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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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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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,

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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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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 s