utilization of rock characterization data to improve well log interpretation

8/12/2019 Utilization of Rock Characterization Data to Improve Well Log Interpretation

1/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPOSIUM, JUNE g-13.1986

UTILIZATION OF ROCK CHARACTERIZATION DATATO IMPROVE WELL LOG INTERPRETATION

Y

R. B. Truman, ResTech Houston, Houston, TexasD. K. Davies, David K. Davies & Associates, Kingwood, TexasW. E. Howard, ResTech Houston, Houston, TexasR. K. Vessell, David K. Davies & Associates, Kingwood, Texas

ABSTRACT

Methods developed over the past four years allow for the use ofquantitative rock characterization data to better define well log responsefor purposes of computation and interpretation. The rock data is derivedprimarily from thin section point count analysis. Additional information

is derived from bulk and fine fraction X-ray diffraction and non-quantitative scanning electron microscopy.

Rock lithology and mineral composition are employed to determine thewell log response to various quantities of minerals in the reservoir rock.The use of the log data then allows a continuous solution for theoccurrence of these minerals in the interval of interest. The result is amore accurate evaluation of reservoir properties from the well logs thanwould normally be attained without this additional rock data. Anadditional benefit from the use of the rock data is an understanding of thedistribution of minerals, such as clay, in the reservoir rock.

Examples of the use and integration of the rock data and log data areshown. The well log data includes the newer measurements such as naturalgamma ray spectroscopy and electromagnetic propagation time. The examplesare elastic rocks. They include a full range of conditions including highporosity shallow Gulf Coast formations; tight low porosity Travis Peak ineast Texas and unconventional reservoir rock such as the Devonian shale inthe Appalachian Basin.

INTRODUCTION

Core analysis data normally available to the log analyst includesporosity, permeability, fluid saturations, capillary pressure, bulk andgrain density, cementation exponent and saturation exponent. Most of thisinformation is integrated with the well log information to provide animproved interpretation of the fluids within the pore system.

Additional information is often available concerning the solid rockportion of the sample. This information includes quantitative mineralabundance, mineral distribution, grain size, and sorting. The informationhas generally been acquired for purposes not related to log analysis and

-1-


2/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPOSI UM, JUNE g-13,1986

formation evaluation, such as determining sensitivity to formation damage,environment of deposition, completion design, and diagenetic history.

This information has been utilized in the past1~2~3~4 to improve and

supplement log interpretation. Generally, the rock characterization datais used qualitatively, because it can be difficult to integratequantitatively. Most often this is due to problems relating toterminology, lack of familiarity of what is measured, and how it relatesto the log data, and the difficulty of quantitatively handling the data ifit is not digitized.

Many of interpretation techniquesa~~~o~~~.5,6,7

utilized today use a bulkvolume rock Most of the water saturation relationshipsrequire accurate evaluation of the shale or clay content. 8 Utilization ofrock characterization data allows the quantitative calibration of these loginterpretation models. This results in a more accurate log-deriveddetermination of the lithology and mineral content of the formation.

ROCK CHARACTERIZATION TECHNIQUES

Quantitative determinations of rock data are based primarily on thinsection (petrographic) analysis and X-ray diffraction analysis. Bothtechniques are used because the type of information derived from eachanalytical procedure differs in some important respects. Thin sections areused to derive grain size, sorting, abundance of individual componentscoarser than approximately 20 microns, shale volume, and shaledistribution. X-ray diffraction analysis is used chiefly to determine theabundance and varieties of clay minerals (illite, chlorite, etc.) presentin the rock. It is also used to establish precise mineral phases incomplex lithologies (for example the quartz - chert - opal - tridymiteseries). Scanning electron microscope (SEM) analysis is also used, but ina qualitative sense only to provide details of pore geometry and porelining minerals.

QUANTITATIVE THIN SECTION POINT COUNT ANALYSIS

Thin sections are prepared using small pieces of rock, broken or cutfrom the selected sample. This piece of rock is impregnated with blueepoxy resin, which fills pores and artificially cements friable ( loose or

soft ) samples. The epoxy is allowed to harden and th; rock is glued to aglass slide and ground to a thickness of 30 microns. At this thicknesslight can be transmitted through the rock and resin. This thin section isthen studied with a petrographic microscope which is specially equipped formineral identification as well as general viewing of the sample. Grainsize and compositional data are derived simultaneously through point countanalysis of the thin section . 10,ll

The accuracy of the point count technique increases with the number ofpoints counted on each slide. It can be shown that 300 points will givereliable results for a minimum investment in time. Probable errorincreases rapidly below 300 points and does not improve significantly above

- 2 -


3/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPOSIUM,JUNE g-13.1986

300. Thus, 300 points are used in most samples for determination of bothcomposition and grain size. The composition of each component of the rockis expressed in volume percent.

The product of quantitative thin section point count is a tablelisting mean grain size, sorting and the composition of the samples (Table

1). The grain size is calculated as the mean long axis diameter of 300grains selected by point count techniques. ExperimentsI indicate thatgrain diameters measured in thin section underestimate true diameters bysmall but consistent amounts. Sorting (the standard deviation of meangrain size) is determined visually from a standard set of publishedphotographs.13 It is expressed non-numerically as poor (large range ofgrain sizes in a single sample) to very well sorted (small range of grainsizes).

The compositional components of the sample are categorized intograins, depositional matrix and cement. Grains form the framework of therock, and most sandstones contain a variety of grain types, which can havewidely different chemistries, specific gravities and radioactivity levels.Fines deposited simultaneously with the grains are referred to asdepositional matrix. (See Appendix for clarification of geological terms).Depositional matrix can be divided into laminar (layered) or dispersed(intergranular) depending upon its mode of occurrence. It is not possibleto identify the individual mineral components in the depositional matrixusing thin section analysis, because they are too fine grained.

Cements occur in intergranular areas and can be many and varied. Thecements listed in Table 1 are the most common in reservoir sandstones.Porosity is visible in thin section and can also be quantified.

Geometrical effects can result:: vo1umes

of grains, depositionalshale, and cements which are in error. The magnitude of the error factorincreases with decreasing grain size, higher grain packing, decreasingsorting and increasing cement. The direction and magnitude of these errorsshould be considered when integrating the results with the X-raydiffraction data and well logs. Geometrical factors, together withmicropores too small to be seen with the petrographic microscope, combineto make porosity values in thin section smaller than true porosity.Therefore, thin section derived porosity is not used in the integratedsolution.

X-RAY DIFFRACTION ANALYSIS

X-ray diffraction analysis is used to determine specific mineralcomponents present which are too fine grained to be identified in thinsection (mineral species present in depositional shale and clay cement).In complex lithologies, such as the Monterey Group, or Frio-Vicksburg ofsouth Texas, it can be valuable in the recognition of the precise phase ofcomplex mineral series (such as the silicas and zeolites).

-3-


4/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPO SIUM, JUNE g-13,1986

The results of X-ray diffraction analysis are very sensitive to minorfluctuations in sample preparation and analytical procedures. Errors ofseveral hundreds of percent can result from such fluctuations.15 Carefuland consistent sample preparation procedures are essential to the

derivation of reliable and reproducible results.

Bulk X-ray analysis is always undertaken on each sample as the firstX-ray procedure. This yields the abundance of clays (as a group, not asindividual species), silicas, carbonates, other minerals and drill solids.Comparison of the reflections of an X-ray diffractometer yieldssemiquantitative data concerning the relative abundance of the variousmineral phases. The diffraction peak intensity of a mineral phase in aproperly prepared sample is related to the relative abundance of themineral in the sample mixture. Most mineralogists interpret the abundanceof non-clay minerals using intensity factors derived by Schultz of the U.S.Geological Survey*16

Schultz's work also indicates that this form of quantitative X-raydiffraction analyses yields results which are accurate to within plus orminus 10 percent of the actual values for mineral phases present inquantities of more than 20 percent. For minerals occurring in quantitiesof less than 20 percent the accuracy of this type of analyses falls to plusor minus 20 percent of the actual value.

Individual species of clay minerals (kaolinite, chlorite, etc.) occurin abundances which are significantly less than 20% in most reservoirrocks. A knowledge of the abundance of each of these species is ofimportance in formation evaluation because of intrinsic differences inradioactivity (both amount and type), formation damage mechanisms, andcation exchange capacity of each of the species. Additional X-ray analysisof the sample is necessary to derive this data.

Clay species are determined using fine fraction (less than 5 micron)X-ray diffraction analysis. This form of analysis yields semiquantitativeinformation concerning the composition and relative abundance of finegrained rock constituents, particularly the clay minerals (Table 2). Finefraction samples are first X-rayed in the air dried state. Subsequentlyeach sample is solvated with ethylene glycol in a glycol bath for 12 hoursand immediately re-X-rayed. This form of analysis is useful in identifyingmixed layer or other expandable clays. Selected samples are subsequentlyheat treated to 400 and 550 degrees centigrade for at least one half hourand re-X-rayed to aid in the distinction of kaolinite and chlorite clay.

Clay mineral peak intensities are measured by integrating the areaunder characteristic peaks on the fine fraction X-ray diffractograms. Peakheight integration is required in the case of clay minerals due to the factthat the clays have small scattering domains and their diffraction peakwidths are not controlled by the diffractometer. Calculation of the degreeof expandability of mixed layer clays employ data published by Reyno1ds.l'

- 4-


5/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPOSIUM, JUNE 9-13,1986

The end product of these varied but complementary bulk and finefraction X-ray analyses is a single table which includes a listing of theabundance of each mineral species, including minor constituents (


6/21


well as the degree of heterogeneity of the portions of the samples used foranalysis.

WELL LOG RESPONSE TO LITHOLOGY AND MINERALS

Some types of well log response are more strongly influenced by thelitholog

Y90fthe rock. Lithology is defined as the physical character of

a rock . As we have seen above, the lithology is generally obtainedfrom the thin section point count data. Well logsinfluenced b

32i;;n~to~~ include

that are primarizyspontaneous potential,20 gamma ray,

resistivity, attenuation of the electromagnetic propagationwave, and microresistivity.

Other well logs are primarily influenced by the mineral content of therock. A mineral is defined as a homogenous naturally occuring hase; bysome authorities restricted to inorganic, crystalline phases . pl9 Themineral content of the rock is obtained from the X-ray diffraction results.

Well logs that are primarily influenced by the mineral content of the rockinclude photoelectric cross-section index,26 27

24 density,25 induced gamma rayspectroscopy, neutron, electromagnetic propagation time 28 and, in somecases, natural spectral gamma ray.2g

In some cases the well logs may be influenced by a combination of thelithology, mineral content, and the distribution of the minerals in therock. This may occur with sonic,30 neutron, and thermal neutron decay timemeasurements. The relative influence of lithology, mineral content anddistribution are difficult to quantify at this time.

METHODOLOGY

The core analysis results are first digitized. This normallyincludes permeability, porosity and saturations. Occasionally graindensity and core gamma ray are available. When the results are fromcontinuous whole core, the data is depth shifted to match the well logdata. If the data is from sidewall core analysis, it is checked forobvious depth discrepancies. If these are noted, then no quantitativeintegration of the log and core analysis results can be achieved.

The core analysis results are then displayed on depth with theavailable well log data. The samples are selected in accordance with thetype of log interpretation being performed. It is advisable to selectsamples throughout the dynamic range of the various log responses. Forinstance, in a sand-shale sequence, samples would be chosen over the rangefrom the cleanest sands to the shales with the highest clay content.Additional samples may be selected to evaluate anomalous or unusual logresponses. In addition, samples are selected in thicker beds where the logresponse of all measurements are fairly uniform. Since the rockcharacterization is generally used for several purposes, it has been foundthat the samples are best selected by a geologist and petrophysicalengineer working together. In some instances, due to recovery or fieldselection procedures, samples are not available over the entire range of

- 6 -


7/21


log responses. When multiple wells are analyzed at the same time thesampling problem is diminished.

The thin section point count, X-ray diffraction and SEM analysis isthen performed. The results are digitized, the data depth shifted and apreliminary evaluation is made against the log data. The purpose of thispreliminary evaluation is to insure that the small petrographic sample isrepresentative of the bed being analyzed. A sample that is analyzed in atwo (2) inch shale stringer in the middle of a ten (10) foot sand would beof little value in the integrated solution. The shale stringer is notrepresentative of the sand bed to which the logs are responding. In thesecases another sample is selected for analysis from that bed.

The rock analysis results are then processed so that data iscompatible with the well log response and the interpretation beingperformed. For instance, the distinction between monocrystalline quartz,polycrystalline quartz, and silica cement (Table 1) is not important to the

log analyst. The mineral is all quartz, Si02, and the log response is thesame regardless of the type of quartz.

The data is then plotted against conventional well log data todetermine the calibration parameters to fit the log response to the rockdata. The type of plots generated are determined by the log dataavailable, type of problem being analyzed and the desired results from thecore analysis. Since all data is digitized, the evaluation usingcrossplots and statistical analysis is quick and straight forward.

The results are then analyzed as to applicability to theinterpretation of the data. Some of the aspects to evaluate are:

. Is the dynamic range of the log data consistent with the dynamicrange of the core data?

. Are there anomalous measurements that should not be included inthe statistics? This is easily evaluated since the depth of eachsample can be plotted.

. Is there some combination of log measurements that will provideimproved overall results?

. Are there additional results that can be obtained from thesynergy of the log and rock characterization data?

. Are there additional results that may be obtained from thesynergy of conventional core analysis data and the rockcharacterization data?

The purpose is not to develop a straight linear regression of the logdata against the rock characterization data. The objective is to use therock data to develop a robust interpretation scheme that is consistent withstandard accepted petrophysical engineering practices, tool response

- 7 -


8/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPOSIUM, JUNE g-13,1986

equations and the ground truth provided by the rock data. This allowsthe widest possible application of the interpretation model for a specificsolution or formation.

APPLICATION OF THE METHODOLOGY - UPPER FRIO

In order to discuss the application of the methodology, the data inTable 1 is crossplotted with several well log measurements (Figures 1,2 and3) The figures are presented as crossplots of core data on the Y axis andthe log data on the X axis. In addition, a histogram of log data ispresented over the interval of interest. The reduced major axis (RMA)regression lines are shown on the plots along with correlation coefficientsand number of samples. The histogram provides a representation of thedynamic range of the log response. This will be important in the selectionof the shale indicators.

There is a good correlation between the spontaneous potential, SP, and

total shale from the thin section point count (Figure 1). Note however,that the dynamic range is poor. When the shale content is greater than 22%the SP is 0 and no quantitative determination of shale volume can be made.It should be noted that this is not an unusual case. Complete SPsuppression generally occurs between 20% and 30% bulk volume shale. Alsonote that the static SP can be obtained at 0% shale volume.

There is also a very good correlation between the electromagneticpropagation wave attenuation, EPT ATT, and shale volume (Figure 2). Inaddition, the dynamic range of the log data is consistent with the linearregression line from the core data. The maximum attenuation corresponds toa shale volume of 100%. In this, plotting attenuation directly works well,since water filled porosity is high and relatively constant.

There is a very poor relationship between gamma ray, GR, and shalevolume (Figure 3). Dropping the circled data point improves thestatistics. However, the linear regression line does not fit the ran e ofobserved log data. This has been observed by previous investigators. 9 1y32A non-linear relationship that is not inconsistent with the observed datais shown. However, more samples over a wider range of log data is requiredto develop a relationship utilizing the GR. The occurrence of dolomitecement in the sample with the higher GR is not unusual and has beenobserved in other wells along the U.S. Gulf Coast.

ROUTINE APPLICATION - TRAVIS PEAK (HOSSTON)

This application is taken from a well drilled to the lower Cretaceous,Travis Peak of northeast Texas.31 The well, the Clayton Williams, SamHughes #l was drilled in Panola County in April of 1984.

Substantial petrographic studies in the Travis Peak have been made bythe Bureau of Economic Geology in Austin, Texas. Thin sections taken fromTravis Peak cores indicate mineral composition can be divided into three

- 8 -


9/21


parts:32 (1) framework (i.e., detrital grains), (2) matrix, and (3)cement.

Quartz is the most abundant detrital mineral. It comprises up to 80%of the sandstone volume and averages 65.4 percent. Feldspars, includingplagioclase, orthoclase and microcline comprise 0 to 10% of the rock volume(2-32 average). Less abundant minerals existing in the detrital grains arechert and metamorphic rock fragments, and more rarely, tourmaline andzircon (heavy minerals).

The detrital shale matrix is deposited primarily as illite andchlorite. Presence of mixed illite-smectite and kaolinite occurs rarely.Near the top of the Travis Peak, presence of carbonate will occur as a finegrained carbonate mud. Some of this mud remains as calcite but most hasbeen ankeritized.

Authigenic minerals (cements) contain a variety of minerals. The most

abundant are quartz, illite, chlorite, ankerite, and dolomite. Less commonminerals include calcite and iron rich calcite, feldspar overgrowths,pyrite, barite and anhydrite.

In the referenced well, eighty four feet of conventional core weretaken. Boyle's porosity were measured on forty four plugs from this core.Petrographic analysis (including thin section point count, fine fraction X-rays and scanning electron photomicroscopy) was performed on twelve endpieces of the plugs used for porosity analysis.

Known shale indicators were tested against thin section shalepercentages to determine the most accurate method of deriving log shalepercentage. Selected for use in this analysis were the gamma ray, neutron,

and electromagnetic attenuation logs. The minimum of these three shaleindicators was used as an input in the calculation of lithology, watersaturation and porosity. Figure 4 illustrates the results of the logshale volume determination as compared to thin section analysis.

The results of the shale volume determination are utilized with otherlog values to determine lithology using a simultaneous solution of fourlinear equations. Using this method, only the major mineral constituentsare identified. In the case of this Travis Peak example these are quartz,ankerite, calcite, and shale. Although shale is not a true mineral, theconstituents (illite, chlorite and small amounts of other minerals) aregrouped for two reasons: (1) solving for the individual components willnot greatly aid the log analyst in his interpretation; (2) any furthersolution tests the accuracy of the described method.

Porosity is estimated using a shale corrected neutron density porosityaverage. In the case where neutron porosity is less than density porosity(gas), density porosity has been weighted by a factor of four. Figure 5compares the results of measured core porosity and log derived porosity.

- 9 -


10/21


Figure 6 displays the results of the analysis. The two coredintervals are shown with log derived results for lithology, porosity, andwater saturation. Included for comparison are results from thin sectionanalysis and porosity as measured from cores. Analysis of the results

indicate two productive sandstone intervals from 6837' to 6845' and from7088' to 7100'. Average porosity and water saturation for the upperinterval are 10.5% and 35.4%. For the lower interval average porosity andwater saturation are 9.7% and 41.7%. The upper interval was selected forcompletion and produced at an initial rate of 580 MCFGPD and 10 BCPD.Absolute open flow potential was recorded at 980 MCFGPD.

MODEL DEVELOPMENT - DEVONIAN SHALE

Rock characterization data can be utilized to develop log computationmethods in difficult formations. This includes the Monterey in California,low resistivity pay sands in the Gulf of Mexico and the Devonian shale inthe Appalachian basin.35

In the Devonian shale it was necessary to develop a model that wouldquantitatively identify the constituents. This required identification ofthe rock constituents and developing the relationships between the logresponse and the rock constituents.

As an example, the data indicates a relationship between uranium yieldand kerogen content (Figure 7) and potassium yield and clay content (Figure8) Note that kerogen is a mineraloid that must be quantified by pointcount or geochemical analysis. The clay is a fine grained mineral thatmust be quantified by X-ray diffraction.

There is also a good relationship between kerogen content and bulkdensity (Figure 9). As kerogen content increases, the bulk densitydecreases. This appears as increasing apparent density porosity. However,the effective porosity is actually decreasing as the kerogen contentincreases (Figure 10). Kerogen content appears to occur at the expense ofsome of the effective porosity. This has lead to the following preliminaryrelationship for the decreaseDevonian shale.35

in effective porosity due to kerogen in the

Vcl vkA@ = X- 8 core

Vclmax Vklim

a0 = decrease in porosity due to kerogenVcl = clay contentVclmax = maximum clay content

vk = kerogen content

Vklim = kerogen content above which no further changein porosity occurs

@core = core porosity where V,l is a maximum and Vk is zero

- 10 -


11/21


This and other applications of the rock characterization data in theDevonian shale has contributed significantly to development of a Devonianshale constituents model and a more reliable evaluation of porosity.

CONCLUSIONS

Rock characterization data may be successfully integrated with welllog data on a routine basis.

Rock characterization data improves the understanding of therelationship between the actual lithology and the well log response.

Rock characterization data contributes to the identification of caseswhere previous assumptions concerning petrophysical relationships do not

apply.

Rock characterization data may be utilized to develop quantitative

interpretation models in complex or difficult to evaluate formations.

ACKNOWLEDGEMENTS

The interpretation development work in the Devonian shale and TravisPeak was prepared for and funded by the Gas Research Institute undercontract No. 5085-213-1148, managed by R.A. McBane and contract No. 5084-211-1062, managed by Scot Hathaway.

REFERENCES

1. Keike, E.M., and Hortman, D.J., The Influence of Microporosityof Formation and the Use of the Scanning Electron Microscope to

Gain New Insight into Pore Geometry , Paper SPE 4531, 48th AnnualTechnical Conference and Exhibition of the Society of PetroleumEngineers of AIME; Las Vegas, Nevada, September 30 - October 3,1973.

2. Heslop, A., Porosity in Shaly-Sands , Paper F, 16th AnnualLogging Symposium of the Society of Professional Well LogAnalysts; New Orleans, Louisiana, June 4-7, 1975.

3. Sneider, R.M., King, H.R., Hawkes, H.E., and Thomas, D.K.,Methods for Detection and Characterization of Reservoir Rock,

Deep Basin Gas Area, Western Canada , Paper SPE 10072, 56thAnnual Technical Conference and Exhibition of the Society of

Petroleum Engineers of AIME; San Antonio, Texas, October 5-7,1981.

4. Morphy III, P.H., Thomsen, A., Using Sidewall Sample ThinSections to Evaluate the Completion Potential of Low-ResistivityPleistocene Sands, Offshore Louisiana , Paper SPE 14273, 60thAnnual Technical Conference and Exhibition of the Society of

- 11 -


12/21


5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

Petroleum Engineers of AIME; Las Vegas, Nevada, September 22-25,1985

Savre, W.C., Determination of a More Accurate Porosity andMineral Composition in Complex Lithologies with the Use of theSonic, Neutron and Density Surveys , Journal of PetroleumTechnology, September, 1963, pp. 945-959

Mayer, C., Sibbit, A., Global - A New Approach to Computer-Processed Log Interpretation , Paper SPE 9341, 55th AnnualTechnical Conference and Exhibition of the Society of PetroleumEngineers of AIME; Dallas, Texas, September 21-24, 1980

Nashmy, K.H., Hefter, T., Chicko, R., Computerized ShaleEvaluation Technique - Comset , 23rd Annual Logging Symposium ofthe Society of Professional Well Log Analysts; Corpus Christi,Texas, July 6-9, 1982

Worthington, P.F., The Evaluation of Shaly-Sand Concepts inReservoir Evaluation , The Log Analyst, January-February, 1985,pp. 23-40

Sorby, H.C., On the Structure and Origin of Non-CalcareousStratified Rocks , Quarterly Journal Geological Society of London(1880), Vol. 36, pp. 46-91

Chays, F., A Simple Point Counter for Thin Section Analysis ,American Mineralogist (1949), Vol. 34, p l-11

Chays, F.,

(1956), p.

Krumbein,Journal of

Petrographic Model Analysis, John Wiley and Sons

113

W.C., Size Frequency Distribution of Sediments ,Sedimentary Petrology (1934), Vol. 4, pp. 65-77

Beard, D.C., and Weyl, P.K., Influence of Texture on Porosityand Permeability of Unconsolidated Sand , American Association ofPetroleum Geologists Bulletin (1973), Vol. 57, pp. 349-369

Halley, Robert B., Estimating Pore and Cement Volume in ThinSection , Journal of Sedimentary Petrology (June 1978), pp. 642-650

Gibbs, R.J., Error Due to Segregation in Quantitative ClayMineral X-ray Diffraction Mounting Techniques , AmericanMineralogist (1965), Vol. 50, pp. 741-751

Schultz, L.G., Quantitative Interpretation of MineralogicComposition from X-ray and Chemical Data for the Pierre Shale ,Geological Survey Professional Paper 391-C, U.S. GovernmentPrinting Office, Washington (1964), p. 31

- 12 -


13/21


17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

Reynolds, R.C., Interstratified Clay Minerals In: CrystalStructures of Clay Minerals and Their X-ray Identification, G.W.Brindley and G. Brown, eds., Ch. 4, Mineralogical Society, London

Pettijohn, F.J., Sedimentary Rocks, 2nd ed., Harper and Row(1957) pp. 718

Bates, Robert L., and Jackson, Julia A., Eds., Dictionary ofGeological Terms, Dolphin Reference Books Edition (1962)

Schlumberger, C., Schlumberger, M., and Leonardon, E.G., A NewContribution to Subsurface Studies by Means of ElectricalMeasurements in Drill Holes , Technical Publication No. 503American Institute of Mining and Metallurgical Engineers,February 1933, p. 8

Kokesh, E.P., Gamma Ray Logging , The Oil and Gas Journal, July26 1951

Schlumberger, C., Schlumberger, M., Leonardon, E.G., ElectricalCoring; a Method of Determining Bottom-hole Data by ElectricalMeasurements , Technical Publication No. 462, American Instituteof Mining and Metallurgical Engineers, February 1932, p. 38

Tixier, M.P., Alger, R.P., and Doh, C.A., Sonic Logging Journalof Petroleum Technology, May 1959

Gardner, J.S. and Dumanoir, J.L., Litho-Density LogInterpretation , 21st Annual Logging Symposium of the Society ofProfessional Well Log Analysts, Lafayette, Louisiana, July 8-10,1980

Alger, R.P., Raymer, L.L., Hoyle, W.R., and Tixier, M.P.,Formation Density Log Applications in Liquid Filled Holes ,

Paper SPE 435, 37th Annual Fall Meeting of the Society ofPetroleum Engineers of AIME; Los Angeles, California, October 7-10, 1962

Culver, R.B., Hopkinson, E.C., and Youmans, A.H., Carbon Oxygen(c/o> Logging Instrumentation Paper SPE 4640, 48th Annual FallMeeting of the Society of Petroleum Engineers of AIME; Las Vegas,Nevada, September 30 - October 3, 1973

Alger, R.P., Locke, S., Nagel, W.A., and Sherman, H., The DualSpacing Neutron Log - CNL , Paper SPE 3565, 46th Annual FallMeeting of the Society of Petroleum Engineers of AIME; NewOrleans, Louisiana, October 3-6, 1971

Wharton, R.P., Hazen, G.A., Rau, R.N., and Best, D.L.,Electromagnetic Propagation Logging: Advances in Technique and

- 13 -


14/21


29.

30.

31.

32.

33.

34.

35.

Interpretation , Paper SPE 9267, 55th Annual Fall TechnicalConference and Exhibition of the Society of Petroleum Engineersof AIME; Dallas, Texas, September 21-24

Serra, O., Baldwin, J.L., and Quirein, J.A., Theory and

Practical Application of Natural Gamma Ray Spectroscopy , 21stAnnual Logging Symposium of the Society of Professional Well LogAnalysts, Lafayette, Louisiana, July 8-10, 1980

Wyllie, M.J., Gregory, A.R., and Gardner, L.W., Elastic WaveVelocities in Heterogeneous and Porous Media , Geophysics, Vol.21, January 1956, pp. 41-70

Clavier, C., Hoyle, W.R., and Meunier, D., QuantitativeInterpretation of TDT Logs , Paper SPE 2658, 44th Annual FallMeeting of the Society of Petroleum Engineers of AIME; Denver,Colorado, September 28 - October 1, 1969

Stieber, S.J., Pulsed Neutron Capture Log Evaluation , Paper SPE2961, 45th Annual Fall Meeting of the Society of PetroleumEngineers of AIME; 1970

Howard, W.E., Hunt, E.R., Travis Peak: An Integrated Approachto Formation Evaluation , Paper SPE 15208, Unconventional GasTechnology Conference, Society of Petroleum Engineers of AIME;Louisville, Kentucky, May 18-21, 1986

Dutton, S.P., Bobeck, P.G., Bureau of Economic Geology,Petrography and Diagenesis of the Travis Peak (Hosston)

Formation, East Texas , prepared for the Gas Research Institute,

September 1985

Campbell, Jr., R.L., and Truman, R.B., Formation Evaluation inthe Devonian Shale , Paper SPE 15212, Unconventional GasTechnology Conference, Society of Petroleum Engineers of AIME;Louisville, Kentucky, May 18-21, 1986

APPENDIX

TERMINOLOGY

In order to discuss and communicate the integration of core and rockcharacterization data, the use of certain terms should be clarified. Ingeneral, the geological definitions were utilized rather than the loganalysis definitions. When petrophysical engineers, log analysts, andgeologists communicate, some terms generally cause communication problems.

The following two terms are defined differently by the two sciences:

- 14 -


15/21


TERM

WELL LOGGINGmatrix

The solid framework which surroundsthe pore volume.

GEOLOGICAL

In a rock in which certaingrains are much larger than theothers, the grains of the smallercomprise the matrix.

effective porosity

Interconnected pore volume filledwith free fluids. Hydrodynamicallyeffective pore volume.

The property of rock containingintercommunicating interstices,expressed as a percent of bulkvolume occupied by such inter-

stices.

The use of the terms secondary porosity and secondary porosityindex are sometimes confused. Secondary porosity is defined as postdepositional porosity . Secondary porosity index is the difference in theporosity from sonic log values and the porosity from density or density-neutron log values . There may or may not be any correlation betweensecondary porosity and secondary porosity index.

There is also some confusion over the terms used for shaledistribution. The terms for the different types of shales can generally begrouped as follows:

WELL LOGGING GEOLOGICAL

Dispersed Clay Authigenic clay cementDispersed shale matrix

Structural Shale Clay ballsShale fragmentsRip up clasts

Laminated Shale Detrital shale matrixLaminar shale matrixShale laminations (


16/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPOSIUM,JUNE g-13,1986

WELL

THIN SECTION POINT COUNT SIOENALL CORE

FlELO kIS-ENGLISH 6AYOU DEPTH J850.0 - 5685.0

TEXTURE GRAIN CONPOStTION CEIIENl COHPOSlTlON

E2kt:-_.__.____~___.____.____.____~

t

ITIDNAL MATRIX IFINES LlEPOSlTEO SlMJLTANEOUSLY YI Ii THE

TABLE 1 Representative thin section point count data

XRAY DIFFRACTION RNALYSIS SIDEWALL CORE

_ OEPTI H 650.0 - 5665.0IELD m

ip%__-__-__-__-__-._-.-._._-._-._-

S-ENGLISH BRTOIJ

CRRBONFlTE WINERRLS

NELL -

CLRT MINERRLSOTHER IIINERPLS R IL

C ON-

-?-

Y2e3=._-__- +._- +._- +._ I-- +._- t._-

t.-- t._-._-IO I--

-

?;___-

-__-__-__-__-__-z

-_

E;ezz_.-__-__-__-___-__-__-_.

c

4 _. ._______.____E- ________. _E _____

__ __

+2 _.

__ __ ,__- ____ __

t

ft

E__ ____ __

__-__

7 __--I-_ __ +__ __

E__-__

3 __

_____E- _ --1 __

__ __

s_ __

-. __

-- __

=k- __-__-__-

tc _ __

i- 10-

TABLE 2 Representative fine fraction X-ray diffraction data

- 16 -


17/21


GILLIS-ENGLISH BAYOU FIELD

CALCASIEU PARl.3

CABLE 3 Type of information avail-able from thin section and X-ray

diffraction results

FIGURE 2 Shale volume vs EPT FIGURE 3 Shale volume vs gammaattenuation ray

FIGURE 1 Shale volume vs

GILLIS-ENGLISH SAYO FIELD

CALCASIEU PARISH

0

SP

- 17 -


18/21


FIGURE 4 Shale volume vslog derived shale volume

FIGURE 7 Uranium yieldfrom well logs vs kerogen

FIGURE 9 Kerogen vs bulkdensity from a well log

T. WlLLlS +1

HAMDEN FIELDYINTON COUNTY. OH)0 I

FIGURE 8 Total clay mineralsvs potassium yield from awell log

FIGURE 10 Kerogen vs coreporosity

- 18 -


19/21

SPWLA TWENTY-SEVENTH ANNUAL LOGGING SYMPOSIUM J UNE 9-13 1986

. .

ITHOLOGY LITHOLOG1

THIN LOG

SECTION

FIGURE 6

685

7 5

7

I Sw

LOG

100 0

IPOROSITY POROSITY

CORE LOG

Presentation of thin section point count data core porosityand log derived results on the Clayton Williams Sam HughesNo. 1 Panola County Texas

19


20/21


Robert B. Truman

Bob Truman received his BS degree in Mechanical Engineering fromCalifornia State University at Long Beach in 1966. He was employed bySchlumberger for 15 years in various positions involving field operations,interpretation research and development, data processing and marketing. Heis one of the cofounders of ResTech Houston, Inc. He is a member of SPWLA,CWLS, SPE of AIME, and an associate member of AAPG.

Dr. David K. Davies

Dr. David K. Davies is a specialist in the analysis of sandstone andcarbonate reservoirs. He received his BS and Ph.D degrees in Geology fromthe University of Wales, Swansea (1962, 1966), and his MS degree in Geology

from Louisiana State University, Baton Rouge (1964). For the past 16 yearshe has been both a university professor of geology, and a consultant to theoil, gas, and uranium industries. He has been a professor of geology atTexas A 6 M University, University of Missouri at Columbia, and at TexasTech University where he also served as Chairman of the Department ofGeosciences and Director of the Reservoir Studies Institute. Currently heis President of David K. Davies & Associates, an international geologicaland engineering consulting company. He is author or co-author of more than80 publications in professional journals, and is a Fellow of the GeologicalSociety of America, Member of the American Association of PetroleumGeologists, Society of Economic Paleontologists and Mineralogists,International Association of Sedimentologists, Society Of Petroleum

Engineers, and the West Texas Geological Society. Heis a Registered

Professional Geologist (No. 4188).

Dr. Davies has received numerous professional honors and recognitions,including the A. I. Levorsen Award of the American Association of PetroleumGeologists for his original and creative research in the field of sandstone

reservoirs. He has also been a Fulbright Scholar, and Invited

Distinguished Lecturer on clays and reservoir characterization to

professional societies in Canada, Japan and Australia. He has beenselected as 1984-85 Distinguished Lecturer of the USA national Society ofPetroleum Engineers.

ROBERT B. TRUMAN- 20 -

DR. DAVID K. DAVIES


21/21


William E. Howard

William (Bill) Howard received his BBA in Engineering-Business atTexas A & I University in 1973. He was employed by Schlumberger WellServices for nine years and served in a variety of capacities includingfield engineer, computing center log analyst, sales, and district manager.He is currently Vice-President of ResTech Houston, Inc. Included in hispresent duties is the role of project manager for the formation evaluationresearch work performed in conjunction with the Gas Research Institute's

tight gas sand program. He has recently authored a paper on the integratedapproach to formation evaluation in the tight gas sands area. He is amember of SPWLA and SPE.

Dr. Richard K Vessel1

Dr. Richard Vessel1 obtained his BS degree from Southern Illinois

University, MA from the University of Missouri at Columbia and Ph.D fromTexas Tech University. He has been involved in regional and field studies

in Australia, Canada, Central America and most of the productive basins inthe U.S.A. He has been responsible for the design of successfulstimulation programs in numerous carbonate and sandstone reservoirs. Dr.Vessel1 has worked with David K. Davies for the past 10 years.

WILLIAM E. HOWARD

utilization of rock characterization data to improve well log interpretation

Documents