability reflects the permeability of a rock to two or more immiscible fluid phases (Archer & Wall 1994). Therefore, assuming a uniform pressure gradient, constant fluid viscosity and constant intrinsic permeability in the oil-filled sandstone and the aquifer, then the flow velocity of water into the reservoir is effectively controlled by the kr of the rock to water. Assuming quartz is transported as an aqueous complex, the relative velocity of transport of quartz into the

QUARTZ CEMENT VOLUMES ACROSS OIL-WATER CONTACTS \

~0.8oJ,d

~ 9

mm

Timing (relative to oil emplacement)

Wettability

Before

Reservoir contains only residual water (is water-we0

Kro

0.6-

337

Quartz cement distribution

(b) ~, 0.4-

Water-wet

During

0.2-

(c) Oil-wet

0 0

0.2

0.4

0.6

0.8

Water saturation (Sw) Water-wet

Fig. 12. Results of water flood tests on preserved Magnus core plugs, demonstrating that the relative permeability of the reservoir to water at low water saturations is very low.

After

(e) Oil-wet

reservoir versus the underlying aquifer is controlled by the kr of the reservoir to water. Relative permeability of reservoir sandstones to aqueous and non-aqueous fluids is usually assessed by the use of waterflood tests on core samples, the results being presented as a function of fractional water saturation (Archer & Wall 1994). Waterflood tests have been carried out on preserved core from Magnus (Gamble & Brooking 1989), and a representative example of the results obtained is shown in Fig. 12. At low values of water saturation (Sw) the relative permeability of the Magnus Sandstone Member to water (krw) is very low. This implies that when the reservoir contains oil, flow of water and influx of silica into the Magnus reservoir will be negligible. If we accept the assertion of Emery et al that oil emplacement and quartz cementation were synchronous, then this seems to rule out the possibility of the quartz cement in Magnus being externally sourced as the large volumes of fluid required to precipitate the amount of quartz cement observed could not have gained access to the reservoir. This conclusion is corroborated by the lack of correlation between the quartz cement and total quartz data (Figs 8 & 9) which showed that silica appeared not to have been imported into the sandstone. With an internal source of quartz in the reservoir, transport of silica in the reservoir probably occurs dominantly by diffusion (Worden et al. 1998). The diffusion rate of silica in solution is governed by Fick's law:

Fig. 13. Possible distributions of quartz cement across the oil-water contact assuming that the source for quartz cement is internal to the reservoir, showing the dependance on reservoir wettability during quartz cementation.

J =

D x (dc/dx). 0 / 0 2

(8)

Where J is the diffusional flux of quartz (i.e. the rate of diffusion of silica), D is the diffusion coefficient of silica, d c / d x is the concentration gradient, O is the porosity and 02. is the tortuosity. Water saturation (Sw) and wettability exert controls on the rate of the component processes involved in internally sourced quartz cementation by influencing the amount of the porosity available for diffusion (i.e. that part that is filled with water) and the tortuosity of the remaining water (Worden et al 1998). Quartz cementation will be least inhibited when water saturation is highest and less inhibited in waterwet than oil-wet reservoirs. Thus patterns of quartz cementation will likely be influenced by Sw and wettability of the reservoir at the time of quartz cementation. If the value of Sw is relatively high in a water wet reservoir, i.e. reservoir-wide Sw of 98% during the first stages of oil migration into a reservoir (England et al. 1987), then the transport, dissolution and precipitation rates of silica will

338

S.A. BARCLAY & R. H. WORDEN

be relatively unaffected and the volume of quartz cement above and below the OWC should be effectively identical. After oil filling, the Sw value is usually < 2025% (Hearst & Nelson 1985). In water wet reservoirs, this residual water exists in the form of grain-coating films. Transport of silica in the oil-leg will be adversely affected by the reduced water volume and the increased tortuosity of the water film so that the amount of quartz cement precipitated above the O W C should be recognizably reduced relative to the aquifer (all other things being equal). In oil wet reservoirs, the rate of silica diffusion and the access of the aqueous medium to the sites of would-be dissolution and precipitation should be radically reduced from the early stages of oil filling and should be effectively zero at maximum oil saturations. Oil wet reservoirs should present differences in quartz cement content about the O W C even at the earliest stages of oil emplacement for synchronous quartz cementation and oil filling. If we concur that oil emplacement and quartz cementation were synchronous in Magnus then we can also conclude that the quartz cementation must have occurred at the earliest stages of oil filling and that Magnus was water wet at the time of cementation. Any other scenario of wettability or Sw (i.e. exact timing of oil emplacement) would produce differences in quartz cement content not observed in the Magnus field.

Conclusions (1) The wireline (petrophysical) pseudo-quartz values correlate reasonably well with the point-count (petrographic) pseudo-quartz values. Both techniques can thus be used to examine the distribution of bulk quartz across the oil-water contact in the Magnus reservoir. (2) Neither the wireline pseudo-quartz nor the point-count pseudo-quartz values correlate with the point-count quartz cement values and are not good indicators of quartz cement values in the Magnus reservoir. (3) The lack of correlation of total quartz content and quartz cement implies that silica was not imported into the reservoir. (4) The point-count quartz cement, pointcount pseudo-quartz and the wireline pseudo-quartz appear not to change significantly across the oil-water contact in the Magnus Sandstone Member. (5) The fact that neither the point-count quartz cement, point-count pseudo-quartz and

wireline pseudo-quartz change significantly across the oil-water contact shows that quartz cementation in the Magnus reservoir was largely unaffected by the emplacement of oil. (6) The reported presence of oil-filled fluid inclusions in quartz cement and the reported simultaneous oil generation and quartz cementation suggest that quartz cementation occurred in the presence of oil. However, the lack of correlation between total quartz and quartz cement and the uniform quartz cement volumes in the oil and water legs seem to indicate that the reservoir was water-wet at the time of cementation and that the water saturation must still have been very high during quartz cementation and that the silica forming the cement was locally sourced. The authors would like to thank British Petroleum Ltd (and J. Rowse in particular) for supplying the petrographic data and the wireline log data, and also the two reviewers for their comments.

References ARCHER, J. S. & WALL, C. G. 1994. Petroleum engineering." principles and practice. Graham & Trotman, London. BARCLAY, S. A. 8,~ WORDEN, R. H. 1997. Reservoir wettability and its effect upon cementation in oil fields. In: HENDRY, J., CAREY, P., PARNELL, J., RUVVELL,A. & WORDEN, R. H. (eds) Geofluids H '97." Contributions to the Second International Conference on Fluid Evolution, Migration and Interaction in Sedimentary Basins and Orogenic Belts. Belfast, Northern Ireland, 10-14 March, 264-267. BJORLYKKE, K. & EGEBERG, P. K. 1993. Quartz cementation in sedimentary basins. American Association of Petroleum Geologists Bulletin, 77, 1538-1548. BROWN, C. E. & NEUSTADTER, E. L. 1980. The wettability of oil/water/silica systems with reference to oil recovery. Journal of Canadian Petroleum Technology, 19, 100-110. COSKUN, S. B., WARDLAW,N. C. & HAVERSLEW,B. 1993. Effects of composition, texture and diagenesis on porosity, permeability and oil recovery in a sandstone. Journal of Petroleum Science and Engineering, 8, 279-292. DE'ATH, N. G. & SCHUYLEMAN, S. F. 1981. The geology of the Magnus oilfield. In: ILLING,L. V. & HOBSON, G. D. (eds) Petroleum Geology of the Continental Shelf of North- West Europe. Heyden, London, 342=-351. DICKSON,J. A. D. 1965. A modified staining technique for carbonates in thin section. Nature, 205, 587. DOVETON, J. H. 1994. Geologic log analysis using computer methods, AAPG computer applications in

QUARTZ CEMENT VOLUMES ACROSS OIL-WATER CONTACTS

geology, 2, AAPG, Tulsa, USA. EMERY, D. MYERS, K. J. & YOUNG, R. 1990. Ancient subaerial exposure and freshwater leaching in sandstones. Geology, 18, 1178-1181. , SMALLEY, P. C. & OXTOBY, N. H. 1993. Synchronous oil migration and cementation in sandstone reservoirs demonstrated by quantitative description ofdiagenesis. Philosophical Transactions of the Royal Society of London, A344, 115125. ENGLAND, W. A., MACKENZIE,A. S., MANN, D. M. & QUIGLEY, Z. M. 1987. The movement and entrapment of petroleum fluids in the subsurface. Journal of the Geological Society, 144, 327-347. GAMBLE, I. J. A. & BROOKING,M. R. A. 1989. Magnus." wettability and reservoir condition waterflood study on wells 211~12a-M12 (A5) and 211/12a-M13 (C7). BP Research, Exploration and Production Division, Report No. RTB/CAU/44/89. GLASMANN, J. R., CLARK, R. A., LARTER, S. R., BRIEDIS, N. A. & LUNDEGARD, P. D. 1989. Diagenesis and hydrocarbon accumulation, Brent Sandstone (Jurassic), Bergen High Area, North Sea. Bulletin of the American Association of Petroleum Geologists, 73, 1341 1360. GLUVAS, J. G. & COLEMAN, M. L. 1992. Material flux and porosity changes during sandstone diagenesis. Nature, 356, 52-54. --, ROBINSON,A. G., EMERY,D., GRANT, S. M. & OXTOBY, N. H. 1993. The link between petroleum emplacement and sandstone cementation. In: PARKER, J. R. (ed.) Petroleum Geology of Northwest Europe." Proceedings of the 4th Conference. Geological Society, London, 1395-1402. HAYES, J. R. & KLUGMAN, M. A. 1959. Feldspar staining methods. Journal of Sedimentary Petrology, 29, 227-232. HEARST, J. R. & NELSON, P. H. 1985. Well logging for physical properties, McGraw-Hill, New York. HURST, A. & MILDOWSKI,A. 1996. Thorium distribution in some North Sea sandstones: implications for p e t r o p h y s i c a l e v a l u a t i o n . Petroleum Geoscience, 2, 59-68. LARTER, S. R. & APL1N, A. C. 1995. Reservoir geochemistry : methods, applications and opportunities. In: CUBITT,J. M. & ENGLAND,W. A. (eds) The geochemistry of reservoirs. Geological Society, Special Publications No. 86, 5-32. MACLEOD, G., PETCH, G. S., LARTER,S. R. & APLIN, A. C. 1993. Investigations on the composition of hydrocarbon fluid inclusions. 205th American Chemical Society Meeting, Abstracts, American Chemical Society.

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OELKERS, E. H., BJORKUM, P. A. & MURPHY, W. M. 1992. The mechanism of porosity reduction, stylolite development and quartz cementation in North Sea sandstones. In: KHARAKA, Y. K. ~; MAEST, A. S. (eds) Proceedings of the 7th International Symposium on Water-Rock Interaction. Park City, Utah, USA, 13-18 July, 11831186. QUIREIN, J. A., GARDNER, J. S. • WATSON, J. T. 1982. Combined natural gamma ray spectral lithodensity measurements applied to complex lithologies, SPE Paper 11143, 57th Annual Fall Meeting, New Orleans, 1-14. RIDER, M. H. 1986. The geological interpretation of well logs, Blackie, Glasgow. ROBINSON, A. & GLUYAS, J. 1992. Duration of quartz cementation in sandstones, North Sea and Haltenbank Basins. Marine and Petroleum Geology, 9, 324-327. SAIGAL, G. C., BJORLYKKE,K. ~; LARTER, S. 1992. The effects of oil emplacement on diagenetic processes--examples from the Fulmar reservoir sandstones, Central North Sea. Bulletin of the American Association of Petroleum Geologists, 76, 1024-1033. SAVRE, W. C. 1963. Determination of a more accurate porosity and mineral composition in complex lithologies with the use of sonic, neutron and density surveys. Journal of Petroleum Technology, 15, 945-959. SERRA, O., BALDWIN,J. & QUIREIN, J. 1980. Theory, interpretation and practical application of natural gamma ray spectoscopy. SPWLA Transactions of the 21st Annual Logging Symposium, Paper Q. SCHLANGEN,L. J. M., KOOPAL,L. K., COHEN STUART, M. A. ~ LYKLEMA, J. 1995. Thin hydrocarbon and water films on bare and methylated silica : vapour adsorption, wettability, adhesion and surface forces. Langmuir, 11, 1701-1710. VAGLE,G. B., HURST, A. & DYPVIK,H. 1994. Origin of quartz cements in some sandstones from the Jurassic of the Inner Moray Firth (UK). Sedimentology, 41, 363-377. WALDERHAUG, O. 1994. Temperatures of quartz cementation in Jurassic sandstones from the Norwegian continental shelf--evidence from fluid inclusions. Journal of Sedimentary Research, 64, 311-323. WORDEN, R. H., OXTOBY,N. H. & SMALLEY,P. C. 1998. Can oil emplacement prevent quartz cementation in sandstones. Petroleum Geoscience, 4, 129-138.

Ocean floor volcanism: constraints from the integration of core and downhole logging measurements T. S. B R E W E R 1, P. K. H A R V E Y l, M . A. L O V E L L 1, S. H A G G A S l, G. W I L L I A M S O N 1 & P. P E Z A R D 2

1Leicester University Borehole Research, Department of Geology, University of Leicester, Leicester, LE1 7RH, UK. 2 Laboratoire de Pdtrologie Magmatique, UPRES 6018, FacultO des Sciences et Techniques de Saint-Jdrome, Avenue Escadrille Normandie-Niemen, F-13397 Marseille Cedex 20, France

Abstract: The volcanic architecture of oceanic crust records the diversity in volcanic activity

during its development in the neovolcanic zone of individual ridge systems. Potentially there exists a spectrum of lithological architectures which may primarily be related to the spreading rate and the dynamics of individual magma chambers along different ridges. Recent studies have emphasized the observable spatial variations within different neovolcanic zones, although direct extrapolation into the third dimension can only be achieved by the use of drilling results. To study the structure of the volcanic layer it is essential that individual lithologies (sheet flows, pillow lavas and/or breccias) can be discriminated from the core and/or logging results and mapped within the borehole. Unfortunately a problem with the drilling of the volcanic basement during the Ocean Drilling Program has been the generally low (typically c. 25%) and biased core recoveries, which produce an erroneous picture of the lithological diversity of the volcanics. This problem is further compounded by the difficulty in determining the volcanic stratigraphy, particularly when the key information is lost during coring (i.e. boundaries/contacts). Downhole logging provides near continuous records of the physical/chemical properties of the borehole which when integrated with core measurements, yield a detailed picture of the architecture of the volcanic layer. Logging results from ODP Hole 896A are of sufficient quality that sheet flows, pillow lavas and brecciated units can be discriminated and mapped effectively within the borehole. From their distribution it is evident that sheet flows become more abundant in the lower part of the hole, which probably correlates with ridge axis volcanism whereas, the predominance of pillow lava flows (< 340 mbsf (metres below sea floor)) in the upper part of the hole, is probably related to off-axis volcanism within the neovolcanic zone.

Construction of the ocean crust is one of the most fundamental processes of the earth and has been operating for at least 2.0 Ga in its present form and probably in a similar or slightly modified form since the earliest history of the earth (Windley 1995). Until the mid 1970s the inaccessibility of ocean floor limited models of the structure of ocean crust to the classic layered stratigraphy (Fig. 1; Hill 1957; Raitt 1963), support for which was provided by extensive studies on ophiolite complexes. Recent studies have questioned the nature of the layer 2/3 boundary, suggesting this is a metamorphic transition and not a lithological b o u n d a r y (Detrick et al. 1994) and a further on-going debate is concerned with the architecture of the

volcanic layer (Smith & C a n n 1992). The stratigraphy of the volcanic pile is important since it is a function of: (a) the distribution of volcanism within the neovolcanic zone (rift valley); (b) how it controls fluid circulation and secondary alteration; (c) its influence on chemical fluxes during the evolution of the crust. Ocean drilling (DSDP and ODP) provides important information (core samples) in the construction of the volcanic stratigraphy, but a limitation is often imposed by the poor and biased core recovery which generally charac-

BREWER,T. S., HARVEY,P. K., LOVELL,M. A., HAGGAS,S., WILLIAMSON,G. 8z PEZARD,P. 1998. Ocean floor volcanism: constraints from the integration of core and downhole logging measurements In- HARVEY,P. K. d~;LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 341-362

341

342

T.S. BREWER E T AL.

Fig. 1. Typical layered model for normal ocean crust, modified from Wilson (1989) and Brown & Mussett (1981). For comparison the lithostratigraphies of ODP Holes 504B and 896A are shown, data from Alt et al. (1993). The position of the layer 2/3 boundary in hole 504B is after Detrick et al. (1994).

terizes basement holes (i.e. < 25%). Biasing of the core record toward the rheologically more competent units (e.g. pillow interiors and massive units) induces a pronounced systematic biasing in the recovered lithologies, which leads to large errors in the calculated core lithostratigraphy (Brewer et al. 1995; Harvey et al. 1995). In contrast, downhole logging results provide near continuous records of the physical and/or chemical properties of the borehole, which for some measurements are represented as images of the borehole walls (e.g. Formation Microscanner: FMS T M (Mark of Schlumberger)). By integrating the core and downhole logging measurements, potential exists to allow the 3 dimensional geometry of the crust to be explored and to evaluate how magmatic and secondary (alteration) processes relate to the observed lithological anisotropy. To demonstrate the potential of this approach, data have been used from ODP Hole 896A which penetrates c.290m of volcanic basement. An important issue prior to any integration of core and downhole measurements is an appreciation of the quality (precision and accuracy) and limitations of the different data-

sets. In integrating core and logging data an obvious feature is that the core and logging data are determined by techniques which have different precisions and accuracy. Even in the case where an individual core and log are recorded from the same apparent depth interval, there may be a considerable variation induced where recovery in the core barrel is < 100%, due to: (a) incorrect depth assignment of individual core pieces; (b) analysis of material by the logging tools which is not present in the core; (c) analysis of a different sample volumes.

Geological setting In the equatorial east Pacific, the Cocos-Nazca spreading centre consists of the Galapagos, Ecuador and Costa Rica Rifts. This rift system was initiated approximately 27 Ma ago, by the formation of the Galapagos Triple Junction (Hey et al. 1977; Lonsdale & Klitgord 1978), which produced a triangular wedge, the Gala-

OCEAN FLOOR VOLCANISM

343

Fig. 2. Location of ODP holes 504B and 896A in the eastern Equatorial Pacific, modified from Hobart et al. (1985). Insets shows the detailed location of Hole 896A, being situated on a bathymetric high (b) which overlies a basement high characterized by elevated heat flow (c). Insets from Langseth et al. (1988).

pagos Gore (Holden & Dietz 1972, Dick et al. 1992). The Costa Rica Rift is the easternmost of the three rift segments, and separates the Cocos and Nazca plates (Fig. 2); the rift system spreads asymmetrically at an intermediate rate (half rate of 3.6 cm yr -1 to the south and 3.0 cm yr -1 to the north). With the drilling of Ocean Drilling Program Hole 896A, two deep basement holes (Holes 504B and 896A) now penetrate oceanic crust formed at the Costa Rica Rift (Fig. 2). Hole 504B, the deepest basement hole in oceanic crust so far drilled, is located approximately 200 km to the south of the Costa Rica Rift, in 5.9 Ma old

crust. Hole 896A is located approximately 1 km to the south-east of Hole 504B in crust ~ 2 . 8 x 104 years older than at Hole 504B. Hole 896A is located on a bathymetric high overlying a basement topographic high (Fig. 2). No attempt was made to recover the sedimentary cover and the position of sediment/basement interface was based upon rubble being felt by the drill bit at 179 mbsf and the hole was cored from 195.1 mbsf to 469 mbsf (Alt et al. 1993). In the drilled section, core recovery averaged 26.9% and this was divided into pillow lavas (57%), massive flows (38%) and breccias (5%) and two small dykes, based on visual descriptions (Table

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1). The basalts are sparsely to highly phyric tholeiites, with plagioclase and olivine dominating the phenocryst assemblage, although between 353.1 and 392.1 mbsf clinopyroxene is present as a phenocryst phase (Alt et al. 1993). With the exception of pillow rims, the majority of the rocks are slightly altered (< 10%) and variably veined (Alt et al. 1993). Pervasive background reducing alteration coupled with saponite and minor pyrite replacement of olivine has led to the grey colour of the core. Oxidative alteration is manifested by dark grey to yellow and red alteration halos which commonly occur around smectite veins (Alt et al. 1993). Two types of breccia were recovered, hyaloclastic and matrix supported which comprised 5% of the core. Matrix supported breccias are lithologically diverse, ranging from angular clasts in intensely veined zones to angular to rounded clasts in a matrix of clays, carbonated and finely granulated basalt. The hyaloclastite breccias were often preserved on the outer edge of pillow rinds and comprise fragments of glass and devitrified glass in a matrix of clay + carbonate. In the deeper parts of the hole (below 364 mbsf) true hyaloclastite breccias are rare, although matrix supported breccias are present in both the pillow and massive lavas. Veins are very abundant throughout the drilled section (Alt et al. 1993; Laverne et al. 1996; Teagle et al. 1996) and vary in width from 0.1 to 2 mm, although thick (up to 8 mm) green saponite + carbonate veins are common in the pillow basalts (Teagle et al. 1996). In order of decreasing abundance the vein types are, green smectite, green smectite + carbonate, smectite + Fe(O,OH)x, smectite + Fe(O,OH)x + carbonate, carbonate and phillipsite (Teagle et al. 1996). Of these vein types carbonate veins are abundant in the upper part of the hole (< 300 mbsf) and also between 390 and 415 mbsf. Orientation of the various vein types is problematic, since many of the core pieces were small and so were not oriented with respect to the azimuth in the core barrel. Where the core was orientated by use of palaeomagnetic declination, it was evident that in the massive units at least one set of fractures are relatively steeply dipping, and probably represent cooling joints, whereas in the pillow lavas the distribution of fractures is more random and relates to radially orientated cooling joints (Dilek et al. 1996). As the veins are infilled with clay minerals (and other secondary minerals) which are electrically conductive relative to the basalts, this produces small scale resistivity contrasts within the rock mass. The scale of this contrast is controlled by the vein size (average c. 1 mm, Teagle et al. 1996), but is often sufficient

to be imaged by the resistivity measurements used to produce the FMS images. Thus, pillow lavas are characterized by anastomizing networks of fractures, whereas the massive units have a more ordered steeply dipping fracture network (Brewer et al. 1996).

Volcanology of the Costa Rica-Galapagos Rift In the late 1970s a series of submersible dives on the Galapagos Rift (Fig. 2) described the geometry, distribution and morphology of volcanism within the rift (Allmendinger & Fridtjof 1979; Ballard et al. 1979; van Andel & Ballard 1979), which is the western most of the three rift segments along the Cocos-Nazca spreading centre (Hobart et al. 1985). Due to the proximity and similar spreading rates, the Galapagos Rift is potentially a good analogue for the Costa Rica rift, and thus, its morphology and volcanology can be used as a first order model for comparative purposes. The Galapagos Rift is relatively simple, consisting of a rift valley approximately 3.5 km wide, with a mean depth of 2.5km (van Andel & Ballard 1979). The rift morphology is composed of small axial volcanoes with faulted marginal plateaus, bounded by steep inward facing fault scarps (Weiss et al. 1977; Ballard et al. 1979; van Andel & Ballard 1979). The dominant flow forms are pillow lavas and sheet flows, with the latter dominating the morphological types along the axial ridge (Ballard et al. 1979). An important morphological type particularly on the southern edge of the rift system are collapse and/or subsided flows (Ballard et al. 1979). Collapse related features in individual flows range from a few centimetres in size to large structures (pits) several tens of metres in width and up to a few metres deep (Ballard et al. 1979). The floors of such pits are often covered with rubble created by the collapse of the roof (Ballard et al. 1979), which could ultimately provide the framework for some of the breccias observed in Hole 896A. From the submersible information it is evident that the lithological architecture of the volcanic pile is strongly anisotropic both laterally and vertically (Ballard et al. 1979), but can be broadly divided into three major units, pillow lavas, sheet flows and brecciated (cemented collapse and/or subsided flows) units. The typical morphology and macropetrology (i.e. fracture pattern) of each major unit is summarized in Table 2; since these are the features which will be imaged by the logging tools.

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Table 1. Criteria used to identify lithological units during ODP Leg 148, after Alt, Kinoshita, Stokking et al. 1993. Massive Units

9 Lack of curved glassy or chilled margins. 9 Presence of well-developed brown oxidative alteration. 9 Generally high core reoveries. 9 Longer sections of continuous core than in pillowed units. 9 A more regular fracture pattern than the pillowed units. 9 MicrocrystaUine to fine grain size. Pillow Lavas

9 Curved or irregular chilled and/or glassy margin. 9 Interior has variolitic texture. 9 Poorly developed oxidative alteration. 9 Abundant fracturing and veining. 9 Veins thick > 1 mm. 9 Fine grain size. Breccias

9 Only recorded as a unit where two or three peices together in the core. 9 Matrix supported breccias lithologically variably which are cemented by clays+carbonate and finely comminuted basalt. 9 Haloclastic breccias often preserved on outer edge of chilled/glassy rinds and comprise fragments of glass and devitrified glass in a matrix of clay + carbonate.

Table 2. Macropetrological features of the volcanic lithologies in hole 896A which may be imaged by the diffrent logging tools. Data from Aft et al. (1993); Yamagishi (1985); Walker (1992). Pillow lavas

9 Pillow form, glassy rind and more massive interior. 9 Inter-pillow material. 9 Radial and concentric fractures, infilled by secondary minerals. 9 Variable porosity and permeability. Sheet Flows

9 Discrete units, with distinct boundaries. 9 Boundaries potential sites of intense secondary allteration. 9 Massive interiors, fine-grained to partially pillowed margins. 9 Rubbly bases and tops, which maybe transitional into breccia units. 9 Regular columnar cooling joints in massive interior. 9 Random cooling joints near to margins. 9 Relatively impermeable units. Breccias

9 Rapid variations in compositions. 9 Variable grain size. 9 Transitional boundaries with pillow lavas and/or sheet flows. 9 Individual clasts may contact fractures. 9 Breccia cements composed of minerals with different physical and chemical properites to the basalts. 9 Variable porosity and permeability structures. Initially some units very high (c. 40%, Teagle et al. 1996).

Downhole logging:

D u r i n g the drilling o f H o l e 896A there were two i n d e p e n d e n t p h a s e s o f logging (Fig. 3). P h a s e 1 involved the d e p l o y m e n t o f the . i n d u c e d g a m m a

ray s p e c t r o s c o p y tool, a l u m i n i u m a c t i v a t i o n clay tool, n a t u r a l g a m m a s p e c t r o s c o p y tool ( f o r m i n g the S c h l u m b e r g e r g e o c h e m i c a l l o g g i n g tool; G L T ) a n d a t e m p e r a t u r e tool, w h i c h r e c o r d e d i n f o r m a t i o n f r o m 120.73 to 347 mbsf. It is

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Fig. 3. Core based lithostratigraphy and core recovery in Hole 896A; data from Alt et al. (1993). Also shown are the depth intervals over which wireline log measurements were obtained. FMSTM: Formation Microscanner; NGT: Natural gamma ray tool; DLL: Dual laterolog (resistivity) tool; GLTaM: Geochemical Logging Tool. TMMark of Schlumberger. important to note that logging results were obtained in the cased section of the hole from 120.73 to 191.46 mbsf as well as the open hole, which potentially provides data to constrain the depth of the b a s e m e n t - s e d i m e n t interface (Brewer et al. 1995). The hole was then deepened to 469 mbsf, which was followed by a second logging phase when three separate logging tools were deployed. The first string contained the dual laterolog, sonic, density and the natural gamma tools and was successfully deployed from 117.28 to 429 mbsf. Following a packer experiment, the magnetometer was then deployed and recorded information from 208 to 438 mbsf. The final logging run deployed the Formation Microscanner (FMS) which recorded data to 423 mbsf.

Logging tools and data The ODP routinely log drilled holes using a range of downhole measurements. These logging

runs are capable of measuring both physical and chemical properties, and can produce downhole images of the borehole wall. Full descriptions of the measurement devices (termed logging tools or tools) used by the ODP are available in the literature (e.g. Davies et al. 1991, Explanatory Notes pp 52-56; Alt et al. 1993, Explanatory Notes pp 21-23). The logging tools we consider in this paper are briefly described below. The natural radioactivity of the formation is measured with a natural gamma ray spectroscopy tool (NGT). The primary sources of natural gamma rays are the radioactive isotope of potassium (40K), and the isotopes of the uranium and thorium decay series with the majority (90% of the measured signal) originating close (< 15 cm) to the borehole wall. In this paper we use the contribution from 4~ as an indicator of alteration based on its solubility and ease of transport. The inclusion of the N G T on each logging run enables the use of the N G T response to ensure consistent depth matching between separate runs of different tools and multiple runs of the same tool at different times. The vertical resolution of the tool is approximately 0.3m (Theys 1991). Hurst (1990) states that the accuracy of the elemental measurement compares favourably with that by chemical determination (i.e. neutron activation). The estimate is, however, dependent on the thickness of the bed and the counting statistics of the measurement; this latter category includes the speed at which the tool is run and the radioactivity of the formation which is itself a random process. The Formation Microscanner is a microelectrical imaging device with four arms which are opened downhole and force four pads against the borehole wall. Each of these pads contains two overlapping rows of 8 buttons, with the buttons mounted against a background electrode or pad face. The buttons, pad face and lower part of the tool are held at a constant potential which passively focuses the current and forces it into the formation. The current flows a finite distance into the formation before reaching the return electrode higher up the tool. The depth of investigation, however, is deemed to be very shallow and the measurement is thus controlled primarily by the near surface features of the formation. The current flowing through each button is monitored and later converted to resistance values enabling an electrical image of the borehole wall to be created. In addition, the tool includes inclinometry and accelerometry measurements to enable its orientation and speed to be measured. Speed corrections are necessary to ensure accurate portrayal of the

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Fig. 4. (a) Morphology of Hole 896A, expressed as hole size deviation determined from the FMS calipers. The hole size deviation is the amount by which the borehole is enlarged beyond the diameter on the drill bit. The FMS has four calipers, arranged at right angles, and in the plots shown here, the two curves, plotted 'away' from the centre of the borehole, map the difference of the means of the two opposing pairs of calipers on the FMS, from the 'true' borehole diameter. Break-outs and enlargement of the borehole only becomes critical when the deviation is > 5 cm, and as such is limited to small discrete zones. (b) An illustration of the stand-off effect in the measurement of alumina by the ACT TM(Activation Clay Tool) over a breakout at about 240 mbsf. a-~Mark of Schlumberger.

resistance images, whilst inclinometry provides reference for orientation of the images. In addition, the four orthogonal arms provide two caliper measurements of the borehole diameter. A detailed description of the principles and limitations of the FMS tool as well as documentation of its varied uses within O D P is given by Lovell et al. (1998). In the ocean crust environment the electrical resistivity of most rocks depends on both the amount of pore space and the proportion of clays present, with electrical resistivity decreasing as both porosity and clay content increase. The only exceptions to this general trend are limited to the presence of electronic conductors such as pyrite which effectively short circuit the electrical paths through the pore space (Lovell 1985; Pezard et al. 1988; Lovell & Pezard 1990). The FMS thus provides visual images of textural variations at the borehole wall due to changes in electrical resistivity resulting from porosity and grain size variations. It also provides two perpendicular borehole diameter measurements. The Aluminium Clay Tool (ACT) forms part of Schlumberger's geochemical combination. The ACT uses neutron-induced gamma ray

spectroscopy with a 252Cf source, to determine the abundance of aluminium. This tool, however, is very sensitive to variations in the distance of stand-off from the borehole wall. Here we use the A C T response in conjunction with caliper observations (see above) to mark zones of borehole enlargement where log quality is likely to be poor. The Dual Laterolog uses focused current techniques to provide two separate measurements of the electrical resistivity of the formation. These are denoted as LLd for the 'deep' and LLs for the 'shallow' measurement, respectively, measured in terms of penetration into the formation away from the borehole. The tool operates well in boreholes with a conductive fluid and high resistivity crystalline rocks over the resistivity range 0.1 to 40000 o h m m (Tittman 1986) with a vertical resolution of 0.61m (Theys 1991). Typically in igneous ocean crust, the electrical resistivity of the rock will be high except where there is extensive fracturing. Comparison of the two distinct measurements involved in the Dual Laterolog can examine the extent of fracturing around the borehole wall The array sonic tool (SDT) uses two acoustic

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Fig. 5. Core recovery and percentage error in core location within Hole 896A.

transmitters and a series of receivers to measure the travel time of sonic waves close to the borehole wall; the exact travel path is partly determined by the source-receiver separation and may penetrate further form the borehole with increasing source-receiver separation if the velocity increases in depth away from the borehole. Here we use the travel times for the compressional wave, converted to apparent velocities. The vertical resolution of the sonic tool is similar to the Dual Laterolog (Theys 1991). The nature and quality of the responses from logging tools is strongly influenced by the morphology of the borehole, such that where the hole is oversized or undersized, anomalous results may be obtained from individual tools. The FMS has two orthogonal calipers which record the borehole size and ellipticity (Bell 1990; Evans & Brereton 1990). The largest section of oversizing (Fig. 4a) occurs at the top of the cored section directly beneath the casing, reflecting oversizing produced during the setting of the casing. The remainder of the hole is generally uniform in size, although there are several break-outs within the borehole (Fig. 4a). These break-outs occur over relatively small intervals (c. 2 m) and so only a small amount of logging data have been lost due to the stand-off effect. Individual tools are affected to different degrees by stand-off, but the ACT is particularly sensitive to this effect and as such can be used as a monitor of tool performance (Bristow &

deMenocal 1992). In Hole 896A the A1 yields from the ACT rapidly fall in the break-outs, but in the remainder of the hole the small scale relief of the borehole does not significantly affect this tools performance (Fig. 4b). This would suggest that extreme caution be placed on any interpretation of the logging results in the identified break-outs (Fig. 4a), whereas, in the remainder of the logged section the hole size does not affect the quality of the logging results. This behaviour of the ACT has been used in the adjacent Hole 504B to detect borehole oversizing in the absence of a caliper log (Harvey et al. 1995).

Error estimation Any reconstruction of the volcanic stratigraphy which uses both core and log must first establish the quality of the different datasets. In dealing with the core, errors arise from inaccurate location (i.e. metres below sea floor; mbsf) and misclassification (e.g. pillow or flow). Errors in the logging data relate to inaccurate location (mbsf), the precision and accuracy of the different tools and the interpretation of the data. Inaccurate depth location of the core, must as first order value be no better than the average recovery of 26.9%, which represents a maximum location error of +73.1% in the cored section. Clearly such a value does not account for the variations in recovery for individual core barrels (Fig. 3) and as such a moving average based on actual core barrel recoveries is a more appro-

OCEAN FLOOR VOLCANISM priate estimation of the location error (Agrinier & Agrinier 1994). By using moving average Values, the location errors range from 4-99% to +28% (Fig. 5). The second type of error for core data relates to the lithological classification of individual core pieces as either pillows, flows or breccias. The classification scheme used for Hole 896A is presented in Table 1, and it should be noted that breccias were only given a unit status if two or more pieces were present in a core. Clearly this classification scheme is both user sensitive and reliant on the recovery of key features (e.g. glassy curved margins for pillows), which makes it a non-robust method, with potentially large errors which cannot be estimated with any degree of certainty. Potential sources of errors in the logging data are: (a) precision and accuracy of the measurement; (b) depth location; (c) vertical coverage of the logs (e.g. loss of results in break-outs); (d) for FMS images the area of the borehole imaged; (e) actual positioning of lithological boundaries. An estimation of the location error for any log can be derived simply by determining the amount of missing or unuseable data due to hole oversizing (break-outs). In Hole 896A, over the entire logged section the location error is +9%. However, this is a global error and a more realistic value is represented by a moving average, where in the break-outs the error is +100%, while in the remainder of the hole the location error is small if depth shifting has been correctly applied. Depth shifting is carried out during shore-based processing where all of the logs are systematically shifted by reference to the NGT, which is deployed with each logging suite. The NGT curves from each logging run are then depth shifted so that curve shapes closely correspond (Williamson pers. comm. 1996), which then minimizes the location errors which may have arisen during different tool deployment (e.g. cable stretching). Measurement errors associated with individual tools can be estimated by either reference to published values (Theys 1991) or by use of multiple tool passes within the same borehole. ODP Hole 504B provides an excellent example for calibration of logging results, since this hole has been logged during several drilling legs (Alt e t al. 1993). Also, during Leg 140 multiple

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Fig. 6. (a) Repeat runs of the DLL within hole 504B, data from Dick et al. (1992). (b) Comparison of LLs data over the same depth interval in hole 504B using data from Legs 140 (Dick et al. 1992) and 148 (Alt et al. 1993). (c) The same depth interval with depth shifting applied.

logging records were recorded for the lower section of the hole (Dick e t al. 1992). Results of two passes for the dual lateral resistivity tool from Leg 140 are shown in Fig. 6a. It is clear that these results are extremely similar and indicate that the reproducibility (precision) of this tool is extremely good (i.e. a small measurement error). A comparison of the LLs data from Legs 140 and 148 across flow unit 2d is shown in Fig. 6b, where it is clear that the

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magnitude of the measurements recorded during each run are very similar, but a major discrepancy exists in the depths of the two measurements (a location error of c. 5 m). This location error probably reflects the difference in depth shifting applied during shore-based processing, but if the Leg 148 results are shifted so that the two highest values from unit 2d (Fig. 6c) coincide then the two datasets are very similar and again demonstrate the excellent precision of the resistivity measurements. These results illustrate the potential that exists for using logging data from different legs to interpret the structure and evolution of ocean crust, but it is important to evaluate the location and measurement errors in order to minimize problems in interpretation. A semi-quantitative estimation of the measurement error associated with the FMS data is possible from Hole 896A, since two passes were recorded over a large section of the hole. The images from both passes can be visually compared and it is evident that the lithological boundaries (e.g. sheet flow/pillows) are similarly imaged on each pass (Brewer et al. 1995), although there is a small depth ( < lm) variation in the actual location, which probably relates to errors associated with depth shifting. Another problem with the FMS is that at best only c. 20% of the borehole wall is covered and that the orientation of the feature controls the probability of it being imaged. Thus, sub-horizontal to inclined structures have a greater likelihood of being imaged compared to near vertical structures, due to there greater probability of intersecting an FMS pad within the borehole. In Hole 896A, the lithological boundaries appear to have sub-horizontal orientations which results in similar imaging during each pass, whereas some high angled vein networks may not be equally imaged on each run. However, from the compilation of the quality of the logging data it is clear that individual tools have different precision and accuracy, but the poorest measurement errors are c. 10% (Theys 1991), which is considerably better than the location errors associated with the recovered core (Fig. 6a).

Ocean basement structure, the logging perspective The porosity and permeability structure of ocean crust are important properties in understanding fluid-rock interactions, distribution and style of secondary low temperature alteration and chemical fluxes between the lithosphere

and hydrosphere. Estimation of apparent porosity within ocean crust can be derived from resistivity measurements recorded with the longspacing resistivity device (Becker 1985) or by the dual laterolog (Pezard 1990). The apparent porosity is a computed value which represents both electrolytic conduction processes in pores, fractures, cracks (including microcracks) and surface conduction mechanisms; the latter are particularly important when conductive alteration minerals (e.g. smectites) are present (Pezard 1990). From the dual laterolog it is also possible to record the average fracturing around the borehole (Boyeldieu & Martin 1984; Sibbit & Faivre 1985; Pezard & Anderson 1990). In ODP holes, sea water is both the drilling fluid and the pore fluid in the rock mass. Consequently, the difference between the deep (LLd) and shallow (LLs) resistivity may be related to rock anisotropy (Pezard & Anderson 1990). Moreover, the difference between the LLd and LLs can be used to compute the fracture porosity, which relates to the relative volume of organized conductive features (i.e. fractures) in the vicinity of the borehole. Early attempts at evaluating fracture intensity in the ocean crust were performed by eye from borehole televiewer (BHTV) images (Newmark et al. 1985) or by direct measurements on the recovered core. Visual fracture analyses of the BHTV images are often extremely inaccurate due to blurring and distortion of the images produced by ship heave, hole ellipticity and tool stand-off. Fracture analysis of actual core is a function of the core recovery and is open to biasing in the drilling of specific rock masses. In the case of the ODP, basement recoveries are often low (< 20%) and any recovered core is often biased towards more massive less fractured materials (i.e. pillow cores, massive flows). The relationship between porosity and resistivity for ocean floor basalts has been extensively discussed by Becker (1985) and Lovell & Pezard (1990), who concluded that the use of Archie~)s formula with a = 1.0 and m = 2.0 approximates the relationship between porosity and resistivity. However, a different law with a = 10 and m close to 1.0 has been suggested for ocean floor basalts (Flovenz et al. 1985; Pezard 1990; Broglia & Moos 1998), which reflects electrolytic conduction in elongated volumes filled with pore fluid (sea water). From the more general derivation of Archie's Law, Pezard (1990) estimated the apparent porosity (RPHI), assuming the LLd measurement represents the true resistivity of the ocean crust, from the following: RPHI2 = R w / R L L d ,

OCEAN FLOOR VOLCANISM where RLLd is the resistivity value from the deep laterolog and Rw is the pore fluid resistivity (sea water). An estimation of the fracture porosity is derived from the difference between the LLd and LLs measurements (Pezard & Anderson 1990). For ODP holes where sea water is used as the drilling fluid, salinity invasion can be ignored since the pore and borehole fluids have the same salinities (Mottl et al. 1983). Thus, the difference between the LLd and LLs can be related to the anisotropy of the pore space within the rock as imaged by the two different depths of current penetration. Furthermore, any effects produced by drilling induced fractures close to the borehole walls should not account for more than a few percent of the difference between the LLd and LLs. Pezard (1990) demonstrated that the LLd measurement mostly represents the horizontal resistivity of the rock mass and is therefore hardly affected by the presence of vertical conductive features (i.e. fractures). In contrast, the shallow LLs measurement is sensitive to both the horizontal and vertical resistivity of the rock mass and so is reduced by the occurrence of vertical and horizontal fractures. The net result of these two effects is that sub-horizontal conductive features (i.e. fractures) p r e f e r e n t i a l l y decreases the LLd ( L L d - L L s >0), whereas sub-vertical conductive features reduce the LLs measurement ( L L d - LLs _<0). Identification of specific volcanic units (e.g. pillows and flows) using resistivity logs, has been documented from several studies (Pezard 1990; Gable et al. 1989; Hyndman & Salisbury 1983), in each of which massive units (flows) were identified by elevated resistivity values relative to the remainder of the volcanics. However, little if any attempt has been made to further subdivide the structure of the volcanic layer and usually all units not identified as massive units (flows) are grouped under the heading of pillow lavas. FMS images may be viewed as apparent resistivity maps of segments of the borehole wall and as such allow for the potential discrimination of different lithological types (Lovell et al. 1998). To date, mapping of lithological types from ODP basement holes based upon FMS images is relatively restricted (Langseth & Becker 1994; Brewer et al. 1996), although in both of these studies a more detailed picture of the basement was produced relative to the core descriptions. Several studies have attempted to use results of the geochemical logging tool (GLT) to discriminate the structure of the volcanic layer (Anderson et al. 1990a,b; Brewer et al. 1990,

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1992; Pelling et al. 1991). The majority of these studies divided the drilled section into large 'units' which were characterized by similar chemical parameters. Brewer et al. (1992) suggested that the NGT derived K values may be used to discriminate pillow lavas (higher K) from massive units (flows, low K). The high K concentration in the pillow lavas reflected the greater abundance of secondary minerals (Kbearing) which developed due to the more permeable nature of the pillow lavas, the more intense fluid-rock interactions and the enhanced mobility of potassium. High quality logging data provide a near continuous detailed record of the physical and/ or chemical properties of the borehole wall. If the logging data can then be reconciled with the core derived observations and measurements, an integrated dataset can be produced, which should provide very important information for the study of the ocean crust. However, it is clear that the logging results must be interpreted in such a way to provide maximum geological information. In the following sections a series of interpretations are presented to illustrate the potential of this method in understanding the lithological architecture of the ocean crust in ODP Hole 896A. Volcanic structure of Hole 896A Visual core descriptions identified massive units (flows) and pillow lavas as the main lithologies in Hole 896A (Fig. 3), with minor breccias (Alt et al. 1993). However, the actual proportions of lithologies present is dependant on the core recovery and the robustness of the identification/ discriminant criteria. The generally low core recoveries (Fig. 3) together with the absence of many of the key discriminant criteria (Table 1) on individual core pieces places severe limitations on this interpretation. In the following discussion each of the lithological types (pillows, flows, breccias) are treated separately to illustrate the types of information which can be derived from the logging data, which is then synthesized to developed a model for the lithological architecture of the volcanics in this hole.

Flows Core recovery in the flows was moderately high and individual core pieces tended to be long compared to recovery in pillow lavas and/or breccias (up to 20cm; Alt et al. 1993). This enhanced and more continuous core recovery probably reflects the massive nature of the sheet

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Fig. 7. Cross plot of sonic velocity (Vp) versus resistivity (LLs) which illustrates the potential for discriminating between the different volcanic lithologies in Hole 896A.

flows; while fracturing is common within the flows its intensity and spacing is such that it does not dominate the overall rheology of the material. Flows have the highest resistivity values within Hole 896A, with values > 10 f~ m, which allows for their rapid identification (Figs 7 & 8a). Similar relatively high resistivity values have been previously used to identify lava flows in ODP hole 504B (Pezard 1990) and DSDP hole 395A (Hyndman & Salisbury 1983). Such high resistivity values result from the massive crystalline nature, the low permeability and poorly conductive nature of the basalt. The flows also have the highest and most restricted range of sonic velocities within the hole (Figs 7 & 8c), reflecting their massive nature and more uniform structure. FMS images of individual flows are all characterized by relatively large areas of uniform visual textures, which are bounded by thin discrete high conductive zones (Fig. 9). This thin conductive zone represents the more highly altered material at the flow margins (i.e. intense development of alteration minerals). The resistivity and sonic logs can be used to rapidly identify individual flows. However, subsequent analysis of these and other logs can be used to further define the detailed morphology and structure of individual flows. In Fig. 8a, a flow is identifiable from its elevated resistivity

signal, but there is also a systematic change in resistivity and in the relationship between the deep (LLd) and shallow (LLs) logs within this flow (Fig. 8a & 8b). The sigmoidal form to the resistivity curves at c. 233 mbsf (Fig. 7a) reflects the coarse grained interior of the flow (Pezard & Anderson 1990), while the sharp break at 232 mbsf, followed by a zone of near constant resistivity (232-230 mbsf) indicates a change in the morphology in the upper part of the flow. Part of this change is governed by fracture orientations, which can be tested by comparison of the LLd and LLs logs; this relationship is displayed as the value (LLd - L L s ) , such that when sub-vertical fractures dominate positive values (i.e. LLd > LLs) are recorded, whereas, when sub-horizontal fractures predominate negative values (i.e. LLd < LLs) result. The flow margins are characterized by sub-horizontal fractures, while in the coarse grain interior, sub-vertical fractures (possible columnar cooling joints) are the dominant type. Confirmation of this fracture distribution is provided by the FMS images from which the intensity and orientations of the fractures within the borehole wall can be estimated (Fig. 9). Also, since all the imaged fractures appear to have lower resistivity values, then these must either represent open sea water filled fractures or filled fractures containing conductive minerals (e.g. clays). Core descriptions for the flows (massive units) identified that all fractures are filled, containing clay minerals, saponite and/or carbonate (Teagle et al. 1996). This would suggest that all of the image fractures are infilled with these conductive minerals. By integrating the fracture geometry and distribution with information from the other logs, a model of the flow can be constructed (Fig. 9), where the flow probably has an altered 'rubbly base', a massive interior and an upper zone which is partly pillowed and is 'rubbly'. Potassium is particularly useful in determining the effects of alteration in oceanic floor volcanics (Brewer et al. 1992; Kempton et al. 1985), since this element is very mobile in aqueous solutions and is significantly enriched in several of the secondary alteration minerals (Table 3). A continuous measurement of the K concentration in Hole 896A is provided by the spectral gamma log, which when viewed on a relatively small scale provides important information. An example is shown in Fig. 8d, where the lowest K concentrations correlate with the flows, whereas, the pillow lavas and breccias have higher and more variable K values. The low K values (typically similar to those of fresh basalts in Hole 896A, Brewer et al. 1996) in the sheet flow

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Fig. 8. Wireline logging responses over a single sheet flow located between 230 and 236 mbsf. Divisions within the flow, whose position is indicated by the vertical grey bars on the lithological columns in the centre of the diagram, A, B and C, are described in Fig. 9. Above the flow are brecciated pillow lavas (BPL), with pillow lavas (PL) beneath. For an explanation of hole size deviation see the caption to Fig. 4.

reflect the more massive, less altered (and veined) nature of the flows. Due to their more massive nature, the flows would probably act as barriers to fluid flow, so limiting their internal alteration and preserving their original low potassium values. This correlation of lower K in the flows is developed throughout the log section, which suggests that as well as acting as barriers to fluid, they also served to focus flow in the more porous and permeable breccias and pillow lavas. Evidence for the focusing of fluid flow comes from the more altered basalts recovered in these zones, which are characterized

by high and variable K values, and also that some of the highest K values are developed both below and above individual flows (Fig. 8d), which probably represent the zones of maximum fluid interactions (Brewer et al. 1992).

Breccias A variety of breccias were recovered from Hole 896A (Alt, et al. 1993, Harper & Tartarotti 1996). However, the amount of actual recovered material was limited (c. 5%) and breccia units were only recorded when two or more pieces of

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Fig. 9. FMS image across a sheet lava flow. The base of the flow is a little below 235 mbsf, and marked by a low resistance zone. The flow has a fractured base about 140 cm thick, above which is some two metres of relatively unfractured rock, representing the central part of the flow. The top of the flow is fractured and pillowed and extends up to 230 mbsf. Resistivity scale: pale (resistive) to dark (conductive).

breccia occurred consecutively in a core barrel (Alt, et al. 1993). Consequently, the amount of breccia identified in the lithological log was low (5%), but this biasing is further enhanced by the probable destruction of breccias during the drilling process. The proportion and distribution of breccias in the hole is particularly important since such units may: (a) control fluid flow and therefore the distribution of secondary alteration in the volcanic section (Teagle et al. 1996); (b) record different processes within the volca-

nic succession (e.g. mass wastage and/or lava fragmentation). Breccias are composed of variably sized clasts cemented by a matrix that can represent up to 40% of the rock (Teagle et al. 1996). In Hole 896A, clasts are composed of fresh to altered basalt and or basaltic glass which is in a matrix composed of smectites 4- carbonate + saponite :k:Fe(O,OH)x (Alt et al. 1993, Harper & Tartarotti 1996; Teagle et al. 1996). Furthermore, breccias contain the most altered basaltic compositions recovered from Hole 896A (Teagle

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Fig. 10. Downhole logging responses from a breccia unit located at 350-370 mbsf. Two metre long FMS images covering part of the section are shown, correctly scaled in the horizontal and vertical directions, as (f) (360-362 mbsf) and (g) (366-368 mbsf). Large unfractured blocks within the breccia are outlined in (f) and (g), and demonstrate the chaotic nature of this lithology. et al. 1996) which illustrates their importance in

understanding secondary alteration processes and chemical fluxes in this segment of crust. The small scale lithological variations of the breccias results in rapidly changing physical and chemical properties which are recorded by the following log responses (Figs 7 & 10):

(a) low resistivity values; (b) a serrated form to the sonic log and spectral gamma logs; (c) microrelief on the caliper log; (e) mottled FMS images. The low resistivity values of the breccias (c.

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5 ~ m, Figs 7 & 10) reflect the matrix material, which is composed of conductive minerals. Highly variable LLd and LLs logs (Fig. 10a,b) may reflect measurements recorded in larger clasts compared to those in matrix. FMS images of the breccias are characterized by mottled images (Fig. 10) which are very diagnostic and allow discrimination from the pillows and flows, which is not always possible from the other logging data. FMS images also allows the 'mapping' of large discrete clasts, in sufficient detail to establish whether the clast is veined or unveined. In the flows, fracture orientation was determined from the relationship (LLd -LLs), but in the breccias this value normally close to zero (i.e. LLd=LLs), suggesting that neither sub-horizontal or sub-vertical structures dominate, which can be partly calibrated from the FMS images. However, this relationship is complicated since what is recorded is the orientation of the boundaries between the clasts and the matrix. From the core material it can be seen that the distribution of such boundaries is random and no one particular direction dominates. This relationship does however help to distinguish the breccias from the flows. K values are highly variable and have relatively large ranges in individual breccia units (Fig. 10d). These features reflect the heterogeneous distribution of the high-K matrix minerals (Table 3) and the lower K of the unaltered basaltic clasts (Table 3). Finally, the microrelief on the caliper log with occasional break-outs and hole oversizing (Fig. 10e) results from the pudding stone macropetrology of such rocks, where individual clasts and/or matrix may be plucked out during drilling or later cave into the hole. This contrasts with the generally smooth caliper logs (Fig. 8e) in the massive and more rheological competent flOWS.

Pillow lavas Pillow lavas comprise c. 58% of the core lithological log and were identified using the criteria in Table 1, with individual pillows ranging in thickness from 50 to 280 mm and possibly up to 350mm (Alt et al. 1993). In Hole 896A the major problems in identify pillow lavas from the core were: (a) the lack of rims, such that the cores of large pillows may have been classified as massive units (Alt et al. 1993); (b) a curved to planar or irregularly chilled margin unique to a pillow lava; (c) core recovery in the intervals classified as pillow lavas was poor, which limits any

spatial resolution. This then makes the distinction between coarse grained haloclastite breccias and pillows particularly difficult; (d) Submersible information suggests that pillow lavas are not always the dominant morphological flow type developed along the Galapagos rift (Ballard et al. 1979). Logging results may provide an answer to some of these problems and the following features summarise the different tool responses in pillow lavas (Figs 7 & 11): (a) variable relief on the caliper log; (b) a rapidly changing (< 1 m) resistivity log containing both relatively high (> 10 ~2m) and low (< 10 ~ m) values; (c) variable fracture orientations identified by the LLd and LLs relationship; (d) FMS images characterized by regions of relatively uniform resistivity (pillows) separated by curved surfaces. Inter-pillow material is more conductive than the pillows. Randomly orientated fractures on individual images; (e) variable amplitude and wavelength variations in the sonic log, with overall values transitional between the breccias and sheet flows (Figs 7 & 11); (f) rapidly changing potassium concentrations. Although some of the short wavelength variations in the pillow lavas are similar to those described from the breccias, the generally higher resistivity and sonic values (Figs 7 & 11) and the different FMS images allow discrimination of pillows and breccias. The major differences between the pillows and flows are the FMS images (Fig. 1lf) and the serrated resistivity and sonic logs of pillows compared to the more uniform logs in the sheet flows. The lithological heterogeneity within a pillow lava (pillow or inter-pillow) is what controls the variation in the sonic, resistivity and potassium concentrations (Fig. 11). This heterogeneity is primarily a function of the origin of the pillows such that an individual pillow contains a core of massive basalt or basaltic glass which is more resistive and has moderate seismic velocities compared to the inter-pillow material. In contrast, pillow rims and, in particular the more intensely altered inter-pillow regions, are more conductive (lower resistivity) and have lower seismic velocities due to the abundance of secondary alteration minerals (Fig. 7). The variation in potassium is slightly more complicated since this element is redistributed during

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Fig. 11. Downhole logging responses for pillow lavas located between 290 and 300 mbsf. A 4m FMS image, correctly scaled in the horizontal and vertical directions, is shown as (f) (294-298 mbsf). Several of the rounded surfaces between the pillows are picked out in (f--solid lines), as are some irregular fractures (dotted).

alteration and is often concentrated in vein minerals (Laverne et al. 1996; Teagle et al. 1996). Low potassium values correlate with the interior of unveined pillows, whereas, the higher values record more altered and veined material. There-

fore, the overall form of the potassium log within any one pillow lava will reflect the intensity of veining, the degree of alteration and the size and shape of distributions of the individual pillows. The relief of the caliper log

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Fig. 12. Comparison of core recovery, core and log derived lithostratigraphies (left). Also shown is the variation in TiO2, K20 and Ni in core samples over the same depth interval (right). The elemental data were measured by X-ray fluorescence spectrometry.

(Fig. 1 le) reflects the differing rheology of the pillows (cores and rims) and the inter-pillow material, such that oversizing results from such factors as: (a) plucking of small pillows during drilling; (b) wash-outs of the inter-pillow material by the drilling fluid; (c) post-drilling collapse due to the differing strengths of the pillow and inter-pillow material; and/or (d) numerous fractures and planar discontinuities intersecting the borehole wall leading to wedge and, or block failure. FMS images (Fig. l lf) of pillow lavas are particularly diagnostic (i.e. curved boundaries to individual pillows) and have previously been used to identify pillow lavas sequences in basement holes with poor recoveries (Langseth & Becker 1994). Also evident on the FMS images are anatomizing networks of infilled fractures in individual pillows (more conductive features; Fig. 1lf). These fractures are probably original cooling features related to eruption which explains their general random form. From the resistivity logs (Fig. l lb) there is no obvious preferred fracture direction as recorded by the rapid variation in the value (LLd - L L s ) .

However, in the majority of the pillow lavas in general L L s > L L d , which is probably not related to the fracture directions, but is more likely to reflect the general sub-horizontal orientation to the stacking of the pillow lavas.

Lithological diversity in Hole 896A By combining all of the different log responses, a log derived lithostratigraphy is constructed for the upper c. 220 m of Hole 896A (Fig. 12). In this model the proportions of the three different lithologies are pillow lavas 31%, sheet flows 23% and breccias 46% which contrasts strongly with the core derived data (pillow lavas 57%, flows (massive units) 38%, breccias 5%). At present there is no attempt to further subdivide into specific types as described from the core material (jigsaw puzzle, sedimentary, tectonic, haloclastite; H a r p e r & T a r t a r o t t i 1996), although work in progress suggests that this subdivision may be possible (S. Haggas, pers. comm. 1997). This log based lithostratigraphy is clearly different to that derived from the core descriptions (Fig. 12) and the proportions of individual lithologies are quite different, with the most obvious being the increased proportion of breccias in the logging results. This variation in the proportion of rocks is probably the result of

OCEAN FLOOR VOLCANISM the following: (a) poor core recoveries in many sections of the hole; (b) non-robust nature of hand specimen criteria for the identification of rock types. This is particularly acute when core recoveries are low and many of the diagnostic features (e.g. chilled margins) are missing; (c) preferential selection during the drilling process of more massive competent materials, such as sheet flows, cores of pillows and large clasts in breccias; (d) under-estimation of breccia units due to lack of recovery and requirement for two pieces in the core before a breccia unit was recorded. Although the logging data appear to provide a solution, it must be stressed that these results have errors associated with the individual tools, such that the spatial resolution varies (i.e. small units may not be imaged by some tools due to measurement scale, Lovell et al. 1998) and in areas of borehole break-outs the measurement errors of the individual logging tools are so high that the data are not recording real geological features but are an artefact of the oversizing (Fig. 4b). However, the logging results when used in conjunction with observations from the core provide important constraints on the physical and chemical properties of rocks in the borehole. Together they can be used to define the lithological diversity of the volcanics and so provide important constraints on magmatic and subsequent alteration processes. In Hole 896A a major break in the geochemical signatures is apparent at c. 340 mbsf (Alt et al. 1993; Brewer et al. 1995) which correlates with the location of the major breccia unit identified from the logging results (Fig. 12). This boundary at c. 340 mbsf also marks a change in the proportion of flows, such that above 340 mbsf, sheet flows are rare and the major flow form are pillow lavas, whereas below 340 mbsf, sheet flows are the more common flow form. Ballard et al. (1979) demonstrated that in the neovolcanic zone of the Galapagos rift, there was a spatial association of flow forms, with sheet flows being the common eruptive mechanism along and close to the rift-axis, whereas, pillow lavas were the more commonly eruptive mechanism for off-axis volcanism. The greater abundance of sheet flows in the lower part of the hole (Fig. 12) probably reflects volcanism in an axial setting, whereas the greater proportion of pillow lavas in the upper part of the hole (Fig. 12) probably represents off-axis volcanism. The

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transition between these two volcanic settings correlates with the thick breccia unit, which may represent debris accumulated during a hiatus in volcanism as the crustal segment moved off-axis. Hydrothermal circulation in the ocean crust is strongly influenced by lithological anisotropy (Becker 1985; Pezard & Anderson 1990; Brewer et al. 1991; Teagle et al. 1996), such that the more massive impermeable flows serve to both focus and restrict fluid flow into the more permeable pillow lavas and breccia units (Alt et al. 1993). As a result of this enhanced fluidrock interaction, the pillows and particularly the breccias, contain the most altered rocks in the drilled section (Teagle et al. 1996), which ultimately controls the mass balances used to constrain chemical fluxes in the crustal segment. Evidence of the preferential alteration in the breccias is also recorded in the rapidly changing whole-rock geochemistry of samples recovered in the interval c. 340 to c. 380 mbsf (Brewer et al. 1996). Previously, this geochemical pattern was difficult to resolve with the lithological description for this section (Fig. 12), which indicated massive units which should have comparatively low porosities and permeabilities. However, from the log stratigraphy, the predominance of breccias in this zone, with relatively high porosities and permeabilities, suggests that secondary alteration controls much of the geochemical variation and behaviour in this zone.

Conclusions Logging results provide important constraints on the geology of the volcanics in Hole 896A, which reflects the excellent quality logging data. Since break-outs are limited in this hole, the amount of data rejected was small (Fig. 4a; < 9%) and so a near continuous logging record is available. Individual logging tools provide different measurements which can be inverted to give different geological information on each of the different rock types. However, where local core recoveries are high ( > 4 0 % ) conclusions from the logging data can be qualified. By combining the core and logging results a detailed model of the volcanic architecture of layer 2A can be derived. These models suggest that the major control on the style of volcanology is the proximity to the ridge crest, such that sheet flows correspond to an axial setting, a feature shown previously from submersible studies (Ballard et al. 1979). Furthermore, the lithological anisotropy is the major control on subsequent fluid flow and secondary alteration, the larger scale picture of which correlates well with the logging

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results. It is therefore critical that logging results are acquired as soon as possible after drilling in order to avoid deterioration of the hole (i.e. oversizing). T h e n by i n t e g r a t i n g c o r e a n d logging results, detailed models of the lithological a r c h i t e c t u r e of the ocean crust can be generated which reveal its 3-D structure, which is crucial for the assessment of m a g m a t i c and later alteration processes.

We thank Z & S Consultants Ltd for the provision of their log and imaging software system, RECALL, and NERC for financial support of the borehole imaging facility at Leicester University (grant GST/02/684). Careful reviews by M. Eisk and C. Laverne helped to improve this paper significantly.

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size spectrum among pillow lavas. Bulletin of Volcanology, 54, 459-474. WEISS,R. F., LONSDALE,P., LUPTON,J. E., BAINBRIDGE, A. E. & CRAIG, H. 1977. Hydrothermal plumes in the Galapagos Rift. Nature, 267, 600. WILSON, M. 1989. Igneous petrogenesis: a global tectonic approach. Unwin Hyman, London. WINDLEY, B. F. 1995. The evolving continents, third edition. John Wiley and Sons Ltd, Chichester. YAMAGISHI, H. 1985. Growth of pillow lobes-evidence from pillow lavas of Hokkaido, Japan and North Island, New Zealand. Geology, 13, 499-502.

Physical signature of basaltic volcanics drilled on the northeast Atlantic volcanic rifted margins C. J. B U C K E R l, H. D E L I U S 2, J. W O H L E N B E R G 2 & L E G 163 S H I P B O A R D SCIENTIFIC PARTY

1 GGA, Geowissenschaftliche Gemeinschaftsaufgaben (Joint Geoscientific Research of the State Geological Surveys), Stilleweg 2, 30631 Hannover, Germany 2 Lehr- und Forschungsgebiet Angewandte Geophysik, Rheinisch-Westfdlische Technische Hochschule, Lochnerstr. 4-20, D-52056 Aachen, Germany Abstract: During several DSDP and ODP Legs in the NE Atlantic, basaltic lava flows of the early rifting and break-up phase in the Tertiary have been drilled and logged. The lava flows were deposited subaerially with characteristic variations in their physical and magnetic properties and it is possible to distinguish different intraflow zones (I-IV) by evaluating downhole and core measurements. The physical and magnetic properties are mainly influenced by the degree of vesicularity and alteration, especially in the top and bottom parts of the flows. The pattern of the magnetic properties susceptibility and remanence seems to be helpful in distinguishing different flow types (aa and pahoehoe) as well as intraflow structures. These zonal characterizations of subaerial basaltic lava flows can be seen frequently in core as well as in downhole measurements. This holds true not only for one hole but also for holes at different locations in the NE Atlantic with an exceptional high correlation down to fine scale variations, pointing to comparable genetic mechanisms during the initial phase of rifting. Several DSDP (Deep Sea Drilling Project) and ODP (Ocean Drilling Program) Legs are located in the NE Atlantic, addressing the nature of early rifting and break-up of the NE Atlantic (Legs 81,104, 152, 163). At the time of magnetic anomaly 24, the time of opening of the N Atlantic, the drillings of the mentioned Legs would be located at the continental margins (Rockall Plateau, Voring Plateau, SE Greenland). These continental margins are type examples of volcanic rifted margins and they are characterized by broad seaward dipping reflector sequences (SDRS). All the drilled basaltic volcanics belong to a large igneous province (LIP) similar to the Kerguelen- or Ontong-Java-Plateau (White & McKenzie 1989; White 1992; Coffin & Eldholm 1992, 1993, 1994). Rifting and break-up along the NE Atlantic during the Early Tertiary was accompanied by an intense phase of magrnatism and the eruption of large volumes of volcanic rocks, which formed the SDRS (Hinz 1981; White & McKenzie 1989; Fitton et al. 1995; White & Morton 1995). Several DSDP and ODP Legs (81 Rockall Plateau, 104 Vring Plateau, 152 and 163 Southeast Greenland Margin) have shown that these SDRS are mainly composed of subaerial basaltic lava flows (Roberts et al. 1984; Eldholm et al. 1987; Larsen et al. 1994, ODP Leg 163 Shipboard Scientific Party 1996). The results of the

DSDP and ODP drillings together with shorebased studies (i.e. Larsen & Marcussen 1992) and drillings on the Faeroe Islands (Waagstein & Hald 1984) all suggest near sea-level eruption of the lava within the SDRS (Larsen et al. 1994). The Tertiary igneous provinces of Britain, Northern Ireland, the Faeroes and Greenland were formed at the same time (White & McKenzie 1989). All these igneous provinces together form the North Atlantic Volcanic Rifted Margin. Although the volcanic nature of the SDRS is firmly established (Roberts, et aL 1984; Eldholm et aL 1987), the geological processes responsible for the formation of their characteristic architecture is still partly a matter of conjecture (Larsen et al. 1994). The purpose of this paper is to show the relation between physical and magnetic rock properties and the volcanic flow structure. Borehole measurements as well as core measurements will be used to cast some light on this matter. D S D P and O D P drillings in the northeast

Atlantic At the time of break-up of the N Atlantic and rifting, all the drillings of the DSDP/ODP Legs

BI]CKER, C. J., DELIUS,H., WOHLENBERG,J. ,~r LEG 163 SHIPBOARDSCIENTIFICPARTY. 1998 Physical signature of basaltic volcanics drilled on the northeast Atlantic volcanic rifted margins In- HARVEY,P. K. ~r LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications. 136. 363-374

363

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Fig. 1. Reconstruction of the North Atlantic at the time of magnetic Anomaly 24 with the disposition of the major continental blocks (after Srivastava 1978), the distribution of on- and off-shore basalt flows and sills and the palaeolocation of DSDP/ODP Legs 81, 104, 152 and 163 (modified after Larsen et al. 1994). 81, 104, 152 and 163 are situated along a more or less N - S trending line at these early continental margins (Fig. 1). The Legs 81, 104 and 152 and the Faeroe Islands are situated along these margins, while Legs 152 and 163 are situated across the margins. To understand the genesis of the voluminous volcanic activity during the short reversed polarity between magnetic anomalies 24 and 25 and the formation of coeval suites of dipping reflectors at the conjugate margins of Greenland, Rockall and the Norwegian Sea, it is useful to correlate the volcanic sequences of East Greenland, Rockall and Voring Plateau and the other volcanic areas in the NE Atlantic in more detail. The petrology and petrography of magmatic products like subaerial flood basalts in large igneous provinces is mostly well known (i.e. Cox

1980; MacDougall 1988; Rowland & Walker 1990). However, there is only little information on flow structure or flow thickness. These characteristics of volcanic flows are reflected by their physical properties and they in turn are controlled by magma eruption rates, emplacement mechanisms and cooling histories (Walker 1971, 1993; Rowland & Walker 1990; Long & Wood 1986; Walker 1971). For the investigations and comparisons carried out in this study, one drill site for each leg with a logging suite as complete as possible in the volcanic succession was chosen: (i) 553A of Leg 81 (Rockall Plateau); (ii) 642E of Leg 104 (Voring Plateau); (iii) 917A of Leg 152 (SE Greenland)

PHYSICAL SIGNATURE OF BASALTIC VOLCANICS

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Due to some weather problems (damage occurred to the vessel during a hurricane force), no downhole measurements have been achieved in any of the holes of Leg 163. For Hole 990A of Leg 163 (SE Greenland) the physical properties of cores were measured onboard with a distance between the measuring points of no more than 5 cm. As can be seen from Fig. 2, the composite stratigraphic section may be referred to an almost complete lava flow sequence consisting of upper series, middle and lower series, and a basal flow. The sequence includes the earliest lavas overlying pre-rift sediments and continental basement rocks as well as Icelandic-type

Lithostratigraphy, vesicularity and physical properties of volcanic flows In all the mentioned boreholes, subaerial volcanic flows of large amounts have been drilled and cored with a core recovery of 50% to 80%. In general, the drilled volcanics are composed of phyric and aphyric basalts with minor dacites and picrites and also some tuff and sediment intercalations. The recorded downhole logs and the physical and magnetic property core measurements are well suited to give information about lithology and intraflow structure. The measured physical properties and downhole logs are mostly controlled by the vesicularity of the basalt flows. Generally, gas bubbles or vesicles are ubiquitous in basaltic flows. The occurrence and features of the vesicles that remain in lava flows, can yield important information on flow mechanisms and lava rheology. There are only a few studies which have yet pursued this topic (Walker 1993). In Fig. 3, a typical flow structure with vertical changes in vesicularity is shown in combination with responses of downhole measurements. The correlation shows that the flow structure can be distinguished by the logs. The lower flow boundary is marked by a sharp peak in resistivity. The region with the maximal amount and sizes of vesicles can be attributed to the lowest values in density RHO and velocity Vp and the highest values in the gamma ray GR. This region corresponds to the flow zones I and II (Delius et al. 1995) that will be described in detail later. Flow zone III marks the massive basalt with only a small amount of vesicles, giving high density and velocity values. In flow zone IV the size and amount of the vesicles again increase, whereas the density and velocity decrease. A description of the lithostratigraphy and intraflow structures of Hole 642E is given by Delius et al. (1995) and Planke (1994) and of Hole 917A by Demant et al. (1995) and Planke & Cambray (1996). Typical subaerial volcanic flow structures can also be seen for example in the Goban Spur drillings W of Ireland (Tate & Dobson 1988) and in the Deccan Traps, Central India (Buckley & Oliver 1990). In the following, the lithostratigraphic descriptions of Holes 553A and 990A are emphasized.

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Fig. 3. Generalized section across a 5 m thick pahoehoe flow unit, showing different shapes, sizes and zonal distribution of vesicles. The curves for the maximal sizes and volume percentages of the vesicles are given in the middle column (modified after Walker 1993). The right side shows typical log responses within a single volcanic flow. SFLU: resistivity in Ohm m in linear scaling to enhance the peak at the bottom of the flow, GR: gamma ray in API; Vp: compressional wave velocity in km s 1, RHO: density in g cm-3. In relation to these log responses, the flow is divided into four typical flow zones I-IV (right column with different grey shadings).

H o l e 5 5 3 A ( D S D P L e g 81)

In Hole 553A a sequence of basaltic lava flows was drilled from 499 mbsf to a total depth of 683 mbsf. The sequence was divided into three subunits (unit 1:499-562 mbsf (metres below sea level), unit 2:562-614 mbsf, unit 3:615-683 mbsf) on the basis of physical and magnetic core properties and the logging data; major petrographic differences were not identified among the units. The principal differences between the subunits relate to the gamma ray curve (GR) with higher variations and on average higher readings for the upper and lower part and lower readings for the middle part (Roberts et al. 1984) (Fig. 4). In the upper part of the volcanic sequence aphyric and microphyric basalts are dominant, whereas in the lower part some phyric basalts are present. The units consists primarily of sequences of tholeiitic basalt lava flows characterized by scoriaceous top surfaces. Regarding the downhole measurements, increased gamma ray values at the top or in the lowermost section of single flow units may indicate scoriaceous parts, tufts, or sediments (see Fig. 4). In fact, during alteration and weathering,

potassium as a compatible element is likely enriched in these sections. Below the scoriaceous agglomerate top parts, phyric basalts with open or filled vesicles (typically filled with zeolite) pass downwards to sparsely vesicular and more dense basalts. Here, vesicles are typically filled (zeolite, chalcedon, smectite), small, and less abundant, the massive basalt is predominant. Towards the base of the flow, large open and filled vesicles become more abundant and in the last decimetres of the flow, vesicles (open and filled) increase rapidly in abundance while decreasing in size. In most cases, this basal part of individual flows is characterized by a sharp increase of the SFLU log (Spherical Focused Log) (Roberts et al. 1984) and a decrease in density and velocity (Fig. 4). Obviously these high resistivities at the base of a single flow may result from a quenched basal lava flow which shows a somehow glassy structure (Delius et al. 1995) that may be altered with time. A description of the typical change of vesicularity within a single flow downwards is given by Roberts et al. (1984); Walker (1993); Aubele et al. (1988). These vesicular flow characteristics are shown in detail in Fig. 3 and

PHYSICAL SIGNATURE OF BASALTIC VOLCANICS

367

Fig. 4. Volcanic pile together with a composite log of Hole 553A at the Rockall Plateau. From left column to right column: simplified lithology (after Roberts et al. 1984), individual lava flows as detected by logs (separated by different grey scales), SFLU log in linear scale to enhance the sharp peaks at the bottom of single flows, density RHOB, neutron porosity NPHI, compressional wave velocity Vp, and gamma ray GR, can be well compared with the physical properties and downhole logs (see also the later section 'Single flow physical property characteristics'). Within one single flow, the densities (RHOB) and compressional wave velocities (Vp) are well correlated and show large variations with values ranging between 2-3 g cm 3 and 2- > 6 km s-l, respectively. The lower values correspond to the flow top and bottom parts yielding the high vesicularities and high alteration degrees, whereas the massive basalt in the centre part of a flow shows the highest values of density and

velocity. The single flows show a typical decrease in neutron porosity downward in the central part with a sharp increase in the vesicular zone in the lowermost part of the flow (Fig. 4) which is also accompanied by firstly an increase in density and then a decrease towards the base part of the flow (Fig. 7a, b, zone IV). The density log in turn is well correlated with the NPHI log, which is a measure for the hydrogen content, not only for pure porosities. So, the NPHI log may indicate not only waterfilled vesicles but also the weathered flow tops

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Fig. 5. Compilation of core measurements from Hole 990A. From left column to right column: core recovery in black, flows 1-13 with flow boundaries, flow type (aa, transitional, pahoehoe), rock type, density (measured on full rounds with the shipboard GRAPE Gamma Ray Attenuation Porosity Evaluator), compressional wave velocity Vp measured on half rounds, magnetic intensity after 30 mA alternating field demagnetization, and susceptibility.

with alteration minerals containing hydrous oxides (smectite, montmorillonite, celadonite, zeolite, chalcedon). Single lava flows from Hole 553A as well as intraflow variations, i.e. the different development of neutron porosity and alteration with depth, are well identified by the logs RHOB, Vp, NPHI, SFLU and GR. The structure of a basaltic lava flow depends (among other things like eruption mechanisms (Aubele et al. 1988) or atmosphere conditions (Sahagian et al. 1989)) on

the distance from the volcanic vent (reflected by the grain size distribution), the cooling history and the thickness of the lava flow and the depositional environment. So, log interpretation in volcanic flows may help in answering questions related to these topics.

Hole 990A ( O D P Leg 163) A comparable structure of the volcanic flows can be seen in Hole 990A. This drilling is situated

PHYSICAL SIGNATURE OF BASALTIC VOLCANICS

369

Fig. 6. Density-velocity correlation for volcanic lava flows in the NE Atalantic. Data were taken from log and core measurements from holes 553A, 642E, 917A and 990A as indicated. The densities of Hole 990A were measured on full round cores whereas the velocities were measured on the split cores (halfrounds). Note the regression lines all lying close together.

close to the coast of SE Greenland in a water depth of less than 500 m. Below a sediment cover which extends down to 212 mbsf, basaltic lava flows were drilled to a final depth of 342 mbsf. As mentioned at the beginning, downhole measurements could not be achieved in any of the holes of Leg 163, but the core recovery in the igneous section was quite good (up to 70%) and provides a good basis for further investigations. The physical and magnetic properties like density and susceptibility were measured with the shipboard MST, the Multi Sensor Track system on full round cores with a measuring point distance of 2 cm prior to sawing. As soon as possible after core retrieval, the p-wave velocities were measured on water saturated half round cores with the Hamilton Frame Velocimeter with a measuring point distance of 5 cm. The magnetic intensities were measured every 10

cm with the shipboard cryogenic magnetometer after alternating field demagnetization of 30 mA to remove secondary remanences like drilling induced magnetizations (Nakasa, pets. comm.). The results of the measurements in relation to rock type, flow type and flow boundaries are shown in Fig. 5. Due to the high sampling rate a very detailed structure of the volcanic pile can be seen. Alltogether 13 lava flows were recognized. The volcanic lava flows at Site 990 can be related to three flow types. In the upper part of the hole, aa lavas were drilled, and in the lower part pahoehoe lavas with transitional type lavas between. The aa lava is characterized by a commonly thick brecciated flow top and a thin vesicular flow base. For the pahoehoe flow type, the brecciated top is missing and it has a thick upper vesicular zone, a massive interior and vesicles at the base. The transitional type lava

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PHYSICAL SIGNATURE OF BASALTIC VOLCANICS has a thin brecciated or vesicular flow top, some internal flow banding and a vesicular base. The lithology of the interior of the flows ranges from aphyric to pyhric basalt. Grain size and flow thickness tend to increase upwards in the drilled section (ODP Leg 163 Shipboard Scientific Party, 1996). A comparable trend in the grain size distribution can also be seen in Hole 553A (see preceding chapter) as well as in Hole 917A with the pahoehoe flows in the lower part and the aa flows in the upper parts of the borehole. These flow type variations and intraflow structures with brecciated and/or vesicular top and bottom zones are reflected by the core measurements (Fig. 5). As described for the log measurements of Hole 553A, density and velocity are again well correlated and show high variations. The core measurements reveal characteristic features for the three flow types. Highest density and velocity values can be found generally in the non-vesicular or only less vesicular flow interiors where the massive and homogeneous basalts occur. Here, the flow interiors are characterized by densities of 3 g cm -3 and velocities of about 6 km s 1. Very low density and velocity values can be found at the flow boundaries of the aa type flows. In general, the density, velocity and susceptibility variations as well as their absolute values are decreasing downwards in the hole, whereas the magnetic intensity variations are decreasing upwards and the average values are increasing downwards. These findings for the physical and magnetic properties might be in correlation with the flow types and the amount of vesicular and/ or brecciated zones and the grain size distributions. In general, the aa flows are more fine grained whereas the pahoehoe flows are more coarse grained. The pahoehoe flows show the lowest variations in density and velocity and the highest variations in magnetic intensity. The high variations of density and velocity for the aa flows are primarily caused by the brecciated and altered top parts of the flows revealing very low density and velocity values. The magnetic intensity shows only some enhanced peaks, but the susceptibility is in general relatively high for the aa flows. Although the differences in magnetic property behaviour might have several single or combined reasons (like alteration conditions, grain sizes, variable cooling histories), they seem to be helpful in distinguishing different flow types. This will be a subject of further investigations. In summary, the core measurements of Hole 990A are well suited for distinguishing flows and intraflow structures as well as the log measurements as shown for Hole 553A.

371

Correlation of physical properties There is a very high correlation between physical properties that have been measured on different core types. Core measurements were conducted on full rounds, half rounds and also on mini cores with 2.5cm in diameter. In Fig. 6, this correlation is shown for the density and compressional wave velocity. Although the minicore measurements integrate over a much smaller volume than the full or half round measurements, the correlation is fairly high. This good correlation is (among other things) an effect of measurements immediately after core recovery. During time after core recovery, and depending on core depth (confining pressure), the cores undergo a stress release (Zang & Berckhemer 1989) and this in fact plays a role for density and for more enhanced velocity estimation by forming microcracks. There is not only a good correlation between measurements for one hole but also between different holes in comparable environments (Fig. 6). The large amount of data are coming from density and velocity downhole and core measurements from the Holes 990A, 917A, 642E and 553A. The lines in Fig. 6 are linear regression lines for each dataset. Although the scatter of the data is not negligible, the regression lines are close together. There seems to be a different trend of increasing velocities at higher densities. This trend is reflected by borehole as well as by core measurements. The reason for this is not well understood. It may be that the anisotropic nature (flow bandings) of the massive basalts is responsible for this trend.

Single flow physical property characteristics Each flow exhibits a characteristic variation in physical properties. This is especially true for thick fine-grained (aa) flows. The detailed structure of selected single flows of this type from the drillings 553A, 642E, 917A and 990A is shown in Figs 7a and b. Flow #3 from Hole 990A (Fig. 7a, top, see also Fig. 5) consists of a moderately phyric plagioclase basalt and has a thickness of 15 m. Due to variations in physical properties, the flow can be divided into four characteristic zones. The top of the flow (zone I + z o n e II) is characterized by a moderate to complete alteration (ODP Leg 163 Shipboard Scienitific Party 1996). These zones of the flow (260-264.3 mbsf) can be compared with low to moderate density (2.5-2.8 g cm -3) and velocity values (2-5 kms 1). The susceptibility and the remanence show the highest values in the alteration zone II. This

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C.J. BI~ICKER E T AL.

might be caused by a low temperature oxidation of titanomagnetite to pure magnetite or maghemite having a higher specific susceptibility (Audunsson et al. 1992). There may also be an enrichment of Fe-oxides in this permeable zone yielding enhanced magnetic values. Zone II characterizes a transition between zones I and III with increasing density and velocity values. In the central part of the flow (zone III) down to 272.5 mbsf, the highest values of density and velocity occur, indicating a massive basalt. The susceptibility values are moderate, pointing to titanomagnetite as the magnetic mineral. The bottom of the flow (zone IV) is also characterized by high alteration and vesicularity with decreasing velocity and density values and enhanced remanence and slightly enhanced susceptibility values. In this zone, a small density peak just below zone III can be observed in many aa flows. For comparison, corresponding logs of Hole 553A (Leg 81) for flow #12, of Hole 642E (Leg 104) for flow #53 and of Hole 917A (Leg 152) for flow #60 are shown in Figs 7a and b. There is a high correlation between the corresponding core and downhole measurements of Holes 990A, 642E, 553A and 917A. They show a very similar vertical structure of the flows.The gamma ray peak at 683 mbsf in Hole 642E may be correlated to the moderately altered zone at the top of the flow (zone II) with the enhanced magnetic values in Holes 917A and 990A. The gamma ray logs in zones I and II of Holes 917A and 553A also show the highest values in these altered and brecciated zones. Obviously this is an effect of potassium enrichment in these zones that probably occurs in alteration minerals (smectite, celadonite). As described before, the bottom of a flow can be detected by a sharp increase in the SFLU log. This sharp increase falls into flow zone IV with low density and velocity values. Often a density peak can also be observed in this zone. The SFLU log of Hole 917A is somewhat different from that of Holes 553A and 642E and the density log does not show this pronounced peak at the base of a flow. This may be an age effect and thus due to a more progressive alteration. During alteration, clay minerals are filling and replacing glass (Despraities et al. 1989). As shown in Fig. 2, the oldest basalts have been drilled in Hole 917A and the youngest basalts have been drilled in Hole 553A. Possibly the basalts drilled in Hole 917A and especially the flow tops and bottoms with the enhanced permeabilities are more altered and thus show a change in their physical properties. The character of the SFLU peak might be also influenced by the depositional environment.

That means, for example, that the peak development is more significant at shallow water conditions than at dry ground conditions during the flow emplacement. In all flows shown in Fig. 7a and b, the central massive basalt is characterized by zone III with highest values in density and velocity but lowest values in gamma ray and magnetic properties. Obviously, the magnetic properties are affected by the alteration thus resulting in enhanced values in the top and bottom zones. Comparing the curves of the four holes there seem to be two groups with a high correlation between the corresponding curves of density and velocity for Holes 990A and 553A and a similar correlation with a relatively higher variation in velocity for the corresponding curves of Holes 642E and 917A.

Conclusion The key to understanding variations in physical properties of basaltic lava flows are the intraflow vesicle sizes, shapes and distributions and the alteration stages of the flow tops and bottoms. The comparison of different physical properties of volcanic flows in the NE Atlantic shows that they can be clearly attributed to four characteristic flow zones (zones I-IV) with changing vesicularity (Fig 3). This holds not only for the comparison of corresponding log measurements but also for continuous core measurements. It has been shown that the core measurements associated with a high recovery are as well suited for the differentiation of a volcanic pile and intraflow structures as the log measurements. Although the flow structure can also be seen in FMS (Formation Microscanner) images (Cambray 1996), the complete physical structure can be derived only by downhole and core measurements. As the structure of a single lava flow depends on many influences (i.e. distance from the volcanic vent (grain size distribution), cooling history, flow thickness, growth and rise of gas bubbles, sea level air pressure), log interpretation in volcanic flows may give help in answering questions related to these topics. It seems that the magnetic properties play a special role in finding these answers. Obviously, the magnetic properties, remanence and susceptibility, are not only suited to differentiate intraflow structures but also to distinguish between aa (fine-grained) and pahoehoe (coarse-grained) flow types. Probably the reason for this is the different cooling history of aa and pahoehoe flows, resulting in different grain size distributions also for the magnetic particles and thus in different

PHYSICAL SIGNATURE OF BASALTIC VOLCANICS magnetic properties. The magnetic properties, susceptibility and remanence are not only influenced by the kind of the carrier of magnetization but they are also strongly influenced by the grain size of the magnetized particles. Palaeomagnetic studies as well as Formation Microscanner image analyses should take notice of the enhanced magnetic values at the top and bottom of volcanic flows, because the magnetic directions might be influenced. Although the holes discussed here are geographically distant, the physical properties and the structures of the drilled volcanic flows are similar, pointing to comparable genetic mechanisms during the initial rifting phase.

Our special thanks are owed to all of the Leg 163 Scientific Party and the excellent crew with Captain E. Oonk especially during the storm. Thanks to Y. Nakasa who made available the magnetic intensity core data from Hole 990A. The help from A. Essers in data preparation and processing is greatfully acknowledged. The critical and helpful comments of two anonymous reviewers have been of great value in improving an earlier version of the manuscript. We would also like to thank P. Harvey for his encouragements in writing this contribution. Thanks to the DFG (Deutsche Forschungsgemeinschaft) for the financial support.

References AUBELE, J. C., CRUMPLER, L. S. & ELSTON, W. E. (1988). Vesicle zonation and vertical structure of basalt flows. Journal of l/oleanology and Geothermal Research, 35, 349-374. AUDUNSSON,H., LEVI,S. & HODGES,F. 1992. Magnetic property zonation in a thick lava flow. Journal of Geophysical Research, 97, 4349-4360. BUCKLEY, D. K. & OLIVER, D. 1990. Geophysical logging of water exploration boreholes in the Deccan Traps, Central India. In: HURST, A., LOVELL,M. A. & MORTON,A. C. (eds) Geological application of wireline logs. Geological Society Special Publications No. 48, 153-161. CAMBRAY, H. 1996. Structures within Hole 917A, Southeast Greenland rifted margin. In: SAUNDERS, A. D, LARSEN,H. C., WISE, W., et al., Proceedings of ODP Scientific Results, 152. College Station, TX (Ocean Drilling Program). Cox, K. G. 1980. A model for flood basalt volcanism. Journal of Petrolology, 21, 629-650. COFFIN, M. F. & ELDHOLM,O. 1992. Volcanism and continental break-up: a global complication of large igneous provinces. In: STOREY, B. C., ALABASTER,T. & PANKHURST,R. J. (eds) Magmatism and the causes of continental break-up. Geological Society Special Publications No. 68, 17-30. & 1993. Grol3e Eruptivprovinzen.

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Spektrum der Wissenschaft, 12, Akademischer Verlag. & 1994. Large igneous provinces: crustal structure, dimensions and external consequences. Reviews in Geophysics, 32, 1-36 DELIUS, H., BI~CKER, C. J. & WOHLENBERG,J. 1995. Significant log responses of basaltic lava flows and volcaniclastic sediments in ODP Hole 642E. Scientific Drilling, 5, 217-226 DEMANT, A., CAMBRAY,H., VANDAMME,D. & LEG 152 SHIPBOARDSCIENTIFICPARTY1995. Lithostratigraphy of the volcanic sequences at Hole 917A, Leg 152, SE Greenland margin. Journal of the Geological Society London, 152, 943-946. DESPRAIRIES, A., TREMBLAY, P., & LALOY, C. 1989. Secondary mineral assemblages in a volcanic sequence drilled during ODP Leg 104 in the Norwegian Sea. In: ELDHOLM, O., THIEDE, J., TAYLOR, E., et al. (eds) 1989. Proceedings ODP, Scientific Results, 104. College Station, TX (Ocean Drilling Program). ELDHOLM, O., THIEDE, J., TAYLOR, E. & LEG 104 SHIPBOARD SCIENTIFIC PARTY 1987. The Norwegian continental margin: tectonic, volcanic, and paleoenvironmental framework. Proceedings in ODP, Initial Reports, 104. College Station, TX (Ocean Drilling Program). FITTON, J. G., SAUNDERS,A. D., LARSEN,H. C., FRAM, M. S., DEMANT, A., SINTON, C. & LEG 152 SHIPBOARD SCIENTIFIC PARTY 1995. Magma sources and plumbing systems during breakup of the SE Greenland margin: preliminary results from ODP Leg 152. Journal of the Geological Society, London, 152, 985-990. HINZ, K. 1981. A hypothesis on terrestrial catastrophes. Wedges of very thick ocean and dipping layers beneath passive continental margins--their origin and palaeoenvironmental significance. Geologisehes Jahrbuch, E22 1-28. LARSEN, H. C. ,~ MARCUSSEN,C. 1992. SilMntrusion, flood basalt emplacement and deep crustal structure of the Scoresby Sund region, East Greenland. In: STOREY, B. C., ALABASTER,T., & PANKHURST,R. J. (eds) Magmatism and the causes of continental break-up. Geological Society Special Publications No. 68, 365-386 --, SAUNDERS, A., CLIFT, P. • ODP LEG 152 SHIPBOARD SCIENTIFIC PARTY 1994. Proceedings Ocean Drilling Program Initial Reports, 152. College Station, TX (Ocean Drilling Program). LEHURAY, A. P. & JOHNSON, E. S. 1989. Rb-Sr systematics of Site 642 volcanic rocks and alteration minerals. In: ELDHOLM,O., THIEDE, J., TAYLOR, E. et al. (eds) Proceedings ODP, Scientific Results, 104, College Station, TX (Ocean Drilling Program). LoN~, P. E. & WOOD, B. J. 1986. Structures, textures, and cooling histories of Columbia River basalt flows. Geological Society of America Bulletin, 97, 1144-1155. MACDOUGALL, J. D. 1988. Continentalflood basalts. Kluwer Academic Publishers, Dordrecht. MAClNTYRE, R. M. & HAMILTON,P. J. 1984. Isotopic Geochemistry of lavas from Sites 553 and 555. In:

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C.J. B(SCKER ET AL.

ROBERTS, D. G., SCHNITKER,D. et al. (eds) Initial Reports DSDP, 81. Washington (U.S. Govt. Printing Office). ODP LEG 163 SHIPBOARD SCIENTIFIC PARTY 1996. Exploring the volcanic-rifted margins of the North Atlantic. EOS AGU Transactions, 77/17. PLANKE,S. 1994. Geophysical response of flood basalts from analysis of wire line logs: Ocean Drilling Program Site 642, V~ring Volcanic margin. Journal of Geophysical Research, 99, 9279-9296. & CAMBRAu H. 1996. Seismic and other physical properties of Flood Basalt. Proceedings ODP Scientific Results, 152, College Station, TX (Ocean Drilling Program). ROBERTS, D. G., SCHNITKER, D. & DSDP LEG 81 SHIPBOARDSCIENTIFICPARTY 1984. DSDP Leg 81 Sites 552-553. Initial Reports. DSDP, 81, Washington (U.S. Govt. Printing Office). ROWLAND, S. K. 8/: WALKER, G. P. L. 1990. Pahoehoe and aa in Hawaii: volumetric flow rate controls the lava structure. Bulletin Volcanology, 52, 615628. SAHAGIAN,D. L., ANDERSON,A. Z. (~ WARD, B. 1989. Bubble coalescence in basalt flows: comparison of a numerical model with natural examples. Bulletin of Volcanology, 52, 49-56. SINTON, C. W. & DVNCAN, R. A. 1996. Timing and duration of volcanism at the SE Greenland margin, Leg 152, Proceedings Ocean Drilling Program Scientific Results 152, College Station, TX (Ocean Drilling Program). SRIVASTAVA,S. P. 1978. Evolution of the Labrador Sea and its bearing on the early evolution of the North Atlantic. Journal of the Royal Astronomical Society, 52, 313-357. TATE, M. P. & DOBSON, M. R. 1988. Syn- and post-rift igneous activity in the Porcopine Seabight Basin and adjacent continental margin W of Ireland. In:

MORTON, A. C. & PARSON, L. M. (eds) Early Tertiary Volcanism and the Opening of the NE Atlantic. Geological Society Special Publications No. 39, 309-334. WAAGSTEIN, R. • HALD, N. 1984. Structure and petrography of a 660 m lava sequence from the Vestmanna-l drillhole, lower and middle series, Faeroe Islands. In: BERTHELSEN, O., NoE-NYGAARO, A., RASMUSSEN,J. (eds) The deep drilling project 1980-1981 in the Faeroe Islands. Annales Societas Scientarium Faeroensis, Supplementum IX, Torshavn. WALKER, G. P. L. 1971. Compound and simple lava flows and flood basalts. Bulletin of Volcanology, 35, 570-590. 1993. Basaltic-volcano systems. In: PRICHARD, H. M., ALABASTER, T., HARRIS, N. B. W. & NEARY, C. R. (eds) Magmatic processes and plate tectonics. Geological Society Special Publications No. 76, 3-38. WHITE, R. S. 1992. Crustal structure and magmatism of North Atlantic continental margins. Journal of the Geological Society, London, 149, 841-854. & MORTON,A. C. 1995. The Iceland plume and its influence on the evolution of the NE Atlantic. Journal of the Geological Society London, 152, 933. WHITE, R. S. & MCKENzIE, D. P. 1989. Magmatism at rift zones: The generation of volcanic continental margins and flood basalts. Journal of Geophysical Research, 94, 7685-7729. & - 1995. Mantle plumes and flood basalts. Journal of Geophysical Research, 100, 17543-17586. ZANG, A. & BERCKHEMER,H. 1989. Strain recovery, microcracks and elastic anisotropy of drill cores from KTB deep well. Scientific Drilling, 1, 115125. -

-

-

-

Development of the Cote D'Ivoire-Ghana transform margin: evidence from the integration of core and wireline log data C. A. G O N ~ A L V E S

1 & L. E W E R T 2

1Laborat6rio de Engenharia e Explora,cao de Petr6leo, LENEP/UENF, Maca~/RJ, 27973030 Brazil 2 Quantsci Ltd, Melton Mowbray, Leicestershire, LE13 1AF U.K. Abstract: The primary objective for drilling the Cote d'Ivoire-Ghana Transform Margin

during ODP Leg 159 was to assess the sedimentary and deformation processes resulting from the different stages of continental break-up and related transform tectonism. In view of the structural importance of the leg, integration of logging and core data is important to help understand the main tectonic and deformation events that occurred. The effect of the transform deformation can be seen in physical properties data, for instance the porosity data derived from index properties measurements. Major breaks in porosity are associated with the tectonized lower Cretaceous and Cenozoic boundary, a trend also reflected in the P-wave velocity measurements. At each site, core and well log data show the presence of a major unconformity between the Cretaceous and Cenozoic, marked by an offset in porosity, density and P-wave data. The physical properties of log data are also heterogeneous, reflecting variations in consolidation, age and lithology. Another interesting aspect covered by core-log integration was the structural relationship within the sediments. As well as the direct measurements made on cores, in situ structural measurements have been obtained using the Formation MicroScanner (FMS; Mark of Schlumberger) logging tool in two of the holes. The measurements cover the Eocene to Turonian-upper Santonian limestones. Bedding planes dip predominantly towards NWNNW and show an increase with depth which can be interpreted to be the result of steady subsidence of the Deep Ivorian Basin. Break-outs and fracturing were also observed. Breakout occurrences depend on sediment type and their axes are perpendicular to the maximum compressive horizontal stress east-northeast west-southwest. Fracturing occurs as normal and reverse microfaults, with dispersion of dips and azimuth directions in these zones. The presence of fault zones are also correlated with changes in the physical properties of the sediments.

During Ocean Drilling Program (ODP) Leg 159, 13 holes were drilled in four sites (959, 960, 961 and 962) on the C6te d'Ivoire-Ghana Transform Margin (Fig. 1). All holes were continuously cored using APC (Advanced Piston Core), XCB (Extended Core Barrel) and RCB (Rotary Core Barrel) techniques and had an overall core recovery ranging from 30% (Site 962) to nearly 70% (Site 960). A full suite of standard Schlumberger logging tools was also deployed in four of these holes (959D, 960A, 960C and 962D), collecting in situ continous physical properties measurements. The central purpose of this paper is to discuss the integration of data from cores and wireline logs to interpret the different stages observed in the evolution of the C6te d ' I v o i r e - G h a n a Margin. The main objectives are to evaluate: (1) the reliability of both types of measurements;

(2) the extent to which the physical properties of the sediments are controlled by the tectonism; (3) the consistency of the structural features in the cores with downhole Formation MicroScanner (FMS) images. We used data from three holes (959D, 960A and 960C) because only these holes were drilled through the entire sedimentary section, covering periods of hiatus and condensed sedimentation from late Cretaceous to Paleocene. In addition, these holes contained a full suite of logs including FMS data. The sedimentary sequence investigated at Sites 959 and 960 consisted from top to bottom, of chert and claystone (Unit IIB), porcellanite (Unit IIC), black claystone (Unit III) and sandy limestone, sandy dolomite and a calcareous sandstone (Units IVA and IVB) (Fig. 2). The three holes (959D, 960A and 960C) penetrated

GON(TALVES,C. A. & EWERTL. 1998. Development of the Cote D'Ivoire-Ghana transform margin: evidence from the integration of core and wireline log data In: HARVEY,P. K. • LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 375-389

375

376

C.A. GON(~ALVES & L. EWERT

Fig. 1. Location (a) and structural (b) maps of the area surveyed during ODP Leg 159 showing the four sites drilled during the cruise (adapted from Mascle et al. 1996).

different geologic units at different depths depending on their location. Mascle e t al. (1996) show that the interplay of tectonic deformation and sedimentation on the C6te d'Ivoire-Ghana Transform Margin is best represented in the spatial and stratigraphic variation of sediments cored at Sites 959 and 960. Although important information is provided by cores from Sites 961 and 962, the completeness of these sections, and thus the direct contribution for interpreting palaeoenvironmental settings, is comparatively limited. On the basis of sections reconstructed for Sites 959 and 960,

three principal stages of continental break-up have been identified: an Intra-Continental Basin stage, a Marginal Ridge emergence stage and a Passive Margin stage. These stages reflect lithologically distinct intervals in the sedimentation history and relate to changes in the tectonic setting. Even though all of these stages are not present in the sediments recovered, this paper shows that the core-log integration in Sites 959 and 960 can give a substancial input to the understanding of the significance of their spatial relations.

DEVELOPMENT OF A TRANSFORM MARGIN Site 959

Site 960

.

10

20

c~CPl 30

I 40

~m

IIA 50

-lllzllBI

~60 u

80 Con.

90

--~IVAZ_I

"l'ur~ 2enorn

100

I

110 Fig. 2. Stratigraphic column for Sites 959 and 960 with lithologic units along the geological time.

Laboratory

measurements

The relationship between the weight and volume of fluid and solid components of sediments and rocks is reflected in the index properties measurements. By measuring the wet and dry mass, and volume of a sample, a number of interrelated parameters were calculated on board. For Leg 159, shipboard measurements of the physical properties determined from 10cm 3 cored samples included bulk density, grain

377

density and porosity. The techniques used to measure density and porosity (pycnometer) are described in Mascle et al. (1996). Pore waters of marine sediments and rocks contain dissolved salts that may change phase during drying of the samples; thus, a correction for pore water salinity is also included. Compressional wave velocity (Vv) measurements were also obtained on board by using a Hamilton frame velocimeter in lithified sediments (Mascle et al. 1996). The velocity was calculated by dividing the distance between a pair of piezoelectric transducers with the travel time of an acoustic signal between them. In the case of hard sediments, where induration made it difficult to insert the transducers, samples were cut carefully and then had their thickness measured. Only coherent core pieces that could be cut into cubes were selected for measurements. This introduced a bias in the results of this method, as the magnitude of the downhole velocity variation may be over-estimated compared to the in situ downhole velocities. Open-hole intervals in each of the holes were logged with the standard Schlumberger tool strings used in ODP operations and described in Table 1. In general, the quality of both downhole physical properties measurements and the geophysical logs are good. Because of the decreasing core recovery downhole, most of the physical properties measurements are from the upper parts of the holes while geophysical logs were obtained in the open-hole intervals below the casing shoe. Due to the soft and unconsolidated nature of the sediments in the upper part of Hole 959D, casing shoe was set deep in the hole, in order to avoid damaging the tools.

Porosity and density Core porosities were measured on undisturbed core samples, and corrected for salinity (Hamilton 1971). Rebound corrections were not applied to core measurements in the sediments of the C6te d'Ivoire-Ghana Margin due to the presence of diagenetic lithification from Unit IIB downhole. Mascle et al. (1996) show that diagenesis is represented through the replacement of opaline phases by chert and porcellanite in the siliciclastic sediments. Little fabric of the primary lithology is preserved within the replacing chert except for vague traces of bioturbational structure. Goldberg et al. (1987) show that rebound corrections in core measurements are very small in sediments under diagenetic processes and tend to become insignifcant values with depth. Conversion of resistivity log to porosity is

378

C.A. GON(~ALVES & L. EWERT

Table 1. Standard Schlumberger tool strings used during ODP Leg 159 operations Hole

Tool string

Tool string components

Depth (mbsf)

959D

QUAD

FMS

DITE/HLDT/CNT-G/NGTC/TLT DITE/HLDT/CNT-G/SDT/NGTC DITE/HLDT/CNT-G/SDT/NGTC FMS/GPIT/NGTC

GLT QUAD

GST / A A CT /CNT-G /NGTC DITE/HLDT/CNT-G/NGTC/TLT

SEISMIC STRATIGRAPHY FMS

DITE/HLDT*/CNT-G/NGTC/TLT**

main (top): 545-395 main (botton) 1081-994.9 Repeat: 1078-521 main: 936.6-546.9 repeat: 932-655.1 main: 928.7-528 main: 374-86 repeat: 278-86 main: 374.6-184.7

FMS/GPIT/NGTC

main: 354.5-173.7

960A

960C

* HLDT ** TLT DITE: HLDT: CNT-G: SDT: NGTC: TLT: FMS: GST: AACT: GPIT:

used with source removed for caliper only. with bullnose on end. Dual Induction Resistivity tool. High temperature litho-density tool. Compensated neutron porosity tool. Sonic digital tool - - array. Natural gamma ray tool. Lamont-Doherty temperature tool. Formation MicroScanner. Gamma ray spectometry tool. Aluminium activation clay tool. General purpose inclinometry tool.

subject to great uncertainties. Archie's equation (Archie 1942) states that

obtained by using the equation qSi~= (Pma - PB)/(Pma -- Pf),

Swn=(a Rw)/(q~m Rt), where Sw is the water saturation, Rw is the formation water resistivity, ~b is the fractional porosity, Rt is the measured formation resistivity and n, a and m are empirical constants obtained from laboratory measurements. This equation has shown to be reliable for clean sands (no clay). In the case of Leg 159 sediments, measured resistivities include contributions from both free water in pores and bound water in clays. Therefore, more sophisticated equations such as models from Waxman & Smith (1968) or Clavier et al. (1977) are needed to account for the clay effect. However, for the high porosity values encountered in the C6te d'Ivoire-Ghana sediments, the effect of clay contributions may result in under-estimation of porosity by as much as 20% (Jarrard et al. 1989). Therefore, other methods for obtaining porosity should be used. A density log obtained from the Lithodensity tool (HLDT) correlates well with core data in sections with good hole conditions. Then, for these cases a porosity estimate (qSD) can be

where pf is the formation fluid density, PB is the log density and Pma is the the grain density. A histogram of the measured grain density for Hole 959D is shown in Fig. 3. The histogram shows two peaks with mean values of 2.34 g cm -3

Fig. 3. Histogram for grain density ((ma) values measured on cores from Hole 959D.

DEVELOPMENT OF A TRANSFORM MARGIN

379

Fig. 4. Core porosity, density log-derived porosity and neutron porosity log in Hole 959D. Caliper and gamma ray curves for the same interval as well as clay content from XRD analysis are also plotted.

and 2.63 g cm -3 for the intervals between 450 to 770 mbsf (meters below sea floor) and 770 to 1100 mbsf respectively. Using those values and a fluid density of 1.024 gcm -3 measured from pore water samples (Mascle et al. 1996) a computed q~D log was obtained and is shown in Fig. 4 together with core measurements. Both direct measured porosities and density log-derived porosity curves show remarkable agreement for the interval between 550 and 1050 mbsf. There is a strong decrease in porosity from 50-55% at 735-740 mbsf to a minimum of 30% at 770 mbsf and then an increase in the values up to 45% downhole. High values in density log-derived porosity at certain points are due to sections of enlarged hole as shown by the caliper curve. The neutron porosity (~bN) log also shows a good agreement with core and density logderived porosity for the lower part of Hole 959D but it is shifted 10% above 735 mbsf (Fig. 4). The difference is unlikely to be attributed to pycnometer measurements error, since core density and porosity measurements for the same samples correlate well (Fig. 5). The difference is probably caused by a scale problem: while pycnometer measurements sample a volume of 10 cm 3, log data are usually collected over a

Fig. 5. Cross-plot of core density and porosity measurements in Hole 959D.

vertical resolution and depth of investigation varying from 0.6 to 1.0 m (Theys 1990). Another reason for the lower q~N values in the upper part of Hole 959D is probably the decrease in clay content from 740 mbsf uphole, which caused a shift in the neutron log data. The effect of clays is to indicate elevated values in porous shalebearing zones. Clays cause a problem for all neutron porosity interpretation because of the hydroxyls associated with the clay mineral structure (Ellis 1986). The large apparent por-

380

C.A. GON(~ALVES & L. EWERT

Fig. 6. Core porosity and neutron porosity log in Hole 960A. Density and gamma ray curves for the same interval as well as clay content from XRD analysis are also plotted. osity values observed below 740 mbsf (Fig. 4) can be due to the hydrogen concentration associated with the shale matrix in the black claystone (lithologic Unit III). Hole 960A presents density and porosity measurements for both core and log data between 50 and 350 mbsf. In this case, a poor correlation is observed between the absolute values of both measurements (Fig. 6). Breaks in log porosity and log density are observed at 105 mbsf (top of lithologic subunit IIA), at 145 mbsf (within subunit IIB) and at 165 mbsf (between lithologic subunits IIB and III). A major break in core density and core porosity measurements is observed at 185 mbsf, which corresponds to the unconformity between upper Cretaceous and Cenozoic (lithologic subunits III and IVA) (Mascle et al. 1996). Observe the extremely high counts for gamma ray at this depth. Here, a highly condensed, 23 cm thick, section of micrit claystone representing the entire upper Santonian to upper Palaeocene is capped by a phosphatic hard ground. The deposition of such an extremely condensed sequence indicates a depositional environment that was probably swept by currents with rare respites that allowed sediment accumulation.

Velocity The sonic velocity log at Hole 959D is of good quality for sections exhibiting good hole condi-

tions, but is highly affected by borehole diameter variations. In sections subjected to good hole conditions, nearly identical velocities were observed by the long and short spaced sonic tools. In both cases, P-wave velocities were determined from full waveform analysis by a simple first break criterion during data acquisition (ODP 1991). Data were processed onboard to remove cycle skipping effects (Mascle et al. 1996). P-wave velocities were also determined from core samples. Core measurements from Hole 959D are shown in Fig. 7 together with the sonic velocity log curve for the interval between 5501050 mbsf. There is good agreement between the datasets despite the fact that core measurements were performed at atmospheric pressure. Both curves increase from about 1.7kms -1 at 550 mbsf to about 2.2kms ~ at the bottom of the logged interval. At 735-740 mbsf velocities from both datasets increase to 2 . 8 k m s l, which coincides with the decrease in porosity observed in Fig. 4. Another major break is observed at 975 mbsf, also corresponding to a decrease in porosity. These breaks correspond to an angular unconformity within Eocene sediments and the boundary between Cenozoic and upper Cretaceous, respectively. Velocity is plotted against porosity in Fig. 8 for both core and log data in Hole 959D. It shows a fair agreement between both datasets. For porosities between 50% and 60%, core measurements show a velocity-depth trend

DEVELOPMENT OF A TRANSFORM MARGIN Caliper

.0

vE

381

Velocity

55o

550

6oo

60o

65O

65O

700

700

750

750

800

8O0

o~

IIC

~:~

e-" ~ |

ca. (D 850

850

0

9OO

9OO

950

950

lOOO

1000

1050

1050

5

lO 15 inches

20

III /

/

1.5

2

2.5

~

e~. E<

3

Km/sec

Fig. 7. Core velocity and log velocity in Hole 959D. The caliper curve and the stratigraphic column are also plotted.

consistent with log measurements. For porosities less than 50%, the few core measurements obtained show larger variance. Three empirical relationships between velocity and porosity for deep sea sediments are also presented in Fig. 8. Wyllie's (Wyllie et al. 1956) time average relationship greatly over-estimates velocities for high porosity sediments. This equation is only valid for clean and consolidated sediments with uniformly distributed small pore spaces, which is not the case here. Raymer's equation (Raymer et al. 1980) exhibits lower velocities for a given porosity. Calculation of velocities using porosity, matrix velocity, density, grain density and fluid density (e.g. Raymer et al. 1980) would not give reasonable results because the lithology here is dominated by silty clay and clayey silty sediments. Schlumberger (1972) shows that shale matrix velocity can vary by a factor of 2, depending on composition, shape, and arrangements of clay minerals. Although Raymer et al. (1980) implied that their relationship is appropriate for clay-rich sediments, most of the datasets shown for comparison in their work are quartz-rich sediments. Hamilton's equation (Hamilton 1979) shows a more linear relationship but does not entirely represent the relation-

3"5/I /

§~

~+

31 ~--25 L "~

+ ~

+core

++~ - [iql-~

~

! Hamilton's model ~

I 1.5 ~.......

10

. Wyllie's model

+

Raymer'model s ~"---.~..L T - - , ~ 20

30

40

~

50

60

-..

70

Porosity (%)

Fig. 8. Cross plot of porosity and velocity for both core and log data in Hole 959D. Crosses are core data and the contours show data density for log measurements. Three empirical models of the relationship between these parameters are also presented. ship between velocity and porosity either for core or log data. Carlson et al. (1986) presented an average velocity trend with depth for deep sea sediments, based on Deep Sea Drilling Project core data. They concluded that sediment velocities increase linearly with depth. Although velocity seems to increase linearly with depth in Hole 959D, there

382

C.A. GON(~ALVES & L. EWERT

are zones of anomalous increase throughout the interval (e.g. between 745-780 mbsf) that cannot be predicted by Carlson's model (Carlson et al. 1986). This is not surprising, in view of the variations in porosity and mineralogy due to diagenesis within the siliciclastic sediments of subunits IIC . The remarkable match between velocity and porosity behaviour in Hole 959D and their variation with depth lead us to reject the Carlson et al. (1986) model in favour of a more traditional explanation that porosity is the dominant control on velocity. The effect of overburden on velocity is indirect, probably arising from changes in porosity due to compaction. Therefore, a theoretical model using classic Hookean elastic equations for the elastic properties of marine sediments (Gassman 1951) was tested for core data on Hole 959D. The reason to use core data instead of log data in this model is that the former are not affected by enlarged hole conditions as seen in both Holes 959D and 960A. Moreover, a computed velocity log from core data can be applied for other holes in the same area regardless of their borehole conditions. In this model, bulk modulus (K) of a mixture of sediment and pore water is related to velocity (Vp) and density (p) by the equation

K=pVp 2- 4/3 #, where # is the shear modulus. K can also be obtained using K= Ks (Kf + Q)/(K~ + Q), where factor Q is given by

Q=(Kw (K~-Kf))/(c~ (Ks -Kw), where Ks is the aggregate bulk modulus of mineral solids, Kw is the bulk modulus for pore water, and Kf is the frame bulk modulus. Because of the depth of the sediments in Hole 959D, a simplified model assuming that both # and Kf are equal to zero cannot be used. Almost all deep-sea sediments possess some rigidity (Hamilton 1971), and ignoring shear and frame bulk modulus leads to under-estimation of Vp. Moreover, in the case of Hole 959D, tectonism and diagenesis were present and considerably affected the sedimentation process. For consistency with the usual units of velocity (Kin s-z) and density (g cm 3), all moduli

(dyne cm -2) is multiplied by 10-1~ In the case of Hole 959D, Kw is assumed to be 2.37x 10 l~ dyne cm -2 and K~ to be 50x101~ dyne cm -2. Kf is calculated from porosity using Hamilton (1971) empirical equation for silty clays, where log (10 8xKf)=3.735 -4.250q5. The calculated shear modulus (#) for Hole 959D varies from 4.4x 101~ dyne cm -2 at the top of the interval to about 6.5x101~ dyne cm -2 at approximately 1050 mbsf, displaying an expected increase in rigidity with depth (Fig. 9). Bulk modulus (K) is also showing an increase with depth from 6x 1010 dyne cm 4 to 8.5• dyne cm 2. The sharp increase in rigidity observed at 745 mbsf is associated with changes in the deformation records of the continental margin and characterized by an unconformity within Eocene sediments. These changes are also reflected as a break in faulting above 745 mbsf (Mascle et al. 1996). Another break and increase in rigidity occurs at the very bottom of the interval and is related to an unconformity between the Cenozoic and upper Cretaceous. Both K and 1 show a strong correlation with porosity and density as shown in Figs 10a
DEVELOPMENT OF A TRANSFORM MARGIN

i"1

"E: :3

Bulk Modulus

Shear Modulu,, 550

550 I ' ~

i

383 <

' of

600

| =

600 ~

i

650

650

UdI~L

~~~+~7

700 75O E e~

700 750 [

~+4" **~,

80o 850

@@

/o

812.3

~,.-~

'~176

Z++

IIC

650

+

900

~;~.

~.~ g_g,

r'~ 9O0

{

95O 1000

1043.3

96ol +~

1000 I

1050

IVA ~

1050 I I

I

I

i

I

i gila

IVB Unknown?~

9

2 4 6 8 10 (dyne/cm 2 x 1010)

3 4 5 6 7 8

(dyne/cm2 x 1010)

Fig. 9. Calculated shear (#) and bulk modulus (K) for Hole 959D. Note the sharp increase in rigidity within Eocene sediments associated with an angular unconformity. The stratigraphic column for Hole 959D is also shown.

(a) ~

(e)

(c)

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384

C.A. GON(~ALVES & L. EWERT

Fig. 11. A 1.5 m interval in Hole 959D showing the FMS image and corresponding core section. Layers dip 5~ to 10~ to the Northwest.

the borehole wall with an array of 16 electrodes positioned in a four pad-contact tool. This provides a high resolution electrical image of the formation which is displayed in colour or grey scale. The tool, which is a 6 meters long cylindrical measuring device, is lowered to the bottom of the hole and records four perpendicular images (one image for each pad) providing approximately 25% of coverage of the borehole wall (ODP 1991). Improved coverage can be obtained using more than one pass with the tool rotated but very often the pads seem to follow the track left by the previous pass, especially in soft formations. Standard resistivity data are also recorded and used to calculate dips in the formation. The tool contains a triaxial accelerometer and three orthogonal magnetometers to accurately orientate the images. During the recording, measurements are also made of hole size, cable speed, and natural gamma ray on the same tool string. Pezard et al. (1992) shows that FMS conductivity measurements are a function of grain

size, porosity, cementation, induration, mineralogy as well as borehole size, shape and surface features. For rocks of similar mineralogy, cementation and fluid type in a uniform borehole, the pixel tone on the FMS images is observed to be a function of grain size. However, prior to obtaining an image of good quality, data must undergo extensive processing. Schlumberger (1989) and ODP (1991) present a complete description of the processing steps needed for obtaining an image representative of the formation resistivity. FMS data were recorded in three holes during Leg 159. Due to unstable hole conditions (enlarged holes) and consequently bad contact between the pads and the borehole wall, the quality of the FMS images in Hole 962D is poor and the data were not used in this work. In Hole 960C, the FMS images are of moderate quality. A large part of the logged interval is affected by poor pad contact caused by irregular hole size. Between 351-341, 292277, 218-200 and 195-175 mbsf, hole conditions

DEVELOPMENT OF A TRANSFORM MARGIN

385

Fig. 12. Dip and azimuth measurements for the interval between 660--780 mbsf in Hole 959D, including histograms with variations in the measurements.

allow interpretation of the FMS electrical images. For instance, 10 to 30 cm thick resistive layers corresponding to lenticular-bedded sand within clayey silt are detected between 292 and 277 mbsf. The increasing resistivity at 218 mbsf is due to an increase in carbonate (limestones) of lithologic subunit IVB (Mascle et al. 1996). In Hole 959D FMS images are of good quality except in a few sections where hole size is enlarged and irregular in the intervals 865-875 mbsf and 795-810 mbsf. Good electrical data were however recorded by the two passes of the tool and allow us to characterize layering and fracturing of sediments. Between 748-762 mbsf, well stratified, highly contrasted resistivity (yellow in the images) layers about 10 to 15 cm thick are observed. Fig. 11 shows an FMS image and a core photo for the interval between 759.5 to 761 mbsf. It shows the alternations of light greenish-grey micrite porcellanite with dark grey porcellanite with clay within the lithologic subunit IIC. Layers dip 5~ to 10~ to the northwest. Dip and azimuth of the sedimentary bedding were manually measured in both the FMS images and cores of Sites 959 and 960. At the

Fig. 13. Rose diagrams comparing core and FMS dip and azimuth measurements obtained for Site 959. Data are in the geographic reference frame.

top of the logged interval in Hole 959D, the resistivity layers are thick and less contrasted. At 575 mbsf, the dips are about 20 ~ to the north. Most of the FMS measurements were performed between 660-780 mbsf (Hole 959D) in the well layered lower part of Unit IIC (Fig. 12). For Hole 959D, azimuth of bedding is relatively constant around 3200-340 ~ and dips vary from 5~ to 25 ~. The low counts (45 ~ and 60 ~) observed in the histogram in Fig. 12 are due to steeper

386

C.A. GON(~ALVES & L. EWERT

Fig. 14. Left: intense calcite veins set in dolomitic limestone of subunit IVB (Site 959). Centre: sketch of a set of conjugate normal microfaults formed prior to tilting in laminated siltstones in Site 960 (unit III). A reverse microfault and calcite infills are also present along the fault planes. Right: a typical subvertical microfault structure in the black claystones of unit III (Site 959) is shown.

dips at the bottom of the interval. These dispersions in dip values are associated with an increase in fracturing. When comparing core and FMS structural measurements for the same depth intervals (Fig. 13) at Site 959, a reasonable agreement is observed. Data on cores appear more scattered probably due to poor core recovery and bad core reorientation in some sections of the hole. Reorientation of structures to geographic coordinates was possible on cores recovered during Leg 159 through the use of Tensor orientation tool data (APC cores) and palaeomagnetic data (XCB and RCB cores) (Mascle et al. 1996). Two kinds of fractures were observed in the FMS data at Hole 959D: subvertical breakouts (conductive, open fractures), dark in the images; and highly dipping faults (resistive, sealed), light in the images. Resistive fractures are mostly normal and reverse microfaults and veins filled by calcite. The fracturing is localized in a few fault zones, less than 10m thick and consists generally of microfaults with very short offsets. The fault zones are strongly correlated with changes in density, porosity and velocity in Hole 959D. Dispersion of dip and azimuth directions are also another characteristic of these sections

on the FMS data (Fig. 12). Occurrence of microfaults and calcite veins on cores within the lower Cretaceous in Sites 959 to 962 are observed in Fig. 14. Microfaults are sometimes anastomosing and indicate reverse motion as shown on the right in Fig. 14. Although it is very difficult to date the faulting events, it appears that the faults affect mainly the porcellanites of lithological Unit II (lower Miocene-upper Palaeocene) and the black claystones (Unit III). Due to their reverse nature, most of the fault zones observed are possibly related to gravitydriven sliding that occurred during lithification of these sediments (Mascle et al. 1996). Breakout occurrences are numerous between 540 and 700 mbsf. Breakouts depend on the sediment type (no breakouts appear in the nontectonized section of Hole 959D between 700 and 745 mbsf) and the average strike of the breakout axes is perpendicular to the maximum horizontal compressive stress east-northeast west-southwest (Mascle et al. 1996). Figure 15 shows a break-out occurrence in Hole 959D given by a conductive (open) fracture on the FMS image. The strike of the breakout axis is north-south.

DEVELOPMENT OF A TRANSFORM MARGIN

387

Fig. 15. FMS image showing a break-out ocurrence in Hole 959D. The conductive open fracture observed here is about 35 to 40 cm long with a strike aproximately N-S.

Discussion The earliest record of sedimentation recovered during Leg 159 is of Albian siliciclastic sequences, which are believed to have formed in deep, tectonically generated basins. Mascle et al. (1996) show that this section is characterized by a progression from intra-continental basins, comprising lacustrines sediments, to marine basins comprising both mixed siliciclastic and pelagic sediments. The cores from Sites 959 and 960 have a range of deformation styles that can be summarized by slumps, normal and reverse microfaults, microfolds and veins. Although sediments from many stratigraphic levels may show deformation, it is noteworthy that the most intense concentration of faults, veins and folds are found towards the end of each site.

Two abrupt downhole increases in deformation are observed at Sites 959 and 960. The first one occurs at 745 mbsf in Hole 959D, coinciding with an angular unconformity within Eocene sediments. The second one occurs at an unconformity between lower Cretaceous sediments in both Holes 959D and 960A. The effects of the deformation can be seen in the variation of the index properties and well log measurements. At Site 960 a major break in porosity is seen within the tectonized Lower Cretaceous sediments, a trend also reflected in the P-wave velocity. The low porosities and high velocities measured in this zone are attributed to the pervasive cementation and diagenesis affecting these sediments. At this site, the presence of a major unconformity is also observed between Cretaceous and Cenozoic, marked by an offset

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in porosity, density and velocity data at 200 mbsf. At site 959 there is a broad pattern of decreasing porosity downhole. Once again, the lowest core porosities are found in Albian strata (Unit V), and an increase to higher porosities is seen across the transition into the overlying carbonates (Unit IV), which are also less tectonized (Gon~alves & Ewert 1995). Two sharp breaks in porosity and velocity are observed between the lower Cretaceous sediments (1040 mbsf) and between Eocene claystones at 745 mbsf, representing an angular unconformity. Following the carbonate sedimentation after the late Santonian, the dominant tectonic setting for sedimentation is characterized as a passive margin. The tectonically controlled contrasts in sedimentation gave way to deepening of the basin and progressive submergence of the margin. Differentiation of the Deep Ivorian Basin and an increase in biosiliceous, pelagic and hemipelagic sedimentation was observed. The most characteristic aspects of this zone ares the gradual increase of porosity and decrease in velocity towards the top of the section within the porcellanites of lithologic subunit IIC. The Eocene age of this zone does not mark a major tectonic event; however, it does correlate with the rapid subsidence of the margin. Defo rm a tio n a l records At Site 959, bedding planes dip predominantly towards the northwest north-northwest and dips increase with depth. This is interpreted as the result of the steady subsidence of the Deep Ivorian Basin between Albian and early Miocene. In addition to direct core measurements, in situ structural measurements were obtained using FMS tools in both Holes 959D and 960C. The logged intervals cover from the lower Oligocene porcellanites (lithologic subunit IIC) to part of the upper Cretaceous to lower Palaeocene black claystones (Unit III) in Hole 959D; and the Eocene (lithologic subunit IIB) to the Turonian-upper Santonian limestone (subunit IVB) in Hole 960C. In Hole 959D, the bedding dips northwest, increasing 5~ to 25 ~ with depth, as expected from seismic data during pre-cruise surveys (Basile et al. 1996). In Hole 960C, beds also dip to the northwest but show no increase with depth. At both sites, dip and azimuths of the bedding exhibit important variations at decimetre to metre scales, interpreted as possible slump deposits (Mascle et al. 1996). Associated rotation axes for the slumps were calculated from sucessive bedding measurement, which mainly trend W - N W to N - N E at

both sites. The scattering of the rotation axes at Site 959 may reflect variations in the strike of the slope of the ridge with time or the interfering influence of uplift of the Marginal Ridge and subsidence of the Deep Ivorian Basin.

Conclusion The objective of this study was to evaluate: (1) the reliability of both types of measurements; (2) the extent to which the physical properties of the sediments are controlled by the tectonism; (3) the consistency of the structural features in the cores with downhole Formation MicroScanner (FMS) images which helped to interpret the development of the C6te d'Ivoire-Ghana margin. Comparison of in situ wireline log and core measured physical properties assessed the reliability of either data type. Because the two data types investigate different volumes of rock and use different techniques to measure the same physical properties, differences can be expected in the results. In Sites 959 and 960, however, both data types agree reasonably well. Differences occur mainly between porosity from the neutron log data and from cores, where the presence of clay affects the former. The velocity log is affected by hole conditions. This is the case of Hole 960A, where enlarged hole sections affected most of the velocity data. Porosity was the dominant control in acoustic properties at Site 959. Therefore, reliable determination of porosity was important in computing velocities. Compressional wave velocity, density, bulk modulus and rigidity are all closely linked to porosity and display the same gradual compaction effect and high frequency variation as porosity. A computed velocity log, based on measured core porosity, core density, core velocity and on the theoretical equations of Gassman (1951), is almost identical to the velocity log in Hole 959D. The only exception to the overall dominance of porosity in affecting acoustic properties is the increase in clay content within the black claystone (lithologic unit III). In the late Cretaceous, a thick sequence of organic-rich black claystone (high clay content) accumulated in the Deep Ivorian Basin, decreasing the density contrast and increasing the velocity contrast, compared with the effect of porosity alone. Also, a hiatus is seen to have affected the results in the Palaeocene. Empirical relations of velocity to porosity fit our data

DEVELOPMENT OF A TRANSFORM MARGIN poorly. Gassman's model (1951) had much greater success in providing a computed velocity log than the empirical equations. However, due to the regional variations in clay abundance and diagenesis, and also due to differences in sedimentation rate (hiatus), we conclude that a reliable method of estimating velocity for high porosity sediments needs a few measurements of velocity available to constrain the solution. FMS images helped us to identify the different structural features present between the two main deformation processes. It was also possible to reorientate cores from the images. Dispersion of dips and azimuths measured from the FMS images helped in identifying zones of microfaults. Break-outs and subvertical-to-vertical faults were also observed in the FMS data and allowed the identification of the direction of maximum stress present on this continental margin. This paper has shown that core-log integration can play an important role in understanding the evolution of this type of margin beyond that already discovered through remote geophysical surveying techniques. However, care must be taken in c o m p a r i n g both datasets, mainly because of the different measuring techniques and the different scales involved.

References ARCHIE, G. E. 1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions of the American Institute of Mineraloguy, Metallurgy and Petrelogy, 146, 54-63. BASILE, C., MASCLE, J., SAGE, F., LAMARCHE, G. PONTOISE, B. 1996. Pre-cruise and site surveys: a synthesis of marine geological and geophysical data on the C6te d'Ivoire-Ghana Transform Margin. In: MASCLE,J., LOHMANN,G. P., CLIFT, P. D. et al. (eds) Proceedings ODP Initial Reports, 159, College Station, TX (Ocean Drilling Program). CARLSON, R. L., GANGI, A. F. & SNOW, K. R. 1986. Empirical reflection traveltime vs. depth and velocity vs depth functions for deep-sea sediment column. J. Geophysical Research, 91, 8249-8266. CLAVIER, C., COATES, G. t~ DUMANOIR,J. 1977. The theoretical and experimental basis for 'Dual Water' model for the interpretation of shaley sands. Proceedings fo the Society of Petroleum

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Engineers., 52na Annual Fall Conference. Paper 6859. ELLIS, D. V. 1986. Neutron porosity logs: what do they measure? First Break, 4(3), 11-17. GASSNAN, F. 1951. Elastic waves through a packing of spheres. Geophysics, 16, 673~585. GON~ALVES,C. A. & EWERT, L. 1995. Sedimentary and structural relationship of the C6te d'IvoireGhana Transform M a r g i n ~ D P Leg 159: evidence from downhole logging measurements (abstract). LOS, 76, F597. HAMILTON, E. L. 1971. Prediction of in situ acoustic and elastic properties of marine sediments. Geophysics, 36, 225-284. 1979. Sound velocity gradients in marine sediments. Journal of Acoustic Association of America, 65, 909-922. JARRARD, R. D., DADEY, K. A. & BUSH, W. H. 1989. Velocity and density of sediments of Eirik Ridge, Labrador Sea: control by porosity and mineralogy. In. SRIVASTAVA, S. P., ARTHUR, M., CLEMENT, B. et al. (eds) Proceedings ODP Scientific Results 105, College Station, TX (Ocean Drilling Program). MASCLE, J., LOHMANN,G. P., CLIFT, P. D. et al. 1996. Proceedings ODP Initial Reports, 159: College Station, TX (Ocean Drilling Program). OCEAN DRILLING PROGRAM (ODP). 1991. Wireline logging manual, volumes 1-3, ODP (LDEO), Palisides, N.Y. PEZARD, P. A., LOVELL,M. A. & HISCOTT, R. N. 1992. Downhole electrical images in volcaniclastic sequences of the Izu-Bonin forearc basin, western Pacific. In: TAYLOR, B., FUJIOKA,K. et al. (eds) Proceedings ODP, Scientific Results, 126. College Station, TX (Ocean Drilling Program). RAYMER, L. L., HUNT, E. R. & GARDNER,J. S. 1980. An improved sonic transit time-to-porosity transform. Transactions SPWLA 21st Annual Logging Symposium, paper P. SCHLUMBERGER 1972. Log interpretation--Vol. I-Principles. Schlumberger Educational Service. New York, N.Y. 1989. Formation MicroScanner Image interpretation. Schlumberger Educational Service. New York, N.Y. THEYS, P. 1990. Log data acquisition and quality control. Editions Technip, Paris. WAXMAN, M. H. & SMITH, L. J. M. 1968. Electrical resistivity in oil-bearing shaley sands. Society of Petroleum Engineers Journal, 8, 107-122. WYLLIE, M. R. J., GREGORY,A. R. & GARDNER,L. W. 1956. Elastic wave velocities in heterogeneous and porous media. Geophysics, 21, 41 70.

Multi-scalar structure at D S D P / O D P Site 504, Costa Rica Rift, II: fracturing and alteration. An integrated study from core, downhole measurements and borehole wall images P. T A R T A R O T T I l, M. A Y A D I 2, P. A. P E Z A R D 2'3, C. L A V E R N E 3, & F. D. D E LAROUZIERE 2

1Dipartimento di Geologia, Paleontologia e Geofisica, Universit~ di Padova, via Giotto n.1, 1-35137 Padova, Italia 2 Laboratoire de Mesures en Forage, Institut M~diterranden de Technologie, Technopdle de Chdteau-Gombert , F-13451 Marseille Cedex 20, France 3 Laboratoire de P&rologie Magmatique, C N R S URA 1277, Facultd des Sciences et Techniques de Saint Jdrdme, Avenue Escadrille Normandie-Niemen, F- 13397 Marseille Cedex 20, France Abstract: We used a database derived from the integration of core material and geophysical downhole measurements in order to investigate the relationships between fracturing and alteration in the volcanic section of DSDP/ODP Hole 504B. The studied crustal section (from top of the basement to 1000 mbsf (metres below sea floor)) consists of low resistivity/ high porosity pillow lavas associated with breccias and rubble material, alternating with high resistivity/low porosity massive basalt flows. A positive correlation between DLL (Dual Laterolog)-derived porosity and occurrence of breccias in the core suggests that breccias more than fractures contribute to the electrical resistivity signal. A structural analysis performed from core suggests that most fractures and veins are steeply dipping, and may represent tectonic features or cracks due to contractional cooling of the crust, the latter being more abundant in pillows. Fractures and veins recorded on core tend to be clustered in massive units or thin flows. This result may derive from criteria adopted during structural measurements and must be taken with care. The natural radioactivity (GR) profile delineates two main alteration zones in the volcanic section: an oxidizing zone with increased potassium above, and a reducing one without K gain below. Most of the GR maxima are found to be correlated with celadonite-bearing alteration halos. GR minima are frequently located at the boundaries between domains of contrasting fracture orientation, where metasomatic reactions may have occurred due to contrasting permeability.

Hole 504B (Costa Rica Rift, Pacific Ocean) is the only bore-hole to penetrate the oceanic crust through the volcanic extrusives into the underlying sheeted dyke complex. For this reason it has become an important in situ reference section for the study of the physical and chemical structure of the oceanic crust. Drilling operations during seven DSDP/ODP cruises devoted to deepening Hole 504B attained an average recovery percentage of 29.8% in the volcanic section, fairly typical of DSDP/ODP basement holes but that makes the cored material poorly representative of the drilled crust. For this reason, lithological stratigraphy must necessarily be integrated with continuous log stratigraphy obtained by downhole geophysical measurements. In this paper we present the initial results of a study based on the integration of core (lithology,

structure, and mineralogy) and downhole geophysical data (electrical resistivity, natural gamma ray, dual laterolog fracture porosity, and borehole wall images recorded with the formation microscanner or 'FMS') in the uppermost volcanic section of DSDP/ODP Hole 504B, down to a depth of 1000 mbsf. By using a multi-scalar approach (i.e. from submillimetric to metric scale) we have focused our study on highly resistive massive basalts and intervals with the highest values of fracture porosity from downhole measurements, in order to investigate the relationships between fracturating and alteration in the upper oceanic crust. At Site 504, the oceanic crust shows the effect of heterogeneous alteration related to the circulation of seawater in the uppermost volcanic section and upwelling hydrothermal fluids in the transition zone and dykes (Alt et al. 1985,

TARTAROTTI,P., AYADI, M., PEZARD, P. A., LAVERNE, C. & DE LAROUZIERE,F. D. 1998. Multi-scalar structure at DSDP/ODP Site 504, Costa Rica Rift, II: fracturing and alteration. An integrated study from core, downhole measurements and borehole wall images In: HARVEY, P. K. & LOVELL,M. A. (eds) Core-Log Integration, Geological Society, London, Special Publications, 136, 391-412

391

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1986, 1989, 1993, 1996a, 1996b; CRRUST 1982; Cann et al. 1983; Dick et al. 1992). The upper 320m of the volcanic section (upper-pillow 'UPAZ' alteration zone; Alt et al. 1986; Laverne 1987) was affected by oxidizing 'sea floor weathering' occurring at high water-rock ratios and low temperatures (< 60~ that produced Fe-oxyhydroxide, saponite, celadonite, aragonite, phillipsite, and minor calcite. The lower portion of the volcanic section (lower-pillow 'LPAZ' alteration zone) is characterized by reactions at lower water-rock ratios and slightly higher temperatures (60 to 110~ that mainly produced saponite and pyrite as secondary minerals. The transition from oxidative seawater alteration (UPAZ) to a reducing environment (LPAZ) corresponds to a permeability barrier which separates two distinct hydrological regimes (Pezard & Anderson 1989; Pezard et al. 1992). Such a barrier is identified in the electrical resistivity profile by the resistive Unit 27 described from core as a massive flow (Cann et al. 1983). Late Ca-Na zeolites occur within the entire volcanic section, but they are particularly abundant around 550 mbsf, in the UPAZ. Zeolites are intrepreted as related to off-axis discharge of low-temperature evolved fluids (Alt et al. 1996b). The transition zone from pillows to dykes and the sheeted dykes were hydrothermally altered under greenschist facies conditions (greenschist facies 'GFAZ' alteration zone) with chlorite, actinolite, albite, titanite and epidote, followed by zeolite facies conditions. These two superimposed metamorphic conditions have been attributed to hydrothermal alteration that took place, respectively, on-axis at temperatures of 250~176 and off-axis at lower temperatures (150~176 The occurrence of early calcic plagioclase and hornblende in the deep dyke section suggests episodes with temperatures higher than 400~ This mineral assemblage has been interpreted as caused by an early alteration event characteristic of an axial midocean ridge reaction zone (Laverne et al. 1995; Vanko et al. 1996). Coring operations during seven DSDP/ODP drilling cruises at Hole 504B have been accompanied by a series of downhole geophysical measurements (e.g. acoustic, electrical resistivity, temperature, hole size, natural gamma and density) that provided a comprehensive and downward continuous database of physical properties for the upper oceanic crust (Anderson et al. 1982; Newmarket al. 1985; Becker et al. 1988; Dick et al. 1992 and refs. therein). Acoustic data suggest that the upper 100m of

the crustal section (i.e. Layer 2A) consist of highly porous and permeable volcanic rocks (Newmark et al. 1985), whilst the original porosity has been mostly sealed by secondary minerals in the underlying 500m (Layer 2B). The transition from volcanic rocks to sheeted dykes records an abrupt change in physical properties: acoustic and seismic velocities increase, whereas bulk permeability and porosity drop by orders of magnitude (Anderson et al. 1982; Detrick et al. 1995). This sharp geophysical boundary at the top of the sheeted dykes is not mirrored by petrological data that suggests a rather transitional boundary (Alt et al. 1986; Becker et al. 1988; Dick et al. 1992).

Basement lithostratigraphy The volcanic section drilled at Site 504 is capped by a 274.5 m-thick sedimentary layer and extends to a depth of about 846 mbsf (Cann et al. 1983; Adamson 1985). This 571.5m thick section, which includes the basement drilled during Leg 69, Leg 70 and the top 10m recovered during Leg 83 (Cann et al. 1983; Anderson et al. 1985) consists of intercalated pillow lavas, pillow breccias and hyaloclastites, massive units (flows or sills), thin flows, breccias, and minor dykes (Adamson 1985; Fig. 1). Massive units usually give long lengths of full-diameter cores, and may have a thickness of up to 25m, although a thickness of 15 to 17m is more common. Electrical resistivity data suggest that core recovery in this section is biased toward massive units, and give an estimate of the pillow basalt plus breccia percentage as high as 70% (Pezard et al. 1992; Ayadi et al. 1998). The transition zone from the volcanic section to the underlying sheeted dykes and massive units extends from 846 to 1055 mbsf. Dykes are recognized by the occurrence of a fine-grained/ chilled rock adjacent to a coarser host rock. The rocks of the transition zone are represented by pillows and dykes that are commonly fractured and brecciated and show a pervasive hydrothermal alteration (Alt et al. 1985, 1986, 1989). The top of the transition zone is characterized by more abundant pillows and breccias than its bottom, where dykes and massive units are more common. In the upper transition zone, rock alteration is very similar to that in the upper volcanic section. From 898 mbsf downward, alteration abrubtly changes and the rocks are more recrystallized. A stockwork-like sulfide mineralization (Honnorez et al. 1985) occurs between 900 and 920 mbsf, within the pillow/ dyke transition. This zone consists of highly fractured pillow lavas with abundant breccias

FRACTURING AND ALTERATION AT SITE 504

393

Fig. 1. Basement lithostratigraphy of the studied crustal section at DSDP/ODP Hole 504B with location of the main brecciated intervals (br). (Modified after Adamson 1985).

and a network of mineralized veins, mainly composed of quartz and sulfide.

Core observations Structural features (macro-scale) Before the O D P Legs 140 and 148 that drilled the deepest part of Hole 504B (Dick et al. 1992; Alt et al. t993) specialist structural studies were not standard procedure at this hole. Unpublished fracture data are reported in Adamson (1985). During DSDP Legs 69, 70, and 83, numbers of fractures were counted on oriented core in order to have an evaluation of the extent of fracturing in different units (Cann et al. 1983). However, no measurements of fracture and/or vein orientation were made during these cruises. According to such fracture density estimate, an

average of 1.82 fractures cm -1 were found in oriented core pieces. Pillow units have an average of 1.0 fracture every 2 cm or less while massive ones have an average of 1.0 fracture every 2 cm or more. The thickest massive flows have fractures every 3.5 to 4.5cm, with an apparent tendency for the fracture density to increase with depth. Slickensides have also been detected on a few fracture faces during D S D P Leg 69. More recently, detailed measurements on drill cores have been carried out in the volcanic section and transition zone. Structural data of the volcanic section of Hole 504B are reported by Alt et al. (1996c) who measured a total of 5280 veins (3424 veins in the upper volcanic section) for an average of 31.6 veins m -1. A structural study of the transition zone between 840 and 958.5 mbsf was presented by Agar

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P. TARTAROTTI E T AL.

(1990, 1991), although no orientation data of the structures were supplied. This interval is inferred to be a long-lived deformation zone likely to be related to a fault (Kinoshita et al. 1989; Becker et al. 1989; Agar 1990, 1991). The occurrence of breccias is well documented in the core descriptions from DSDP Legs 69, 70, and 83. During Legs 69 and 70, autoclastic breccias cemented by clays were recovered more frequently at the base of the hole (about 770 mbsf). This type of material is expected to be more abundant than indicated by core recovery (Fig. 1). Alt et al. (1996c) evaluated the amount of breccias in the volcanic section of Hole 504B to be as much as 9.2%. In the section drilled during Leg 83, highly brecciated material has been described as hyaloclastites in pillows and as rubble surfaces of massive flows (Anderson et al. 1985). However, a definite interpretation of the origin of some breccias (sedimentary vs. tectonic) is still lacking, often because the nature of a breccia in a drill core is uncertain and ambiguous (Agar 1994; Barany & Karson 1989; Alt et al. 1996c; Harper & Tartarotti 1996). We have focused our structural study on fractures, veins, and brecciated intervals. The structural dataset used in this paper is an unpublished log of fractures, veins, and breccias at hand-specimen scale, based on direct core observations made by P.T. and F.d.L. on a visit to the ODP West Coast Repository at La Jolla, CA. These data are complemented by downhole and continuous rock physical properties and FMS images (see Ayadi et al. 1998), and presented in order to study:

features are likely to be related to tectonic processes (although an origin due to contractional cooling cannot be completely ruled out). In that way, fracture and vein orientation data may be more reliable than those obtained from irregular structures. For this purpose, fractures that are clearly drilling-induced (e.g. disking fractures, petal-centreline fractures) were not counted. Fractures and veins showing typical patterns of contractional cooling (e.g. triple junction, T-intersections; Pollard & Aydin 1988; radial or concentric arrays, with respect to the curved chilled margin in pillows lavas; Fig. 2a) were avoided during measurements. We also did not count vein networks related to incipient brecciation and interpillow veins characterized by sinuous shape and cm-scale thickness, that clearly derive from filling of interstitial spaces between pillows. Fractures and veins were preferentially measured in relatively continuous cores without rubble zones. The counted fractures usually have apertures less than 1 mm. The measured veins range from less than 1 mm to 15mm in width, and are filled with several types of minerals. On the basis of the colour of vein infilling in hand specimen, veins were classified in one of the following categories: dark green, light green, red, black, and white. Under the microscope, green minerals were recognized as clay-minerals (Fig. 2b); black and red minerals are Fe-oxides and/or Fe-hydroxides, and finally white minerals may be zeolites, anhydrite, or Cacarbonate. Ca-carbonates (likely aragonite or calcite) occur as fibrous or blocky crystals (Tartarotti et al. 1996).

(i) the structure of the crust in relation to tectonic processes; (ii) the effects of fracturing, veining and brecciation on the bulk characteristics of the rock.

Breccias and rubble. Breccias and rubble are very abundant in the volcanic section. Alt et al. (1996c) indicate the presence of 6% breccia in the upper 320m of the volcanic section, and 19% breccia in the lower volcanic section. The occurrence of breccias in the core was carefully checked and positioned with respect to the lithostratigraphy (Fig. 1). Different types of breccias were identified on the basis of clast shapes and nature of the matrix. However, many breccias are texturally similar, and a distinction between types is not always clear. The first type of breccia is represented by hyaloclastites. This breccia is usually observed near pillow rims and consists of clasts coming from the various parts of the pillow (mainly glass shards and subvariolitic basalts). Clasts are cemented by green clay minerals and carbonate. A second type of breccia consists of angular clasts with the same c o m p o s i t i o n as the surrounding rock (Fig. 2c). In some cases, clasts

Fractures and veins. A total of 1112 macroscopic

fractures and veins were measured on 797 core pieces oriented for 'way-up' relative to the core barrel (taken as reference frame). We restrict the term fracture to open planar features without any mineral infilling, and the term vein to filled features. The total of fractures and veins has been filtered during data collection in order to: (i) include only naturally-formed features; (ii) consider, among these latter, only planar and continuous fractures and veins. We assume that fractures and veins with these

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can be pieced back together, and are surrounded by a matrix consisting of Ca-carbonate and/or clay minerals. When brecciation is incipient, breccias pass into complex vein networks cutting intact basalt. Similar breccias have been described in a nearby volcanic section at the ODP Hole 896A and interpreted as due to hydrofracturing (e.g. 'jigsaw-puzzle breccias', Alt et al. 1993; Harper & Tartarotti 1996). The third type is of uncertain origin. The clasts are made of glass and basalt, and are somewhat rounded. The matrix consists of clasts (ram to cm-scale) cemented by clay minerals. This breccia may be sedimentary in origin due to mass wasting along escarpments and slopes or, alternatively, may derive from the disaggregation of the 'jigsaw-puzzle'. Another type of breccia is encountered in the stockwork-like mineralization zone. In the stockwork zone, the pillows were affected by major brecciation interpreted to postdate early breccias and fractures (Agar 1990). This breccia is interpreted by Agar (1990) to be caused by hydrofracturing on the basis of the suspension of angular clasts in the matrix, but it differs from the 'jigsaw-puzzle' breccias in the nature of the matrix which consists of quartz, epidote, and sulfides. Finally, cataclastic zones have been observed in core coming from an inferred fault zone, at around 900 mbsf, and probably associated with shear zones (Fig. 2d). Rubble consists of loose fragments of rock, with sizes ranging from mm to cm. D a t a acquisition a n d correction procedure. Determining the orientation of observed structures in the core is problematic. The drilling process causes fracturing and rotation of core such that the relative rotation of any section or core piece may occur around the core axis. The structural study is limited by the fact that the core may only be oriented for 'way-up', and indication of azimuth is possible only if palaeomagnetic corrections are available. Consequently, the orientation of core must initially be made relative to a local reference frame, and then be corrected to true North and true vertical. The convention adopted in the present study for the measurement of azimuth and dip of fractures and veins is that introduced by the Ocean Drilling Program (ODP Leg 135: MacLeod et al. 1992; Parson et al. 1992; ODP Leg 140: Dick et al. 1992), and is illustrated in Fig. 3. The ODP cores are sectioned along the axis of the core. One half is stored as 'archive', the other is used for sampling ('working half). Fracture and vein orientations were measured in the

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archive half relative to local reference coordinates, i.e. the core barrel reference frame. The plane normal to the axis of the borehole is referred to as the horizontal plane. On this plane, a 360 ~ net is used with a pseudo-north (000 ~ at the bottom line of the working half (Fig. 3), i.e. with a pseudo-south (180 ~ at the bottom line of the archive half that we used for measuring. The split surface of the core, therefore, lies in a plane striking 090o-270 ~ and dipping vertically. Dip direction (azimuth) of the structural features in any core piece could be corrected to geographic coordinates using palaeomagnetic measurements, and dip values could be corrected to true vertical if the hole deviation to vertical is taken into account. Corrections of the dip direction values to geographic coordinates from palaeomagnetic measurements are not available for the upper portion of Hole 504B. Correction to vertical can be neglected because the hole deviation from vertical has been estimated to be generally under 5.5 ~ with values under 2.0 ~ from 600 to 1400 mbsf (Alt et al. 1993), which is within the accuracy in determining the dip angles on core. Depth values of fractures, veins and breccias were computed with the help of a computerized program proposed by Agrinier & Agrinier (1994). This program is based on a model assuming that the individual probability density of sampling during coring is uniform, and that the relative position of the rock pieces are preserved in the core. Depth values obtained by this method were compared with those obtained by the DSDP/ODP conventional system (Alt et al. 1993). A difference of the order of 10cm was evaluated between the two methods,

FRACTURING AND ALTERATION AT SITE 504

397

Fig. 4. Distribution of planes density (number of plane per metre of core) derived from cores (measured raw data). (a) Total core planes density. (b) Open fractures and veins density. (e) vein aperture.

which can be considered negligible and beyond the accuracy in determining depth in deep drill holes. In this study, 423 fractures and 689 veins were measured on vertically oriented core pieces, i.e. pieces that clearly could not have rotated top to bottom about a horizontal axis in the core liner. Fracture and vein orientation was obtained on core pieces by direct measurement of azimuth and dip by the use of a compass, extrapolating the structural feature to a planar surface in space, with an accuracy of 10 ~ Fracture and vein density (raw data) in the examined crustal section of Hole 504B are plotted in Fig. 4. The distribution of the total core planes (i.e. number of fractures + veins) is non-uniform down the hole, with the total plane density tending to be higher at those depths corresponding to the

highest recovery (Fig. 4a). This is because the measurements were aquired mostly in long and continuous cores. Generally, veins are more abundant than fractures (Fig. 4b). The raw data extracted from core (Fig. 4) are not representative of the drilled crustal section because the average core recovery is low and the selection used during measurements tends to select the structural features. In order to reduce the bias due to low recovery, a correction was applied to the raw data from core to compute a more precise fracture and vein density. The number of fractures/veins was computed for each m-long interval, then divided by the local recovery. As a given interval may be split between two different core sections, the computed recovery is obtained from:

398

P. TARTAROTTI ET AL. R' ( A B ) = A A ' (RN)+A'B (RN+I)

(1)

where R ' ( A B ) = c o m p u t e d recovery for the interval AB (1 metre-long), A A ' = p o r t i o n of AB belonging to core (N), A'B = portion of AB belonging to core (N + 1), RN = recovery of core (N), R N + 1= recovery of core (N + 1). Depth intervals without fracture frequency data refer either to cores with no recovery (compare recovery % with number of total planes in Fig. 4a) or to cores with no measurable structural features. A further correction was applied to the fracture/vein dataset in order to take into account the sampling bias due to the verticality of the drillhole. This correction is necessary because a vertical drill hole is more likely to encounter shallow-dipping cracks and veins than steeply dipping or vertical ones. The correction is a function of cos 0, where 0 is the dip angle (Newmark et al. 1985; Dick et al. 1992). Results Distribution of fractures and veins. The corrected data indicate that fracture and vein distribution is not uniform with depth (Fig. 5). The fracture population neither systematically increases nor decreases with depth. Instead, it tends to be clustered with local highs (i.e. > 50 fractures m -1) at specific depths, namely 280, 440, 580, 680, 720, 740, and 830 mbsf (Fig. 5 b). Most of these depths correspond to thin flows and massive units, for which relatively higher recovery percentages were obtained, except for cores at 440 mbsf, consisting of pillow basalts giving lower recovery (Fig. 5a; see also Fig. 1). At 580 and 680 mbsf, massive Unit 27 and 34 occur, respectively (Fig. 1). We can note that spikes of high fracture density occur almost regularly every 80 to 100 m, describing a sort of periodicity down the hole (Fig. 5). As with fractures, vein density varies cyclically down the hole (Fig. 5b): veins tend to cluster at certain depths, i.e. near 280, 360, 440, 540 (Unit 24), 640, 820, and 970 mbsf, partly corresponding to pillow or massive units. The highest vein densities do not correspond exactly with zones of intense fracturing. In fact, fracture density maxima appear to alternate with vein density maxima, especially from 400 mbsf down. Generally, high fracture and vein densities correspond to massive units or thin flows, which are sampled with higher recovery than pillows. Fracture and vein orientation. The histograms of corrected dip angle for fractures and veins are

Fig. 5. Distribution of planes density (number of plane per metre of core) derived from cores (corrected data). (a) Total core planes density and location of the main brecciated intervals and rubble asobserved on cores. (b) Open fractures and veins density. reported in Fig. 6. Open fractures show that steep fractures and steep veins dominate in the logged interval. The vein frequency is relatively higher than the fracture frequency for subhorizontal and intermediate (up to 60 ~) populations. Fracture and vein apertures. Fracture and vein apertures have been evaluated in the core by measuring the width normal to the planar fracture or vein. Open fractures commonly have apertures less than 1 ram. Veins exhibit the greatest thickness (8-15 ram) at 320 mbsf (massive flow), 397 mbsf (pillows), 907 mbsf (dyke), 912 mbsf (pillows), 978 mbsf (massive flow), and 981 mbsf (massive flow) (Fig. 4c). Distribution o f breccias and rubble. Breccia and rubble are very abundant in the drilled crustal

FRACTURING AND ALTERATION AT SITE 504

Fig. 6. Histograms of corrected dip angle of open fractures and veins derived from measurements on cores.

section occurring mainly within pillows and thin flows (Fig. 1). However, the total proportion of breccias based on visual core observation is likely under-estimated, because breccia is a delicate lithology and tends to be lost during drilling. Consequently, the distribution of breccias and rubble down the hole may reflect sampling bias due to under-estimation (Brewer et al. 1995). According to initial core descriptions (Cann et al. 1983; Anderson et al. 1985), breccias and rubble appear to be clustered at specific depths, and are most abundant at 340, 430, 480, 560, 650, 690-705, 780, and from 900 to 1000 mbsf (Fig. 5a). These depth intervals correspond to pillows and minor flows where a relatively low

399

Fig. 7. Distribution of secondary minerals along the studied section derived from petrographic observations. Symbols refer to the type of mineral occurrence in rocks. Only the most abundant mineral observed in thin section is reported. Zones of alteration under 'oxidative' and 'reducing' conditions are reported.

recovery was achieved (Fig. 5a). Rubble was recovered from 300 to 310, 561 to 615, 815 to 900 mbsf which correspond to pillows with a relatively low recovery (Fig. 5a). Mineralogy (micro-scale) We studied 268 thin sections of the recovered basalts for petrographic descriptions. We compiled a database of the alteration minerals from personal thin sections (data available from the authors) and from the D S D P / O D P collection

400

P. TARTAROTTI E T AL.

(personal observations and data reported in literature, e.g. Honnorez et al. 1983; Kurnosov et al. 1983; Noak et al. 1983). The studied thin sections are not fully representative of the penetrated interval because the average recovery was relatively low, and thin sections are derived from a selection of core samples. The distribution with depth of alteration minerals, as observed in thin section, is illustrated in Figure 7. This distribution refers to depth intervals at which the minerals represent the most abundant secondary phase in the studied thin section. The most common secondary minerals are: clay minerals (i.e. smectite, including mixed layer smectite-chlorite) and celadonite, zeolites, Fe-oxhydroxides, chlorite, prehnite, talc, quartz, actinolite, Fe-sulfides, and analcime. These minerals may replace primary minerals (e.g. olivine) and glass, or fill fractures, voids, vesicles and cracks (Fig. 2b). Smectite is the most abundant mineral in the studied section (270 mbsf to 1000 mbsf; Fig. 7), and occurs either in the rock groundmass or in veins. Zeolites (including both Na- and Ca-zeolites) are clustered between 528 and 572 mbsf, and between 900 and 1000 mbsf. Fe-oxhydroxides are the next most abundant minerals from 270 mbsf down to 600 mbsf, occurring either in the rock groundmass or as vein infilling. Celadonite is scarce and occurs only down to 650 mbsf. Celadonite was mainly observed in samples of pillows. Chlorite is present in the deepest part of the studied section, appearing at about 900 mbsf within samples from thin flows. Carbonate is scarce and does not occur below 600 mbsf. It was observed only in samples from massive basalts. Prehnite was detected in a few samples from 550 and 850 mbsf, at shallower depths than those reported in Alt et al. (1986). Quartz is present from 800 down to 1000 mbsf, and mainly occurs in pillow units. Actinolite appears in the deepest part of the studied section. Talc and Fe-sulfides are the most abundant mineral at around 700 mbsf. Analcime is scarce in samples from 600 mbsf. The K-rich minerals (i.e. celadonite, K-feldspar, phillipsite) are mostly located in the top part of the basement, down to 570 mbsf (Fig. 7). This zone was altered by large volumes of seawater, freely circulating through the uppermost volcanic pile, causing greater oxidation and alkali-enrichment of the rocks (Alt et al. 1996b). In contrast, deeper volcanic sections are characterized by more restricted circulation of seawater and evolved fluid compositions under more reducing conditions (Fig. 7). Chemical analyses of these minerals are reported by Honnorez et al. (1983) and Laverne (1987). K-

feldspar is very scarce (one occurrence). Phillipsite mainly occurs in veins and as glass replacement. Celadonite and celadonite-smectite mixtures are much more abundant than phillipsite and are restricted to black and red alteration halos which are typical of oceanic basalts altered at low temperatures in oxidizing conditions (Alt et al. 1996b; Belarouchi et al. 1996; Laverne et al. 1996).

Downhole measurements Downhole measurements of rock physical properties provide a continuous and m-scale description of crustal structures. The extensive downhole measurements program conducted at DSDP/ODP Site 504 over the years has produced a comprehensive dataset that may be compared with structural and petrographic data obtained from cores. Electrical

resistivity

Electrical resistivity data were recorded in Hole 504B with a lateral device, the Dual Laterolog (DLL) tool of Schlumberger. Lateral devices are largely influenced by anisotropy and provide accurate data at high resistivity values, such as those obtained in crystalline formations. The sensor was designed to provide, at different frequencies, two measurements of electrical resistivity often referred to as deep (LLd) and shallow (LLs) due to their respective horizontal penetration into the rock. Resistivity data recorded by the dual laterolog (DLL) resistivity probe during Legs 83, 111, and 148 in the upper part of the hole are extremely reproducible, and may be represented by the latest electrical resistivity profile (Fig. 8b). From this profile, an estimate of 'apparent' porosity was computed (Becker 1985; Becker et al. 1989; Pezard & Anderson 1989; Pezard 1990), in order to discriminate individual lithological units such as pillows and massive flows, and to constrain the large-scale morphology of the crust as synthetised in the following. From the highly porous and altered seismic Layers 2A and 2B to the sheeted dykes of Layer 2C, an increase of about two orders of magnitude in resistivity values was observed, i.e. from the average value of 10.0f~m in the pillows to the average value of 250.0 f~m in the dykes (Pezard & Anderson 1989). The resistivity profile reported in Fig. 8b shows the occurrence of relatively high resistivity layers corresponding to massive and thick (about 150 m) lithostratigraphic units as defined from core observations. They are Units 2D, 22,

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24, 27, 30, 34, 37, 64, and Unit 73 (Fig. 1) and correspond to depth intervals where the highest recovery percentages were obtained (Fig. 8a). Unit 2D is considered as the upper limit of an underpressured aquifer located within Layer 2A. One exception is massive Unit 9 that shows low resistivity values (Fig. 8b). Other massive and thin basaltic layers have lower resistivity than the thick massive units listed above. These thin units are Units 11, 17, 32, 44, 46, and Unit 49 (Figs 1 & 8b). In the resistivity profile of Fig. 8b, spikes of relatively high resistivity also occur at around 280, 400, 765, and 850 mbsf. These spikes correspond to thin flows (at 280, 400 and 765 mbsf) and to dykes (850 mbsf) as defined from core observations (Fig. 1). Another high resistivity unit of intrusive origin is located at 898 mbsf (Fig. 8b). This unit likely corresponds to DSDP Unit 57 that caps the stockwork-like section discovered during Leg 83 (Anderson et al. 1985). A comparison between the electrical resistivity profile and the mineralogical zonation in Hole 504B has suggested that certain low-porosity massive units appear to constitute either permeability barriers or alteration boundaries (Pezard & Anderson 1989). Porosity evaluation

Several estimates of porosity may be obtained from DLL electrical resistivity. A first estimate might be derived directly from the deep penetrating measurement (LEd) using Archie's Law (Archie 1942; Brace et al., 1965; Becker, 1985) and considered as representative of the 'total' porosity of the rock. A second estimate may be derived by accounting for surface conductivity due to the presence of clay minerals (Pezard 1990; Pezard et al. 1996; Revil et al. 1996), producing an estimate of the porous fraction where fluids are free to move in the basement. A third estimate may be derived from the difference between the two measurements (LLs and LEd) after correction for hole size effect (Pezard & Anderson 1990). Due to the tool geometry and strong focusing, the presence of sub-horizontal conductive features preferentially decreases the deep measurement more than the shallow one. In contrast, the deep resistivity measurement (LEd) is always higher than the shallow one (LLs) where fractures are subvertical. Thus, comparison of the two DLL measurements provides an evaluation of porosity in anisotropic media. The porosity estimate is a minimum because of a possible conflict between horizontal (h-DEE) and vertical (vDLL) structures. The effect of both horizontal

and vertical structures might cancel out, and only a minimum estimate of fracture porosity is obtained (t-DEE). Horizontal and vertical fracture porosity data as derived from electrical resistivity are reported in the profile of Fig. 8c. This profile represents an important tool for understanding the distribution of fractures orientation with depth, which may have an important role in the geometry of hydrological circulation. Fracture porosity data (FP) at Hole 504B (Newmarket al. 1985; Pezard & Anderson 1989, 1990; Pezard et al. 1992, 1996) suggest that Layer 2A is characterized by the highest porosity values, mainly related to the presence of horizontal fractures (horizontal fracture porosity - - HFP) down to 405 mbsf, below which a regime of mostly vertical fractures is inferred. The section of most intense fracturing is 30m thick and bound on top by Unit 2D (from 311 to 325 mbsf). This section corresponds to the under-pressured aquifer (Anderson & Zoback 1982; Anderson et al. 1985) that defines the upper limit of the main zone of water intake into the crust, although thinner permeable zones might be located above Unit 2D. From the top of Layer 2B (405 mbsf) downhole, vertical fractures dominate (vertical fracture porosity - - VFP) in DLL data. The lowest computed porosity (a fraction of a percent) corresponds to a thick lava flow in the extrusive section (Unit 27). Below this particular unit, Unit 28 corresponds to the only interval outside Layer 2A where intense subhorizontal fracturing was observed on BHTV images ( N e w m a r k e t al. 1985). In the uppermost part of the basement, massive Unit 2D (from 309.6 to 323 mbsf), records a low fracture porosity and separates an upper zone where horizontal fracturing dominates (from 279.7 to 293.3 mbsf) from a lower one where vertical fracturing dominates (i.e. the aquifer; Figs 8b,c). Above Unit 2D and in contact with it, a short interval of vertical fracturing occurs between 302.3 and 306.2 mbsf. Unit 2D is separated from the aquifer by a thin zone of high horizontal fracture porosity at 323 mbsf. The aquifer is the first important zone of vertical fracture porosity, extending from 323.9 to 333.1 mbsf. Below the aquifer, other zones of vertical fracture porosity alternate with zones of either generally low fracture porosity or horizontal fracture porosity (Fig. 8c). Thick massive units that show high resistivity have relatively low DEE-derived fracture porosity, with the exception of Unit 9 (Figs 8b,c). Thin massive, low resistivity units also have low fracture porosity, with the exception of Units 44,

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Fig. 9. Composite profiles of geophysical logs and mineralogy log from the studied section. (a) Recovery percentage. (b) Electrical resistivity, on the left, and DLL-derived fracture porosity, on the right. Zones from 1 to 10 are reported. (c) Natural radioactivity profile, on the left; distribution of K-rich minerals and secondary minerals derived from petrographic observation in thin sections, on the centre and right. (d) Distribution of red and black alteration halos derived from visual observation on cores and petrographic observations.

46, and 49 occurring near the inferred fault zone (Figs 8b,c). For the purpose of this study, the fracture porosity profile has been divided into a number of zones which record the highest values of vertical or horizontal fracture porosity (Table 1, Fig. 8c). These zones also often correspond to depth intervals where relatively low recovery percentages were obtained (Fig. 8a). These zones of high VFP are found to occur regularly downhole, about every 80 m, and to alternate with zones where low fracture porosity dominates (although isolated spikes of relatively high VFP occur at 373, 390, 656, 755 and 989 mbsf; Fig. 8c). The zones with low fracture porosity

mostly consist of massive units and thin flows (Table l; Fig. 8). F M S image analysis The Formation MicroScanner (FMS) creates a picture of the borehole wall by mapping its electrical conductance using an array of electrodes (Liithi & Banavar 1988). During ODP Leg 148 (Alt et al. 1993), FMS images were recorded over the entire length of Hole 504B basement, utilizing a device characterized by four padmounted electrodes (Pezard 1990). The images recorded by the FMS show c o n d u c t i v i t y changes, like those resulting from beds of a

FRACTURING AND ALTERATION AT SITE 504 different nature or from different fracture infilling (e.g. open spaces vs mineralized fractures). Data processing is required to convert the raw data into a colour-scale image representative of conductivity changes. The images are analysed by interpretative software in order to obtain a structural data set (e.g. fracture aperture, fracture orientation, etc.). FMS data processing and analysis in Hole 504B is described by Ayadi et al. (1988). In this paper, we are interested in the FMSderived plane density (Fig. Be). This profile shows a slight plane density decrease from the top of the basement down to 800 mbsf, and then an increase from 800 to 1100 mbsf. This increase is interpreted as a fault zone (Pezard et al. 1997; Ayadi et al. 1988). Palaeomagnetic data support this interpretation, indicating tilting of the volcanic section (Furuta & Levi 1983; Becker et al. 1988; Kinoshita et al. 1989). Above this faulted section, high FMS fracture density values are recorded at 440, 510, 570 mbsf, where core recovery is very low or zero (Fig. 8a). High plane density values from core are obtained at about 440 mbsf (Fig. 8e). N a t u r a l radioactivity

Gamma ray density logs were run in Hole 504B during DSDP/ODP Legs 83, l 1 l, and 148 together with other geophysical logs. During DSDP Leg 83, an active source neutron porosity log and a gamma-ray (GR)-density log were run from 274.5 to 1287.5 mbsf (Anderson et al. 1985). The active source nuclear log offers a method of calculating the quantity of hydroxyl minerals present over the entire well bore length. This method is based on the nuclear physics of neutron and gamma ray propagation and gives a measure of the amount of alteration within the logged section. The standard GR data, including the thorium, potassium, and uranium activity are reported in Fig. 9c. The natural radioactivity signal shows an overall intermittent trend down the hole. However, the highest GR values (as high as 8 G A p i = 8 x 109 Api) occur within the upper pillow alteration zone (UPAZ) of Hole 504B. From 570 mbsf down, an overall decrease of GR values is obtained. The upper part of the profile is also characterized by the occurrence of K-rich minerals, as detected from petrographic observations (Fig. 9c). Compared with the DLL-derived FP profile (Fig. 9b), natural radioactivity maxima correspond to both VFP highs (e.g. Zone 2, the aquifer) and VFP lows (e.g. at 400, 460, 550, and 625 mbsf), suggesting a weak correlation.

405

Similarly, natural radioactivity minima corresponds to both VFP highs and VFP lows (Fig. 9c). The lowest GR values are commonly located at the boundary between domains of contrasting fracture orientation, e.g. at 322.4 mbsf (top of the aquifer), 405 mbsf (top of Zone 3), 467.5 mbsf (top of Zone 4), 510.2 mbsf (bottom of Zone 4), 544.66 mbsf.

Interpretation of core-log FMS data The main characteristics of Hole 504B core and log data (Figs 8 & 9) include: (i) the absence of overwhelming large-scale trends; (ii) a step-like decrease in natural radioactivity between the UPAZ and LPAZ (Fig. 9c); (iii) a tendency of data highs to cluster (e.g. zones of intense fracturing/veining, high fracture porosity and K-content, hence natural radioactivity). In order to study the effect of fracturing and alteration on the physical properties of the upper oceanic crust, we present a detailed comparison of core and downhole geophysical data. P h y s i c a l properties a n d lithology

The relationship between geophysical properties and lithology of the upper crust in Hole 504B may be inferred by comparing geophysical profiles and basement lithostratigraphy, or FMS and core plane densities (Figs 1 & 8). Some massive basaltic units are identified by relatively high resistivity (Fig. 8b) and by low fracture porosity (Fig. 8c). One exception is Unit 9 that shows low resistivity and relatively high fracture porosity. This unit consists of massive pillow basalts with abundant red alteration halos (Core 20), and of brecciated pillow lava (Core 21). On the other hand, other massive units have low resistivity and low fracture porosity (with the exception of Units 44, 46, and 49 that show high fracture porosity). These units are thinner than the high resistivity units (Fig. 1). Massive units commonly give long and continuous cores, as testified by relatively high recovery (Fig. 8a). Low resistivity/high FP zones commonly correspond to relatively low recovery percentages (Fig. 8a,b,c). Crustal sections characterized by the highest FP (Zones 1 to 10; Fig. 8c) consist of pillows with minor flows. The occurrence of horizontal or sub-horizontal lava flows may locally affect the geometry of fracture orientation, as in Zone 1 where HFP prevails, in

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contrast with other zones which are characterized by high VFP values. Zone 2 corresponds to the aquifer and mainly consists of pillows with abundant breccias and rubble. This interval represents an extensively fractured and brecciated section, as suggested by the presence of brecciated samples (Table 1). Zone 3 is characterized by two main peaks of VFP. In this interval, a very low core recovery (from 10 to 21%) was obtained, probably due to the abundant breccias. The VFP spikes at 411 and 419 mbsf likely also corresponds to brecciated levels, as suggested by the occurrence of breccias at about 407 mbsf and 421 mbsf in cores 19R-2 and 21R-1 (according to the DSDP nomenclature), respectively. This pillowed section, extending from core 19R-1 to the top of core 21R1, includes a massive layer (core 20R-l, massive Unit 9?), interpreted as a probable single cooling unit (Cann et al. 1983) that may account for the VFP 'low' located at 413.9 mbsf between two VFP spikes (Fig. 8c). Zone 4 corresponds to a section consisting of pillows and breccias. The basalts are frequently cut by a network of veins filled with dark green minerals (smectites) which may be associated with breccias. VFP peaks and brecciated levels appear as related (i.e. VFP spikes located at 478, 480, and 485 mbsf related to breccias in cores 28R-2, 28R-3, 29R-1, respectively). From core 29R-1 down, a more massive basalt recovered in core 29R-2 can be related to the FP low located at about 490 mbsf. No core was recovered down to core 32R-1 (507.5 mbsf). This gap corresponds to an interval with VFP peaks at 498 and 505 mbsf. Zone 5 is a long section (from 564 to 622 mbsf) consisting of pillows and breccias in the upper part, massive basalts in the middle, and breccias in the lower part. Breccias are also reported in this depth interval by Alt et al. (1996c). Zone 5 includes a highly fractured zone, located at 570 mbsf, as inferred by FMS data (Pezard et al. 1997; Ayadi et al. 1988). Massive Unit 27 is located in the middle of Zone 5 (below the fractured zone at 570 mbsf) and is characterized by low VFP values. Two VFP spikes at 569.35 and 617.2 mbsf may account for the occurrence of rubbles and breccias in cores 38R-2 and 44R-2, respectively. Zone 6 is made of massive basalts in the upper part, and pillows with breccias in the remaining part. The two VFP peaks occurring in this interval may be explained by the presence of breccias in cores 53R-1, 54R-1, and 56R-1. In these cores, brecciated basalts are frequently associated with pillows intersected by networks of veins filled with smectite. Zone 7 is a short crustal section located at about 730 mbsf and

consists mainly of massive basalts and pillows. Thus, the recorded values of VFP may not be directly correlated with lithology. Zone 8 corresponds to a VFP high at 780 mbsf, and consists of pillows and breccias. Breccias of core 64R-3 occurring at 778.0 mbsf may be correlated with the high porosity values in this interval. Zone 9, corresponding to the top part of the main fault zone met in Hole 504B, is characterized by pervasive VFP, with spikes as high as 8.3 %. This basement section is made of pillow lavas frequently cut by veins arranged in networks, breccias and minor massive basalts, sometimes associated with breccias. Massive units (scarce), alternating with pillows and breccias may account for the low values of VFP (823.8 mbsf). From Zone 9 down, VFP values remain relatively low ( < 1 . 0 % porosity). Zone 10 represents one of the few zones with non zero VFP values. The VFP high corresponds to brecciated intervals within the 'stockwork-like' section, where rocks mainly consist of altered pillows commonly cut by veins or vein networks. In conclusion, it appears that the crustal zones at Hole 504B characterized by the highest FP and lowest resistivity correspond to the most brecciated pillow lavas units, which generally also produces the lowest recovery values. That probably means that breccia and rubble strongly affect the electrical signal during geophysical logging. This interpretation is corroborated by data of core observations in the volcanic section of Hole 504B reported by Alt et al. (1996c) that indicate the occurrence of breccias at interval depths corresponding to Zones 2, 3, 4, 5, 6, 7, 8, and 9. Physical properties and fracturing

The density distributions of fractures and veins from cores (Fig. 8d, e) show that peaks of fracturation mostly correspond to thin basaltic flows and massive units, i.e. to high resistivity and low porosity units (Fig. 8b, c) for which relatively high recovery percentages were obtained (Fig. 8a). Thick massive units record high fracture and/or vein densities, with the exception of massive Unit 30 (Fig. 8b,d). This unit consists of massive basalt with breccias in Core section 47R-2. Thinner massive units also show peaks of high fracture/vein density, with the exception of Unit 32, consisting of massive basalt with breccias at Core sections 48R-1 and 48R-2. This result is apparently contradictory, but in fact reflects the criteria applied during structural analysis in cores, i.e. to select only planar and continuous fractures/veins and to avoid drillinginduced fractures or those with triple junction

FRACTURING A N D ALTERATION AT SITE 504

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and T-intersection patterns. The core planes density shows that density highs occur every 80 to 100m, suggesting the occurrence o f evenly spaced zones of fractures. This spatial periodicity may reflect the episodic character o f lava

eruptions during crustal accretion. In massive Units 2D, 24, 27, and 34 the total core planes orientation (dip angle) show that steep planes prevail in all such intervals (Fig. 10). This result is comparable with the orienta-

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tion of FMS planes, although in Units 2D, 24 and 34 sub-horizontal planes are also frequent (Fig. 10). The FMS planes density profile shows the presence of two intervals that appear to be more fractured than the rest of the basement. The lower interval is located between 800 and 1100 mbsf and corresponds to the main fault zone (Fig. Be). This zone is also characterized by high FP values (Fig. 8c). The upper interval is located between 400 and 575 mbsf, and also corresponds to a highly fractured zone (Ayadi et al. 1998) characterized by low resistivity and high FP (Fig. 8b,c). P h y s i c a l p r o p e r t i e s a n d alteration

The distribution of secondary minerals in the volcanics of Hole 504B is here compared to geophysical profiles, namely with FP and GR (Figs 7, 8 & 9). Two main observations can be made. Firstly, the distribution of zeolites is concentrated between 528 and 572 mbsf, and between 900 and 1000 mbsf. In the bottom 100m of the studied section, zeolites and prehnite occur together with actinolite, epidote, quartz, chlorite (Fig. 7). The two depth intervals of 528-572 mbsf and 900-1000 mbsf are located close to two highly fractured zones, one between 400 and 575 mbsf and the other between 800 and 1100 mbsf. Rocks at around 575 mbsf are characterized by high VFP, high FMS planes density and by the occurrence of breccias (Fig. 8). In the second interval (from 900 to 1000 mbsf) VFP and FMS planes density increases towards the bottom while breccias are ubiquitous (Fig. 8). These observations suggest that such intervals are, at least in part, highly porous and fractured zones, or fault zones that may represent preferential conduits for fluid circulation (Nehlig & Juteau 1988). Zeolites are interpreted as deriving from low-T evolved fluids, due to late off-axis hydrothermal processes (Alt et al. 1996b). The occurrence of zeolites together with actinolite, chlorite, epidote, quartz (i.e. minerals of the greenschist facies) between 900 and 1000 mbsf suggests that this crustal zone underwent successive stages of fracturing/faulting and mineralization due to circulating fluids, as suggested by Alt et al. (1986) and Agar (1990, 1991). Secondly, there is not any strong correlation between GR and fracture porosity (Fig. 9). GR minima, however, are frequently located at the boundaries between domains of contrasting FP, i.e. contrasting fracture orientation. Along such boundaries, metasomatic rections, e.g. leaching of alkalis, may have occurred due to contrasting

permeability (Rose 1995), thus explaining the GR decrease (Fig. 9b,c). Variations in natural radioactivity (GR) downhole should be related to inhomogeneities in the extent and type of basalt alteration, which in turn may be affected by the distribution of fracture porosity in the crust. For this reason, the downhole log has been integrated with mineralogy, as collected from core and thin section observations. In Fig. 9c we have plotted the occurrence of K-bearing minerals (including celadonite, phillipsite, and K-feldspar) and of all secondary minerals in order to compensate for the bias due to under-sampled or even unsampled sections (e.g. from 490 to 507 mbsf). When comparing the natural radioactivity (GR) and K-bearing minerals logs, correlations at two different scales may be envisaged: (1) at a large scale, the distinction between the UPAZ and the LPAZ is clearly observed in the GR, with a relatively sharp boundary at 570 mbsf, at the top of Unit 27. This depth corresponds to the base of the highly fractured zone inferred by Ayadi et al. (1998). In the LPAZ, the GR signal is relatively smooth and low, becoming smoother with depth. In the LPAZ, the average GR value is on the order of 2.5 GAPI, with only two peaks higher than 5.0 GAPI. The GR signal is much more irregular in the UPAZ, and several depth intervals have values higher than 5.0 GAPI. This difference in the background noise can be partly explained by the common occurrence of Fe-hydroxides in the UPAZ, while these are absent in the LPAZ (Fig. 9 d). Even if the K-content of these minerals is low, it could affect the GR signal. (2) At a smaller scale, it appears that the GR peaks of the UPAZ do not exactly match the occurrence of K-minerals. If we consider the occurrence of K-bearing minerals, we have seen that phillipsite mainly fills veins and replaces glass. Celadonite and celadonite-smectite mixture are much more abundant than phillipsite and occur in red and black alteration halos. Such oxidized halos are parallel to fractures and exposed surfaces. The grey-coloured internal part of the samples do not contain any celadonite but only saponite, which has low potassium content. Recent logging of Hole 504B drill core reveals that red halos comprise at least 27% of the upper volcanic section (Alt et al. 1996a). The distribution of red and black halos (percentage of alteration halos for each core interval; Alt et al. 1996a) is

FRACTURING AND ALTERATION AT SITE 504 reported in Fig. 9c. The highest percentage of red halos does not perfectly correlate with the GR signals. However, in some cases (e.g. from 310 to 315 mbsf; from 510 to 585 mbsf) the correlation is good. Thus, it is possible that many GR peaks correspond to zones where alteration halos occur. The orientation of halos in space with respect to the drill hole (e. g. the logging tool) may influence the signal shape and intensity. Furthermore, the average low recovery percentage obtained in Hole 504B and the selection made during sampling may prevent a continuous correlation between core and logging data downhole. As a conclusion, the natural radioactivity log clearly reflects two different types of low temperature alteration undergone by the effusive section of Hole 504B: (1) the oxidizing type with K-uptake in the UPAZ; (2) the reducing type, with only slight gain of K in the LPAZ. Most of the GR peaks in the UPAZ are probably correlated to the occurrence of celadonite-bearing alteration halos, but the poor recovery and heterogeneity of alteration effects may explain why this correlation is not visible along the entire section.

Conclusions A set of geophysical logs (electrical resistivity measurements and derived fracture porosity, natural radioactivity, and fractures mapped from high-resolution electrical images) carried out in D S D P / O D P Hole 504B have been compared with lithological and mineralogical data as determined in rock cores and in thin sections. By integrating core and log data, we analysed the structure of the upper oceanic crust from the m to the sub-mm scale. This multiscalar study points out the following results. The upper oceanic crust in Hole 504B (from top of the basement to 1000 mbsf) consists of low resistivity-high porosity layers, mostly corresponding to pillow lavas associated with breccias and rubble material, alternating with high resistivity-low porosity layers, which mainly correspond to massive flows. The high fracture porosity signal (mainly vertical) correlates well with the occurrence of breccias in pillows. This means that breccia may strongly affect the electrical signal during downhole log. The mineral composition of clasts and matrix in

409

breccias should be tested in order to check its influence on signals. The most porous units are encountered about every 80m and may be interpreted as tectonized zones (e.g. highly fractured zones, cataclastic zones, and/or fault zones; see also Ayadi et al. 1998). Many massive units in the studied section are characterized by high resistivity and low FP. Units with such features are generally thick (100 m scale). They are intersected by fractures and veins, as attested by core observations. However, such fracture and vein densities do not seem to contribute much to the DLL-derived fracture porosity. Spikes of high fracture/vein density occur every 80 to 100m, pointing out the presence of fracture spacing likely related to the episodic character of lava eruptions during crustal accretion. Fractures and veins in massive units are mostly steeply dipping and may be related to contractional cooling effects (Lister 1974) and/or to the regional tectonic stress field. Massive Unit 27 appears to be less porous and fractured than other massive units. This result confirms that Unit 27 may represent a permeability barrier which separates two different hydrological domains in the upper crust, as suggested by Pezard & Anderson (1989). In contrast to thick massive units, thin (< 100m thickness) massive units are characterized by relatively low resistivity and low fracture porosity. They are also cut by fractures and veins. It is possible that different electrical resistivity values are caused in this case by different thickness of massive layers. Two zones located between 400 and 575 mbsf and between 800 and 1100 mbsf, respectively, are characterized by low resistivity, high fracture porosity, high core and FMS planes density, and by the occurrence of breccias. The upper zone is interpreted as a highly fractured zone (Ayadi et al. 1998). It is characterized by a high concentration of Ca- and Na-zeolites. This depth interval is also located at the base of the UPAZ, as pointed out in GR data. The deeper zone corresponds to the main fault inferred by Becker et al. (1988), Kinoshita et al. (1989), Furuta & Levi (1983), Pezard et al. (1997), and Ayadi et al. (1998). This zone is characterized by the occurrence of zeolites, prehnite and actinolite + chlorite, i.e. low- and high-T minerals. In this fault zone, the alteration has been so intense that the mechanical and physical properties are found to be modified, especially between 900 and 1050 mbsf (Pariso & Johnson 1991; Pezard et al. 1997). These observations suggest that the morphology and the structural setting, e.g. fracture density/orientation, brecciation, and faulting of the oceanic crust strongly affect the

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geometry of fluid circulation and distribution of alteration. In particular, fault zones may be associated with episodic fracturing and mineralization under different T-conditions during accretion processes, both on- and off-axis. We are greatful to J. Bode and S. Prinz of the ODP West Coast Repository in La Jolla (CA) for their help during our work on cores. Two anonymous reviewers provided significant improvements to the paper. This research was supported by the ODP support program of CNR in Italy (to Paola) and by the 'Geosciences Marines' ODP support program of CNRS in France (to Mariem, Christine, Philippe and Francois-Dominique).

References ADAMSON, A. C. 1985. Basement lithostratigraphy, Deep Sea Drilling Project Hole 504B. In: ANDERSON, R. N., HONNOREZ, J., BECKER, K. et al. (eds) Initial Reports of the DSDP, 83, Washington, (US. Government Printing Office), 121-127. AGAR, S. M. 1990. Fracture evolution in the upper ocean crust: evidence from DSDP hole 504B. In: KNIPE, R. J. & RUTTEd, E. H. (eds). DeJormation Mechanisms, Rheology and Tectonics, Geological Society, London, Special Publications, 54, 41-50. - 1991. Microstructural evolution of a deformation zone in the upper oceanic crust: evidence from DSDP Hole 504B. Journal o f Geodynamics, 13, 119-140. 1994. Rheological evolution of the oceanic crust: a microstructural view. Journal of Geophysical Research, 99, 3175-3200. AGRINIER, P. & AGRINIER, B. 1994. A p r o p o s de la conaissance de la profondeur fi laquelle vos 8chantillons sont collect6s dans les forages. Comptes rendus de l' AcadSmie des Sciences, Paris, t. 318, 1615-1622. ALT, J. C., LAVERNE, C. & MUEHLENBACHS,K. 1985. Alteration of the upper oceanic crust: mineralogy and processes in DSDP Hole 504B, Leg 83. In: ANDERSON, R. N., HONNOREZ,J., BECKER,K. et al. (eds) Initial Reports of the DSDP, 83, Washington, US Government Printing Office, 217 248. , HONNOREZ,J., LAVERNE,C. & EMMERMANN,R. 1986. Hydrothermal alteration of a 1-km section through the upper oceanic crust, Deep Sea Drilling Project Hole 504B: The mineralogy, chemistry and evolution of basalt-seawater interactions. Journal of Geophysical Research, 8 0 , 217 229. , ANDERSON, T. F., BONNELL, L. & MUEHLENBACHS, K. 1989. The mineralogy, chemistry and stable isotopic composition of hydrothermally altered sheeted dikes, DSDP Hole 504B, Leg 111. In: BECKER, K., SAKA1, H. et al. (eds) Proceeding of the Ocean Drilling Program, Scientific Results, 111, 27-40. - - , KINOSHITA, H., STOKKING, L. B. et al. 1993. Proceeding of the ODP, Initial Reports, 148, College Station, TX (Ocean Drilling Program).

& MICHAEL, P. J. 1996a. Proceedings of the ODP, Scientific Results, 148, College Station, TX (Ocean Drilling Program). , LAVERNE, C., VANKO, D. A. et al. 1996b. Hydrothermal alteration of a section of upper oceanic crust in the Eastern Equatorial Pacific: a synthesis of results from Site 504 (DSDP Legs 69, 70, and 83, and ODP Legs 111,137, 140, and 148). In: ALT, J. C., KINOSHITA, H., STOKKING,L. B. & MICHAEL, P. J. Proceedings of the ODP, Scientific Results, 148, 417-434. , TEAGLE, D. A. H., LAVERNE, C. et al. 1996c. Ridge flank alteration of upper ocean crust in the Eastern Pacific: synthesis of results for volcanic rocks of Holes 504B and 896A. In: AcT, J. C., KINOSHITA,H., STOKKING,L. B. & MICHAEL,P. J. Proceedings of the ODP, Scientific Results, 148, 435-450. ANDERSON, R. N. & ZOBACK, M. D. 1982. Permeability, underpressures, and convection in the oceanic crust near the Costa Rica Rift. Journal of Geophysical Research, 87, 2860-2868. - - ,

HONNOREZ. J., BECKER,K. E T A L . 1982. DSDP HOLE 504B, THE FIRST REFERENCE SECTION OVER 1 KM THROUGH LAYER 2 OF THE OCEANIC CRUST. NATURE, 300, 589 594.

et al. 1985. Initial Reports of the DSDP', 83, Washington, (US Government Printing Office). ARCHIE, G. E. 1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Journal Petroleum Technology, 5, 1 8. AYADI, M., PEZARD, P. A., BRONNER, G., TARTAROTTI, P. & LAVERNE, C. 1998. Multi-scalar structure at DSDP/ODP Site 504, Costa Rica Rift, III: faulting and fluid circulation. Constraints from integration of FMS images, geophysical logs, and core data. This volume. BARANY, I. & KARSON, J. A. 1989. Basaltic breccias of the Clipperton fracture zone (east Pacific): sedimentation and tectonics in a fast-slipping oceanic transform. Geological Society o f America Bulletin, 101, 304-220. BECKER, K. 1985. Large-scale electrical resistivity and bulk density of the oceanic crust, DSDP Hole 504B, Costa Rica Rift. In: ANDERSON, R. N., HONNOREZ, J., BECKER, K. et al. (eds) Initial Reports of the DSDP, 83, Washington, (US. Government Printing Office), 419-482. , SAKAI, H. et al. 1988. Proceedings of the ODP, Initial Reports, 111, College Station, TX (Ocean Drilling Program). ,- - , - et al. 1989. Drilling deep into young oceanic crust, hole 504B, Costa Rica Rift. Reviews of Geophysics, 27, 79-102. BELAROUCHI, A., LAVERNE, C . , GENTE, P., AGRINIER, P., & COTTEN, J. 1996. Alt&ation fi basse temp6rature des basaltes en domaine ocbanique et comportement des terres rare: 6vidence fi partir des &hantillons dragu6s durant la mission SEADMA 1 'ride m~dio-atlantique, 20-24~ Bulletin de la Soci~t8 G~ologique de France, 167(4), 543-558. BRACE, W. F., ORANGE, A. S. & MADDEN, T. R. 1965. The effect of pressure on the electrical resistivity

FRACTURING AND ALTERATION AT SITE 504 of water-saturated crystalline rocks. Journal of Geophysical Research, 70, 5669-5678. BREWER, T. S., HARVEY, P. K., LOVELL, M. A. & WILmAMSON,G. 1995. Stratigraphy of the oceanic crust in ODP Hole 896A from FMS images. Scientific Drilling, 5, 87-92. CANN, J. R., LANGSETH,M. G., HONNOREZ,J. et al. 1983. Initial Reports of the DSDP, 69, Washington (U.S. Government Printing Office). CRRUST (Costa Rica Rift United Scientific Team) 1982. Geothermal regimes of the Costa Rica Rift, east Pacific, investigated by drilling, DSDP-IPOD Legs 68, 69, and 70. Geological Society of America Bulletin, 93, 862-875. DICK, H. J. B., ERZINGER, J., STOKKING, L. B. et al. 1992. Proceedings of the ODP, Initial Reports, 140, College Station, TX (Ocean Drilling Program). ERZINGER,J., BECKER,K., DICK, H. J. B., STOKKING,L. B. et aL 1995. Proceedings of the ODP, Scientific Results, 137/140, College Station, TX (Ocean Drilling Program). FURUTA, T. & LEVl, S. 1983. Basement palaeomagnetism of Hole 504B In: CANN,J. R., LANGSETH,M. G., HONNOREZ,J., VON HERZEN, R. P., WHITE, S. M. et al. Initial Reports of the DSDP, 69, Washington (US Government Printing Office), 697-703. HARPER, G. U. 8r TARTAROTTI, P. 1996. Structural evolution of upper Layer 2, Hole 896A. IN: ALT, J. C., KINOSHITA,H., STOKKING,L. B. & MICHAEL,P. J. Proceedings of the ODP, Scientific Results, 148, 245-259. HONNOREZ, J., ALT J. C., HONNOREZ-GUERSTEIN,B.M., LAVERNE,C., MUHELENBACHS,K., RuIz, J. & SALTZMAN,E. 1985. Stockwork-like sulfide mineralization in young oceanic crust: Deep Sea Drilling Project Hole 504B. In: ANDERSON, R. N., HONNOREZ,J., BECKER, K. et al. (eds) Initial Reports of the DSDP, 83, Washington, (U.S. Government Printing Office), 263-282. , LAVERNE, C., HUBBERTEN, H. W., EMMERMANN, R. & MUEHLENBACHS,K. 1983. Alteration processes of layer 2 basalts from DSDP Hole 504B, Costa Rica Rift. In: CANN,J. R., LANGSETH, M. G., HONNOREZ,J. et al. (eds) Initial Reports of the DSDP, 69, Washington, (U.S. Government Printing Office), 509-546. KINOSHITA, H., FURUTA, T. & PARISO, J. 1989. Downhole magnetic field measurements and paleomagnetism, Hole 504B, Costa Rica Ridge. IN: BECKER, K., SAKAI,H. et al. (eds) Proceedings of the ODP, Scientific Results, 111, 147-156. College Station, TX (Ocean Drilling Program). KURNOSOV,V. B., KHOLODKEVlCH,I. V., CHUBAROV,V. M. & SHEVCHENKO, A. YA. 1983. Secondary minerals in basalt from the Costa Rica Rift, Holes 501 and 504B, Deep Sea Drilling Project Legs 68, 69, and 70. In: CANN, J. R., LANGSETH, M. G., HONNOREZ,J. et al. Initial Reports of the DSDP, 69, Washington (U.S. Government Printing Office), 573-584. LAVERNE, C. 1987. Les Alt6rations des basaltes en domaine oc6anique: min6ralogie, p6trologie et

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g6ochimie d'un syst6me hydrothermal: le puits 504B, Pacifique oriental. Th6se, Univ. Aix-Marseille III. --, VANKO,D. A., TARTAROTTI,P. & ALT, J. C. 1995. Chemistry and geothermometry of secondary minerals from the deep sheeted dike complex, Hole 504B. In: ERZINGER,J., BECKER, K., DICK, H. J. B., STOKKING,L. B. et al. (eds) Proceedings of the ODP, Scientific Results, 137/140, 167-189. , BELAROUCHI, A. & HONNOREZ, J. 1996. Alteration mineralogy and chemistry of the upper oceanic crust from Hole 896A, Costa Rica Rift. In: ALT, J. C., KINOSHITA,H., STOKKING,L .B. & MICHAEL, P. J. (eds) Proceedings of the ODP, Scientific Results, 148, 151-170. LISTER, C. R. B. 1974. On the penetration of water into hot rock. Geophysical Journal o f the Royal Astronomical Society, 39, 465-509. LISTHI, S. M. • BANAVAR,J. R. 1988. Application of borehole images to three-dimensional geometric modeling of eolian sandstones reservoirs, Permian Rotliegende, North Sea. American Association o f Petroleum Geologists Bulletin, 72, 1074-1089. MACLEOD, C. J., PARSON, L. M., SAGER, W. W. & the ODP Leg 135 Scientific Party 1992. Identification of tectonic rotations in borehole by the integration of core information with Formation MicroScanner and Borehole Televiewer images. In: HURST, A., GRIFFITHS, C. M. & WORTHINGTON, P. F. (eds.). Geological applications of wireline logs //, Geological Society, Special Publications No. 65, 235-246. NEHLIG, P, & JUTEAU, T. 1988, Deep crustal seawater penetration and circulation at ocean ridges: Evidence from the Oman ophiolite. Marine Geology, 84, 209-228. NEWMARK, R. L., ANDERSON, R. N., MOOS, D. & ZOBACK,M. D. 1985. Sonic and ultrasonic logging of hole 504B and its implications for the structure, porosity, and stress regime of the upper 1 km of the oceanic crust. In: ANDERSON,R. N., HONNOREZ, J., BECKER,K. et al. (eds) Initial Report of the DSDP, 83, Washington, (U.S. Government Printing Office), 479 510. NOAK, Y., EMMERMANN, R. & HUBBERTEN, H.-W. 1983. Alteration in Site 501 (Leg 68) and Site 504 (Leg 69) basalts: preliminary results. In: C A n , J. R., LANGSETH, M. G., HONNOREZ, J. et aL (eds) Initial Reports of the DSDP, 69, Washington (U.S. Government Printing Office), 497-508. PARlSO, J. E. & JOHNSON, H. P. 1991. Alteration processes at Deep Sea Drilling Project/Ocean Drilling Program Hole 504B at the Costa Rica Rift: implications magnetization of oceanic crust. Journal o f Geophysical Research, 96, 11703-11722. PARSON, L. M., HAWKINS,J. W., ALLAN,J. et al. 1992. Proceeding of the ODP, Initial Reports, 135, College Station, TX (Ocean Drilling Program). PEZARD, P. A. 1990. Electrical properties of MORB, and implications for the structures of the oceanic crust at DSDP Site 504. Journal of Geophysical Research, 95, 9237-9264. - & ANDERSON, R. N. 1989. Morphology and alteration of the upper oceanic crust from in situ

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Index Page numbers in italics refer to Figures or Tables accuracy in measurement 43-4 acoustic images 250 aeolian sandstone resistivity log 49-50 aluminium and clay concentration 87-9 aluminium clay tool (ACT) 119, 134, 347 anhydrite in lithology logging 92 array sonic tool (SDT) 347-8 artificial neural networks (ANN) 110-12 role in fracture analysis method 112 results 112-13 see also neural networks As-Sarah sandstone analysis 11 porosity-permeability data 14 up-scaling 12 Atlantic Ocean floor basalt flow character 371-2 Cote d'Ivoire-Ghana margin transform method of analysis 377 results density 378-9 formation microscanner 382-6 P wave velocity 380-1 porosity 377-8 results discussed 387-8 lithostratigraphy 365-71 seaward dipping reflector series 363 Australian offshore Queensland Trough microresistivity imaging 264-5 sedimentary sequence 264 standard core plug measurement 265-6 Townville Trough microresistivity imaging 266-7 problems of scale 269-70 sedimentary sequence 266 standard core plug measurement 267-8 azimuthal resistivity imager (ARI) 281 Bahama Bank core-log integration methods 286-7 results 287-90 results discussed 292-4 basalts of Atlantic Ocean floor flow character 371-2 lithostratigraphy 365-71 subaerial features 363 biscuiting 278 Borrowdale Volcanic Group 98

rock properties causes of variation 107 mean formation velocity 100, 101, 104 Bouma sequence microresistivity image 264-5 box plots 5-6, 103, 104, 106 Brazil offshore porosity/permeability calculations 145-6 Vp/Vs relationship 141 Brent Group 41, 41-2, 251,257 see also Tarbert Formation Brockram Breccia 99, 100 bulk density methods of analysis 71,330, 377 results 378-9 see also density Bunter Sandstone complex impedance test 157

613Canalysis 200, 206 calcite dogger 41, 42 calcium and clay concentration 88-9 calibration in measurement 44-6 carbonate in lithology logging 91-2 Carboniferous Sandstone complex impedance test 157 lithofacies analysis methods 2 results 3-6 results discussed 6-7 cation exchange capacity 118, 121, 123 Ceara Rise 43 45 celadonite 319, 368, 400 cement Chaunoy Formation 206-10 effect on porosity/permeability 327 precipitation in presence of oil 327-8 methods of analysis petrography 332 wireline 330-2 results 332-3 results discussed 333-8 Chaunoy Formation cementation history 206-10 core analytical methods geochemical 199-200 petrophysical 198-9 core analytical results geochemical 205-6 petrophysical 202-5 core description 200-2 depositional setting 198

414

INDEX

chemical analysis, handling uncertainty in 54 analytical methods 56-7 combined errors 60-1 results discussed 61-2 random error analysis 57-8 sampling methods 56 systematic error analysis 58-60 technique 55-6 chemical modes 26 chlorite 118 CIPW norm 26 circumferential borehole imaging log (CIBL) drill-induced fractures 255-7 faulting and clay smearing 258-9 natural fractures 253-4 clay content effect of mineralogy on formation factor 121 effect of smearing 258-9 effect on complex impedance 154-6 effect on elastic rock properties 232-3 effect on permeability 225-6 and elemental analysis application 89-91 theory 87-9 and gamma ray log 84-6 and nuclear spectroscopy logs 86-8 and wireline logs 330-2 Cocos-Nazca spreading centre see Costa Rica Rift competency and core recovery 277-8 complementary parameter 160 complex impedance test 147 method of measurement 148-50 results 150-1 results discussed 152-6 sample description 157 complex resistivity 147 compressional (P) wave velocity effect of clays on 232 in ocean crust 312, 366, 367, 368, 370 methods of analysis 377 results 380-1 relation to S wave velocity 142-5 testing at Sellafield method 99-100 results 100-5 see also seismic anisotropy Compton scattering 1 conductivity 43, 218-19 induction log 164-5 mapping with FMS methods 378 results 382-6 in shales modelling 227 testing model 228-9 continent/ocean sediment balance see sedimentological input studies

core recovery problems 277-8 rate 129 core-log integration problems 273-4 application to ODP programme methods 286-7 results Bahama Bank 287 90 Costa Rica Rift 405-9 Mediterranean Sea 290-2 results discussed 292-4 core acquisition 277-8 depth assignment 275-7 handling errors 348-50 heave and stretch 278-9 improving accuracy 279-81 parametric differences 277 sample disparity 274-5 correlation coefficients 40 Costa Rica Rift DSDP/ODP programme 48 downhole logging equipment 346-8 error handling 348-50 procedure 345-6 drilling site and sampling 342-4 ocean crust alteration study alteration effects 391-2 core analysis methods bore correlation 396-8 breccia and rubble 394-6 fractures and veins 394 mineralogy 399-400 core analysis results breccia and rubble 398-9 fractures and veins 398 mineralogy 400 lithostratigraphy 392-3 logs electrical resistivity 400 2 formation microscanning image 404-5 gamma ray density 405 porosity 402-4 wireline-core integration 405-9 ocean crust fault study downhole measurements methods 312-15 results 316-17 fracture patterns 315-16 ocean crust fault patterns fluid circulation 319-20, 321,323 fractures 319, 320-1,321-3 ocean crust stratigraphy study ocean crust accretion 306-8 pillow lavas 298-9 structural setting 312 volcanic lithology 299-300 volcanic stratigraphy downhole methods

INDEX dual laterolog 300 formation microscanner 300-1 results 301-6 ocean crust volcanism study physical methods fracture density 350 porosity/permeability 350 resistivity 350, 351 summary of lithologies 358-9 visual methods breccia recognition 353-6 flow recognition 351-3 pillow recognition 356-8 Cote d'Ivoire-Ghana margin transform method of analysis 377 results density 378-9 formation microscanner 382-6 P wave velocity 380-1 porosity 377-8 results discussed 387-8 crack alignment numerical modelling 175-9 role in seismic anisotropy 173-4 method of measurement 174 results 179 results discussed 179-82 see also microcrack analysis cross plots in correlation 3-5 luminance v. bulk density 21, 22 luminance v. porosity 22 cross-scaling application of 12-13 defined 10-11 deep induction tool (ILD) resolving power 261 size of sample 274 degassing 278 density clay minerals 118 use in deconvolution 120-1 density logging techniques application of linear perturbation 162-3 through drill pipe 163-4 X-ray measurement 17 density logs Atlantic Ocean floor 366, 367, 368, 370 Cote d'Ivoire-Ghana transform margin methods of analysis 377 results 378-9 Chaunoy Formation 199, 202 Wessex Basin methods of analysis 71, 74 results 72, 73 results discussed 78 density-porosity cross plot 105

415 density-velocity correlation, basalt 369 depth recording problems in logging and drilling 275-7 differential strain analysis method 186-7 results 188-90 results discussed microcrack system 193-4 stress orientation 192-3 theory 185-6 discontinuity analysis methods 108 results 108-9 doggers 41, 42 dolomite Chaunoy Formation 206-10 recognition in wireline logs 330-2 Dorset coast 65-7 density survey methods 71, 74 results 72, 73 results discussed 78 gamma ray survey methods 67-8, 71 results 69, 70 results discussed 74-8 drill pipe stretch 279 drilling depth v. wireline depth 275 DSDP Hole 504B see Costa Rica Rift DSDP Leg 81 basalt lithostratigraphy 366-8 dual laterolog (DLL) 300, 312, 347, 400-2 effective porosity model 216-17 association with total porosity model conductivity 218-19 fluid saturation 219 formation resistivity factor 218 grain density 217 porosity 217-18 shale volume fraction 216-17 role in quality assurance 219-22 'effective' property values 9 elastic constants 141-2, 145 electrical conductivity see conductivity electrical double layer 148 electrical formation factor 46-7 electrical image logs case study of Tarbert Formation 240-1 facies analysis 244-6 sedimentary history 246-7 sedimentary structures 242-3 compared with cores 238-9 equipment 237-8 sedimentary feature recognition 239-40 electrical resistivity log aeolian sandstone 49-50 Costa Rica Rift 400-2

416

INDEX

elemental analysis by chemical analysis 83-4 by ECS and RST 81-3 use in clay content measurement application 89-91 theory 87-9 elemental capture spectroscopy (ECS) 82-3 Eratosthenes Seamount core-log integration methods 286-7 results 290-2 results discussed 292-4

facies analysis Carboniferous sandstone methods 2 results 3-6 results discussed 6-7 use of electrical image logs 244-6 fault analysis CIBL image 258-9 FMI image 260 fractal analysis methods 109-10 results 110 at mid-ocean ridge downhole measurements methods 312-15 results 316-17 fracture patterns 315-16 ocean crust fault patterns fluid circulation 319-20, 321,323 fractures 319, 320-1,321-3 feldspar in lithology logging 92 field v. wireline measurements density methods 71, 74 results 72, 73 results discussed 78 gamma ray methods 67-8, 71 results 69, 70 results discussed 74-8 fluid circulation in ocean crust 319-20, 321,323 fluid inclusions oil in quartz 327-8 use in thermometry 199-200, 205-6 fluid phase in sampling 274 fluid saturation 219 foresets on electrical image logs 242-3 formation density log (FDL) correlation with X-ray luminance 17 database 17-18 methods 18-21 results 21-3 results discussed 23-4 formation evaluation 214-15

quality assurance 213, 219-22 formation factor/electrical resistivity 121, 123-4 modelling 126 formation micro imager (FMI) 237-8 cemented fractures recognition 260 drill-induced fracture imaging 254-5 fault imaging 260 natural fracture imaging 251-3 problems in interpretation 257-8 use in core-log integration 279-80 formation micro scanner (FMS) 48, 346-7 Costa Rica Rift study fracture analysis 404-5 fault analysis 313-15 volcanic stratigraphy 300-1 Japan Sea study 116, 120 modelling data 125 ocean crust analysis methods 378 resolving power 261,279 results 382-6 formation resistivity factor 218 Fourier series in data handling 269 Fourier transform infrared (FT-IR) 83 fractals role in up-scaling 107-8 methods 108 results 108-9 fracture analysis characterization 249-50 Costa Rica fracture/alteration study alteration effects 391-2 core analysis methods bore correlation 396-8 breccia and rubble 394-6 fractures and veins 394 mineralogy 399-400 core analysis results breccia and rubble 398-9 fractures and veins 398 mineralogy 400 density of fractures 350 lithostratigraphy 392-3 logs electrical resistivity 400-2 formation microscanning image 404-5 gamma ray density 405 porosity 402-4 wireline-core integration 405-9 identification cemented 260 drill induced 254-7 natural open 250-1 problems in reservoirs 249 use of artificial neural networks 110-12 method 112 results 112-13 frequency response in complex impedance 150-1

INDEX Fullbore formation micro imager (FMI) see formation micro imager Galapagos Rift see Costa Rica Rift gamma ray attenuation porosity evaluation (GRAPE) 287, 368, 370 gamma ray data, use in core-log integration 279, 280 gamma ray logs Atlantic Ocean floor 366, 367, 370 Costa Rica Rift study 313-15, 319, 405, 408 Chaunoy Formation 199 and clay content 84-6 Izu Bonnin arc 134 Wessex Basin methods of analysis 67-8, 71 results 69, 70 results discussed 74-8 see also natural gamma also spectral gamma gamma ray spectroscopy tool (GST) 119, 134 geochemical logging tool (GLT) 25, 378 analytical methods 56-7 handling combined errors 60-1 results discussed 61-2 random error analysis 57-8 sampling methods 56 size of sample 274 systematic error analysis 58-60 technique 55-6 use in ocean crust study 351 Gouy theory 148 grain density 122, 217 methods of analysis 377 results 378-9 gypsum in lithology logging 92 heave compensation 278 high resolution laterolog sonde (HALS) 281 hummocky cross stratification (HCS) on electrical image logs 243-4 illite properties 118 impedance see complex impedance induction log 164-5 inductively coupled plasma mass spectrometry (ICP-MS) 287 infrared spectroscopy see MINERALOG integrated images 250-1 integration of datasets 47-8 ionic double layer 148 iron and clay concentration 88-9 Izu Bonnin arc 47 lithology interpretation and neural networks 133 method 135-8 results 138

417 results discussed 138-40 core recovery 133-4 downhole data 134-5 Japan Sea see Oki Ridge Jurassic System see Brent Group also Kimmeridge Clay Formation kaolinite properties 118 KCI in drilling mud 2 Kimmeridge Clay Formation 66, 67, 72, 74, 78 laboratory data v. in situ logs density data in Wessex Basin 71-4 velocity data at Sellafield 102-5 ladder diagram 276 lavas at mid ocean ridge classification 300 volumes 307 limestone V p / V s 141 linear perturbation theory 159 theory applied to density log 162-3 lithodensity log 134 lithofacies analysis Carboniferous sandstone methods 2 results 3-6 results discussed 6-7 lithology classification in ODP bores 130 effects on core recovery 277-8 quantification 81, 89-90 anhydrite 92 carbonate 91-2 clay 90-1 sand 92 summary 92-3 Lochabriggs Sandstone 11 loess in Japan Sea 117 low frequency electrical resistivity log 119-20 luminance 17, 19 magma chamber behaviour at mid-ocean ridge 306-8 magnesium and clay concentration 88-9 Magnus Field regional setting 329 structure and stratigraphy 329-30 Magnus Sandstone Member diagenesis 330 oil emplacement and cementation study methods

418

INDEX

petrography 332 wireline 330-2 results 332-3 results discussed 333-8 sedimentology 330 major element analysis 25 measurement process evaluation direct v. indirect 40 quality 43-7 resolution 41-2 scale 42-3 Mediterranean Sea ODP study core-log integration methods 286-7 results 290-2 results discussed 292-4 mica in lithology logging 92 microconductivity images 250 Costa Rica Rift 300-1, 313-15 microcrack analysis DSA technique method 186-7 results 188-90 stress orientation 192-3 stress relief configuration 193-4 theory 185-6 USWS technique method 187-8 results 190-2 stress orientation 192-3 stress relief configuration 193-4 theory 187 microspherically focused log (MSFL) 263, 281 s e e also spherically focused log mid-ocean ridge features see Costa Rica Rift Milankovitch cycles 117 MINERALOG use in modal analysis 26 method 31 results 31-5 results discussed 35-7 mineralogy 83-4 Japan Sea core 118 ocean crust 399-400 use in modal analysis 26 methods 27 results 31-5 results discussed 35-7 modal analysis testing by experiment 25-6 method 28-31 results 31-5 results discussed 35-7 modular dynamic tool (MDT) 14 Morecambe Bay fluvial sandstone analysis 11-12 up-scaling 13, 14-15 multilayer preceptron 111-12

natural gamm ray log (CGR) 134 Japan Sea 119-20 modelling data 125 s e e also gamma ray logs natural gamma ray spectroscopy tool (NGT) 119, 346 natural radioactivity, Costa Rica Rift study 405, 408 neural networks in classification 131-3 methods 135-8 results 138 results discussed 138-40 see also artificial neural networks neutron log in salinity measurement 165-70 neutron porosity 330, 379-80 Atlantic Ocean floor 3 6 7 Chaunoy Formation 199, 202 Sellafield 105 Niggli norm 26 non-destructive imaging s e e X-ray scanning normative values 26 North Atlantic Volcanic Rifted Margin 363 North Sea Carboniferous sandstone lithofacies study methods 2 results 3-6 results discussed 6-7 Magnus Field 329 oil emplacement and cementation study methods 330-2 results 332-3 results discussed 333-8 sedimentology 330 structure and stratigraphy 329-30 Norwegian continental shelf reservoir fracture studies cementation 260 drill induced 254-7 fault recognition 258-60 identification 257-8 natural open 251-4 Nothe Grit Formation 72, 74 nuclear spectroscopy logs and clay content 86-8 ocean crust analyses s e e Costa Rica Rift ocean/continent sediment balance s e e sedimentological input studies Ocean Drilling Program (ODP) data quality assessment 43 lithological classification 130 use of neural networks 131-3 methods 135-8 results 138 results discussed 138-40 ODP Leg 128 s e e Oki Ridge ODP Leg 133 s e e Australia offshore ODP Leg 148 s e e Costa Rica Rift

INDEX ODP Leg 159 see Cote d'Ivoire-Ghana margin transform ODP Leg 160 see Mediterranean Sea ODP Leg 163 368-71 ODP Leg 166 see Bahama Bank oil emplacement in relation to cementation methods of analysis petrography 332 wireline 330-2 results 332-3 results discussed 333-8 in fluid inclusions 327-8 Oki Ridge 115-16 core data analysis description 116-17 mineralogy 118 core-log data forward modelling 124-6 downhole log analysis CGR 119-20 FMS 120 SFL 119 log analysis deconvolution 120-1 formation factor 121 sediment property prediction results cation exchange capacity 123 formation factor 123-4 grain density 122 mineral fractions 122-3 opaline silica 116-17, 122-3 ophiolites 297-8, 311 orientation, role in measurement 43 orthogonalization 159 Oseberg Syd Field 240-1 Tarbert Formation facies analysis 244-6 sedimentary history 246-7 sedimentary structures 242-3 Osmington Oolite 72, 74 Oxford Clay Formation 66, 74, 78 oxide analysis 25 P (compression) wave velocity effect of clays on 232 in ocean crust 312, 366, 367, 368, 370 methods of analysis 377 results 380-1 relation to S wave velocity 142-5 testing at Sellafield method 99-100 results 100-5 see also seismic anisotropy Paris Basin geological setting 197-8 Chaunoy Formation cementation history 206-10

419 core analytical methods geochemical 199-200 petrophysical 198-9 core analytical results geochemical 205-6 petrophysical 202-5 core description 200-2 depositional setting 198 Penrith Sandstone 42, 43 complex impedance test 157 cracks effects on seismic anisotropy methods 179 results 179-82 permeability Chaunoy Formation 198, 202, 205 effect of cement on 327 factors affecting 225-6 ocean crust 350 Penrith Sandstone 43 in shales 226 evaluating model 232-3 modelling 227-8 testing a model 229-32 up-scaling 9 permeability/porosity relationship 14, 145-6 permeability/resistivity studies cross-scaling 12 methods compared 14-15 summary of data 11-12 up-scaling 12-13 petrography Chaunoy Formation 200-1 Magnus Sandstone Member 332 use in modal analysis 26 method 31 results 31-5 results discussed 35-7 phillipsite 319, 400 photoelectric effect clay minerals 118 use in deconvolution 120-1 pillow lavas 298-9, 356-8 Piper Formation 18 plug-density method 14 pore geometry, effect of clays on 232 porosity Chaunoy Formation 198, 202, 204-5 effect of cement on 327 effect of clays on 232 from resistivity 317-18 ocean crust 350, 402-4 ocean sediments 377-8 Penrith Sandstone 43 St Bees Sandstone 105 see also effective porosity also total porosity porosity/permeability relationship 145-6 Portland Sand 67 potassium and gamma logs 1, 2, 3, 4, 5, 331

420

INDEX

precision in measurement 43-4 prehnite 400, 408 probe-microresistivity method 14 'pseudo' property values 9 QUAD 378 quality in measurement 43-7 assurance in formation evaluation 213, 219-22 quartz cement precipitation in presence of oil 327-8 methods of analysis petrography 332 wireline 330-2 results 332-3 results discussed 333-8 in lithology logging 92 physical properties 118 recognition in wireline logs 330-2 Queensland Trough microresistivity imaging 264-5 sedimentary sequence 264 standard core plug measurement 265-6 radioactivity, Costa Rica Rift method of analysis 312-13,405 results 319, 408 see also gamma ray logs random error, role of 55 in chemical analysis 57-8 ratio plots 6 remanence 370 reservoir saturation tool (RST) 82, 83 resistivity logs Atlantic Ocean crust 370 Costa Rica Rift 300, 312, 350, 351 Izu Bonnin arc 134 Japan Sea 119, 125 ODP Leg 133 studies Site 815 266-7 Site 823 264-5 problems of data integration 49-50, 279 problems of scale 261-3, 270 resistivity/permeability studies cross-scaling 12 methods compared 14-15 summary of data 11-12 up-scaling 12-13 resolution in measurement 41-2 rock mass rating (RMR) 113 Rockall Plateau 363, 367 S wave velocity effect of clays on 232 see also seismic anisotropy St Bees Evaporite 99

St Bees Sandstone 99 rock properties causes of variation 105 velocity 100, 101, 103 salinity effect on complex impedance 151-4 from nq,'utron logs 165-70 sample density, effect of 274 sample si:,e, incompatibility problems 274 sampling errors 53, 55 sampling strategy in coring 278 sand fraction in lithology logging 92 sandstone complex impedance properties methods 148-50 results 150-1 results discussed 152-6 samples 157 cracks effects on seismic anisotropy methods 179 results 179-82 saponite 319 saturation effect on complex impedance 154 scale effects in measurement 42-3 seaward dipping reflector series 363 sedimentary structures case study of Tarbert Formation 240-1 facies analysis 244-6 sedimentary history 246-7 structure interpretation 242-3 recognition on electrical images 239-40 sedimentological input studies in Japan Sea core data analysis description 116-17 mineralogy 118 downhole log analysis CGR 119-20 FMS 120 SFL 119 forward modelling 124-6 log analysis deconvolution 120-1 formation factor 121 sediment property prediction results cation exchange capacity 123 formation factor 123-4 grain density 122 mineral fractions 122-3 seismic anisotropy 173-4 experimental testing method 174 result 179 results discussed 179-82 numerical modelling 175-9 see also P waves also S waves Sellafield discontinuity analysis method 108

INDEX results 108-9 fault analysis method 109-10 results 110 fracture frequency analysis method 112 results 112-13 geological setting 98-9 rock mass rating 113 rock properties density 105 porosity 105 velocity 99-105 shale modelling conductivity 227 modelling permeability 227-8 testing models conductivity 228-9 permeability 229-32 unified permeability-conductivity model 226-7 shale volume fraction 21 6-17 Sherwood Sandstone 12, 13 silicon and clay concentration 88-9 slabbed core sampling problems 274 smectite 118 sonic log 134 sonic transit time 199, 202, 330 spacing population technique 108 spectral gamma ray (SGR) logs 1, 67, 277, 287 correlation test methods 2 results 3-6 results discussed 6-7 factors affecting 1-2 spherically focused log (SFL) 264, 287 Japan Sea 119 modelling data 125 see also microspherically focused log stretch problems in drilling 279 susceptibility 370 systematic error, role of 55 in chemical analysis 57, 58-60 Tarbert Formation 240-1 facies analysis 244-6 sedimentary history 246-7 sedimentary structures 242-3 thermometry and fluid inclusions 199-200, 205-6 thorium and gamma ray logs 1, 2, 3, 4, 5, 287, 331 tornado chart 160 tortuosity, effect of clays on 232 total porosity model 216 association with effective porosity model conductivity 218-19

421 fluid saturation 219 grain density 217 porosity 217-18 shale volume fraction 21 6-17 role in quality assurance 219-22 Townville Trough comparison of methods 268 microresistivity imaging 266-7 problems of scale 269-70 sedimentary sequence 266 standard core plug measurement 267-8 transform margin study see Cote d'IvoireGhana margin Triassic System studies see Chaunoy Formation Troodos ophiolite 311 turbidite microresistivity image 264-5 ultrasonic shear wave splitting (USWS) method 187-8 results 190-2 results discussed microcrack system 193-4 stress orientation 192-3 theory 187 uncertainty in measurement case study of geochemical analysis 61-2 analytical methods 56-7 handling combined errors 60-1 random error analysis 57-8 sampling methods 56 systematic error analysis 58-60 technique 55-6 defined 54 problems in chemical analysis 54 problems in field sampling 55 up-scaling 9 application of 12 defined 10 problems 107 use of fractals 107-8 methods 108, 109-10 results !08-9, 1I0 uranium and gamma ray logs 1, 2, 3, 4, 5, 287, 331 VECTAR technique 159 veins in ocean crust 319, 321,323 Costa Rica Rift core data methods 394 results 398 core-log integration 406-8 velocity of seismic waves see P waves also S waves vertical averaging in log data 274 Viking Graben 251,257

422

INDEX

volcanic cycles, at mid-ocean ridge 298, 306-8 volume of sample, incompatibility problems 274 Voring Plateau 363

Wessex Basin 65-7 density survey methods 71, 74 results 72, 73 results discussed 78 gamma ray survey methods 67-8, 71 results 69, 70 results discussed 74-8 whisker plot, velocity 103 wireline depth v. drilling depth 275 wireline heave compensation 278

wireline velocity log, Sellafield 100-2

X-ray diffraction (XRD) 204 use in modal analysis 26 method 29, 31 results 31-5 results discussed 35-7 X-ray scanning correlation with FDL 17 database 17-18 methods 18-21 results 21-3 results discussed 23-4 uses 17

zeolite in ocean crust 319, 321,323, 408

Core-Log Integration edited by P. K. Harvey and M.A. Lovell (Department of Geology, University of Leicester, UK) This volume addresses some of the problems of core-log integration encountered by scientists and engineers from both industry and academia. Core and log measurements provide crucial information about subsurface formations. Their usage, either for integration or calibration, is complicated by the different measurement methods employed, different volumes of formation analysed and, in turn, the heterogeneity of the formations. While the problems of comparing core and log data are only too well known, the way in which these data can be most efficiently combined is not at all clear in most cases. In recent years there has been increased interest in this problem, both in industry and academia, due to developments in technology which offer access to new types of information and, in the case of industry, pressure for improved reservoir models and hydrocarbon recovery. The application of new numerical methods for analysing and modelling core and log data, the availability of core scanning facilities, and novel core measurements in both two and three dimensions, currently provide a framework for the development of new and exciting approaches to core-log integration. The contributions within Core-Log Integration geologically range from hydrocarbon-bearing sediments in the North Sea to the volcanic rocks that form the upper part of the oceanic crust. • 432 pages •

over 300 illustrations, including colour

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31 papers

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index

Cover illustration: Spectral gamma ray log, Formation Microscanner borehole image and digital core image of Middle Eocene sediments obtained during ODP Legs 149 and 173 from sites on the Iberia Abyssal Plain, indicating some of the problems of scale in core-log integration.

ISBN 1-86239-016-9