136 - 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

T H E G E O L O G I C A L S O C I E T Y

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Contents

Preface

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

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

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?

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

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

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

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

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

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

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

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

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

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

vii

17

25

39

53

65

81

97

115

129

14I

147

159

173

vi CONTENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Index

185

197

213

225

237

249

261

273

285

297

311

327

341

363

375

391

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 proper t ies ' -5 papers, (3) 'Petrophysical relat ionships ' -8 papers, (4) 'Integration of core and borehole i m a g e s ' - 5 papers and (5) 'Applications and case s tud ies ' -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

1 Research 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.

Gamma 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 environ-

ments (Selley 1978; Cant 1992). The spectral

gamma ray tool measures radiation produced by

the radioactive decay of naturally occurring

radioactive elements. The most common natu-

rally occurring radioactive elements in sedimen-

tary rocks are potassium, thorium and uranium.

As each of these elements decay they give off gamma radiation of a particular energy mea-

sured 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 addi-

tion, 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). Sedimen-

tary 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 confidential-

ity agreements we are unable to identify the wells

in question or the names of the correlated units. Gamma ray logs are affected by hole condi-

tions, 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 Logging Drilling Correlated number contractor mud sandstone

1 1 KC1 A 2 1 Oil 3 1 KC1 4 2 Oil A 5 2 Oil 6 1 Oil 7 ! KC1 8 1 Oil A 9 1 Oil B 10 1 KC1 B 11 2 polymer B 12 1 KC1 B 13 1 Oil B

M e t h o d s

A simple seven category lithofacies scheme was

adopted for the cored wells with classification on the basis of lithology and sedimentary struc-

tures: cross-stratified sandstones, silty sand-

stones, sandy si l tstones, c laystones, coal,

limestones and rooted beds. After depth match-

ing 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.

O b s e r v a t i o n s

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 radia-

tion 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 sand-

stones, 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 con-

tractor 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 potas-

sium but a few have almost no potassium. Changes within lithofacies for a particular well

could be due to differences in the detrital

composi t ion 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 cross-

stratified 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. Geolo-

gic factors such as a change in provenance or

diagenesis are unlikely to produce such a clear

systematic difference. Other possible explana-

tions are that the sandstones were deposited in

different deltaic environments: mouth bar, dis-

tributary 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.


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 be-

tween three wells. The cross plot of potassium

against thor ium (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 environ-

ment. The factors most likely to affect the logs

are caving, the use of different drilling fluids, and


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..

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 explanat ions 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.

Unit B. 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 differ-

ence? 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-


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 interdistribu-

tary 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 Nor th Sea have been identified from core descriptions and compared

with spectral gamma ray logs. The results show


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 de-

positional environment on the basis of spectral

gamma ray logs alone. The differences between wells are at tr ibuted to changes in logging

environment, mainly mud characteristics, bore-

hole quality and different logging companies

which have made detailed correlations impossi-

ble. 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 concentra-

tions. 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 analy-

sis can be reliably made using spectral gamma

ray response alone. The influence of downhole environment could be further tested by compar-

ing 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.

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DRESSER ATLAS. 1982. Well logging and interpretation techniques (3rd edition). Dresser Industries Inc., USA.

LEEDER, M. R., RAISWELL, R., AL-BIATTY, H., MCMA- HON, 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. CORBETT, 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 extra- polation 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 scale- up 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 petro-

physical 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 core- log 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. 137- 139). Comparing probe to plug to well test permeabilities is an up-scaling problem (Cor-

bett et al. 1996a).

The issue of measurement scale for the same petrophysical property is the process of up- scaling. 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 relat ionship--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 distin- guish 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 up- scaling 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.

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

(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 Ca rman-Kozeny 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 lamina- set 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 permeability- thickness 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.).


-11975" :

-12025" ~ P r o b e

Cum. Prob~

~LT

~ -12125 ~~ -12175'

-12225 , , , ~ 9 , 9 , , , , , 9 , 9

0 50 100 150 200 250 300 350 400

-11900

-11950" ~ P r o b e

P L T

-12000- ~ i i

-12050 -

I

-12150 , , , , ' , 9 , 9 , 9

0 250 500 750 I000 1250 1500 1750

Permeability (mD)

Fig. 7. Validation of probe/MSFL predictor (refer to Fig. 5a) against production log data (PLT) in two wells from the As-Sarah Field. The intervals picked out by the predictor over a 250 ft interval correlate well with the productive intervals seen with the PLT. Refer to Ball et al. (1997) for more details.

Bed Scale

t

'~ ~ I~T ~

,

~. 0.001t o 5

Bedset Scale

t

I

~ enrlda~: ; % o P ~ : 2 a t timates

I I

10 15

Measurement interval (ft)

Fig. 8. Validation of probe/FMI resistance predictor for kv/kh with pressure data from a Modular Dynamic Tool (MDT) for an interval of the Sherwood Sandstone. The probe estimator uses the harmonic average (over a moving window--to represent vertical permeability over a measurement interval) divided by the arithmetic average (horizontal permeability) over the same expanding window. Refer to Thomas et al. (1996, 1997) for more details.

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 up- scaling 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 plug- density method for permeability prediction9 This will reveal the nature of improvements provided to the petrophysicist by the smaller scale measurements9 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 context9 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 regions9

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 permeability9

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 recovery9

4 "

~ 3 -

;~ 2"

0

r -1

J ~ - . 9 . .ii 9 ] : 9 9 ' .

9 i,iii. 9

2-047 . . .

0 ' ;0 ~0 3'0 Porosity (%)

Fig. 9. Core plug porosity and permeability relationship for the PUC-B Sandstone. This type of relationship is typical in texturally heterogeneous fluvial reservoirs. Clay content and cementation variations at plug scale due to clay drapes and rhizocretions also impact these data. These factors combine adversely to make a complex relationship between permeability and porosity, one which cannot be used with any confidence for permeability prediction. A more texturally sensitive surrogate property is needed and was provided (in this study) by the MSFL resistivity9 Refer to Brayshaw et al. (1996) for more discussion on the textural controls on permeability and to Ball et al. (1997) for more details of the PUC-B study.

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, kv /kh ) 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!


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 in-

dustry. However, the very fact that there are

representative elements within reservoirs (e.g.

stratal elements, genetic units, architectural

elements) can also be exploited. The fundamen-

tal 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 petro-

physics--genetic petrophysics. This method in-

volves a more selective use of petrophysical

measurement which is intended to be more cost-

effective. Indeed, a genetic petrophysics ap-

proach 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 bene-

fits 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 consis-

tent-volume cross-scaling and geologically-con-

strained up-scaling can be effective.

The tools, understanding, and techniques are

now available for the development of a more

geologically-based petrophysics method--which

we refer to as genetic petrophysics--that is fit for

the purposes of reservoir modelling. It is under-

stood that improved modelling prepares the way

for improved oil recovery--the ultimate motiva-

tion 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, Schlum- berger, 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.

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, Lon- don, 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 Geophy- sical Research, 93, 7729-7740.

HALDORSEN, H. H. 1986. Simulator parameter assign- ment 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 Interna- tional Symposium of the Society of Core Ana- lysts, 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-

16 P. W. M. CORBETT ET AL.

tion measurements. Journal of Geophysical Re- search, 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, geometry , 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 permea- metry. In: OAKMAN, C. D., MARTIN, J. H. CORBETT, P. W. M. (eds) Cores from 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.

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 resin- stabilized 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 char- acterized:

(a) bedding features and sedimentary struc- tures;

(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 pre-

served 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.

D a t a b a s e

The Scott partner group provided access to a range of core and associated materials:

(a) 108ft of l i thological ly/mineralogical ly 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

17

18 A. R. DUNCAN ET AL.

(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 silt- stone interbeds. The sandstones are generally fine grained and are frequently apparently structureless or faintly laminated. Current rip- ples and burrows are locally observed. Bioturba- tion 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 (1- 3.5%). 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 straightfor- ward matching of each frame to form the 3 ft composite image, a steel mesh with a grid of V2 in 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.


190

170

~'~ 150

t30

e=

C 110 E ,-1

, - I 9O

50 2.35

Ox x i o o i x x

_ ~ . ~ o ~, j . . . . . . i o e j x ~ ~ x | " ~ ' x x o x t~ x '- . . . . . C o r r e c t e d

~ ~ o :! o i ~ ~ F o ~ ~ - ~ ~ $ ' ~ ' ~ ~ ~ I ~ x , ; Luminance

x Luminance

o Corrected Luminance

- - L u m i n a n c e R2=0.6445

2.4 2.45 2.5 2.55 2.6 2.65 2.7 2.75 2.8 B u l k Density (wireline) (g/cc)

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 X- ray 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 frac- ture 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.

190 . . . . . . . o io 6 . i

I 170 . . . . . . . . o ...... ~

o ~

~ 1 ~ 0 1 . . . . . . . . . . . . . . . . . . ~ o. 0 ~ i o

~ o o oo o~ ~ _ ~ oo ~ o _ 130 . . . . ~ . . . . . . . ~ . . . . . . . . . ,, ~ -. - : - ~ = - - L o . . . . . .

0 0 ~ O 0 '

I o o o ~ o , o ^ o ~ . . ~ f o o o ! '

"~ o ! ~ / o - o 2 ~ ! . . . . . o I . . I U ~ . o v ~

~ _ o~ ~9 i , o C o r r e c t e d _1 9o i - ~ '~ ~ . . . . . . . . . . i . . . . ~ L u m i n a n c e

o e l o o i ' oo .~o o ! i

70 ._0 0 O0 o; ~ - - : t ~ C ~ : , i L u m i n a n c e

i 1 R 2 = 0 . 6 1 3 5 i

0 2 4 6 8 10 12 14 16 18

P o r o s i t y ( p l u g ) ( % )

Fig. 5, Cross plot of luminance values from slabbed material and porosity values from conventional core analysis of core plugs taken prior to slabbing. Luminance values corrected for slab thickness variations.

190

170

~.~ 150 m

.Q =" 130

0 C (~ 110 e"

E

- - I 90

I

i i

i - - - 4 . . . . . . .

~ ~Nxx -

i " - %

i ! i D B D 1 ,, B D 2

i _ _ 1 o B D 3 x B D 4 ] ! x B D 5 , B D 6 ' i - - A l l data R2=0.88 : i

x ;

x ' _ _

1.80 1.90 2.00 2.10 2.20 2.30 2.40 2.50 2,60 2.70 2.80

B u l k D e n s i t y ( g / c c )

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

th ickness cor rec t ion is seen to have little

significance in the final correlation of the logs.

Detailed convent ional core analysis and sedi-

mentological analysis has been carried out on

the core sections analysed for this project; this

data has been compared with the luminance data

measured from the slabbed section close to the


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. Com- parison 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

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.

Documents

136 - Core-Log Integration