Volume Issue 0 1998 [Doi 10.2118_39667-Ms] Barman, Indranil; Sharma, Arun; Walker, Richard;...

7/28/2019 Volume Issue 0 1998 [Doi 10.2118_39667-Ms] Barman, Indranil; Sharma, Arun; Walker, Richard; Gupta-Datta, Ak -

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PE 39667

ermeability Predictions in Carbonate Reservoirs Using Optimal Non-parametricransformations: An Application at the Salt Creek Field Unit, Kent County, TXdranil Barman, SPE, Texas A&M U,, Arun K, Sharma, SPE, Mobil E&P US Inc., Richard F. Walker, SPE, Mobil E&P Uc. and Akhil Datta-Gupta, SPE, Texas A&M U.

y ri gh t 1998, Soci et y o f Pet ro leum Engineers, I nc ,paper was prepared for pfeseiitatiin-et the 1998 SPE/DOE Improved Oil Remveiy

pms ium held i n Tul sa , Okl ahoma, 19-22 Apri l 1s93pape r was sel ec ted for p resen tati on by an SPE Program Committee fo ll owing revi aw o frmation contained in an abstracl submitted by the auther( s), Contents of tha paper, asen ted , have not been rev iewed by the Soci ety of Petro leum Engi nears and a re sub je ct toection by the author(s), The material, as presented, does not necessarily reflect anyti on o f the Soci ety of Petrol eum Engi neer s, its offi cars , or members Papers prasented atmeetings are subjact to publication review by Editorial Committees of the Society of

o leum Engineers, E lect roni c raproctuc tkm, d is tr ibut ion, o r s !o rage of any par t o f th is papercommerci al purposes wi thou t the wri ttan consen t of the Soci ety of Pa trol eum Engi neers isibi ted , Pe rmis si on to repr cduca i n pri nt is re stri cted to an abstra ct o f no t mo re than 300d% illustrations may not be copied The abstract must contain conspicuousnowl edgmen t o f whe re and by whom the pape r was presented, Writa Li brari an , SPE, P,O.833836, R ichardson, TX 75083-3836, US A, , fax 01-972-952-9435,

this paper, we have utilized a non-parametricand regression technique called ACEernating conditional expectation) to estimate permeabilitym well logs at the Salt Creek Field Unit (SCFU), Texas, aterogeneous reef carbonate reservoir. Previous attempts torive permeability correlations at the SCFU have been lesssatisfactory, leading to an over-dependence on porosityrived reservoir descriptions to predict fluid flow. Usingn-parametric regression, we have now established ationship between permeability and several common wells that are available field-wide. These include densityrosity, neutron porosity, shallow resistivity, deep resistivityd gamma ray logs. The approach adopted here also allowedto integrate our geologic understanding of the reservoir intonon-parametric regression, further optimizing the finalelation. We have successfully predicted permeability in ajority of the uncored wells with acceptable accuracy atFU. These results have led to an enhanced reservoirracterization based on flow (permeability) rather thanrage (porosity). This benefits both daily operations andrvoir simulation efforts. This first, full-field application ofE in a carbonate reservoir has demonstrated the strengthpotential wide-scale use of non-parametric methods toict permeability in heterogeneous reservoirs.

meability predictions are a critical aspect of a reservoircription. It is a common practice to develop correlations

between permeability and porosity based on core datathen predict permeability in uncored wells using porosderived from well logs.-4In sandstones, a linear relationsnormally exists between porosity and the logarithmpermeability. Thus, permeability predictions in sandstonesbe achieved with acceptable accuracy using porosity fiwell logs. In carbonates, however, petrophysical variatirooted in diagenesis, grain size variation, cementation, etc.destroy the direct relationship between porosity apermeability. In such cases a better predictor is necessarequiring more rigorous models capable of adequathandling any inherent non-linearities. Such a model could ufor example, multiple well logs that individually orcombination represent permeability directly or indirectAccording to Wendt et al., the term better predictorsomewhat subjective, but can be quantified throughfollowing questions: Should we strive for accurate averadata at the expense of all other considerations? Should lpermeability values be eliminated in order to better predhigh values? Should we match the core data at every depOr should we edit erratic core data caused by errasampling? The answers to these questions are not absoland vary from application to application. They depend upthe objective of the correlation development, the complexof the variables and our understanding of the reservoir.Several limitations inhibit multiple regression techniqumany arising from the inexact nature of the relationshbetween petrophysical variables. Conventional parametregression requires a priori assumptions regarding functionrelationships between the independent and dependevariables. In complex carbonates such underlying physirelationships are not known in advance, making traditionmultiple regression techniques inadequate, leading to biasestimates. Non-parametric transformations, however, generaregression relations in a flexible data-defined manner throuthe use of scatterplot smoothers and in doing so let the ditself suggest the functionalities. They have been developedoffer a much more flexible data analysis tool when explorithe underlying relationship between independent adependent variables.

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2 BARMAN l., SHARMA A.K., WALKER R.F. AND DATTA-GUPTA A. SPE

In this paper we have used a very general, yetcomputationally efficient algorithm called the AlternatingConditional Expectation (ACE) for permeability predictionsfrom multiple well logs at SCFU. The algorithm provides amethod for estimating optimal transformations for dependentand independent variables such that the correlation ismaximized in transformed space. The ACE algorithm wasoriginally proposed by Breiman and Friedmanb and recentlyintroduced to reservoir characterization by Xue et al.78andDatta-Gupta et al.sBackgroundThe Salt Creek field, located in Kent County, Texas, is ahighly heterogeneous, shallow reef carbonate reservoir in thePermian basin (Fig. 1). The reservoir lies on Strawn limestoneand is overlain by the Cisco shale (Fig. 2). The buildupconsists of grainstone shoals, bars and spits associated with ashelf environment. The average depth and thickness are 6300ft and 170 ft respectively. The oil-water contact is located at -4425 ft subsea. The field was discovered in 1950 and placedunder a pressure maintenance scheme in May, 1953, using acenter line water injection program. A 40 acre developmentprogram was initiated in 1970 and completed in 1984. Duringthis time, the waterflood pattern changed from a centerlinedrive to a field-wide inverted nine-spot. In 1985, a reservoircontinuity study led to a 20 acre infill drilling program thatadded over 150 wells, changing the pattern configuration to afive spot. A Carbon Dioxide (COJ flood was initiated in 1993for the 20 acre five spot patterns.Like many other reef carbonates, the Salt Creek fieldexhibits extreme heterogeneity making permeabilitypredictions difficult. Conventional statistical analysis of thepermeability, porosity, and facies relationship from cored welldata did not reveal any reliable correlations that could be usedto predict permeability in uncored wells. As a result,surveillance and reservoir management efforts have relied ona reservoir characterization built from a storage model basedon porosity, rather than a flow model based on permeability.Scattered high permeability streaks have the potential toinduce prematurely high GOR, production declines, andreduced C02 utilization, all of which can lead to pooreconomic performance. As the COZ flood matured, the needfor a detailed and accurate reservoir description had increased,prompting the search for a better predictive technique.MethodologyIn the Salt Creek reservoir, a single porosity value cancorrelate to a wide range of permeabilities. Even within aspecific stratigraphic zone or facies, for a porosity value of say10%.,permeability can vary from 0.1 to 100 md, Correlationsbased on porosity only can, therefore, lead to erroneousresults. Our objective was to correlate permeability withmultiple well logs that are directly or indirectly indicative ofpermeability from cored wells. Towards this goal, optimal

non-parametric transforms were derived to maximizcorrelation between permeability and the well log resAn iterative procedure using alternating condexpectation (ACE) was used for this purpose. The retransforms were then utilized to predict permeabiluncored wells where only well log data were available.instead of using one single variable (porosity)independent predictor, we used a series of well log valour predictor variables.Although intuitive relationships existed between indlogs and permeability, quantitative relationshipscomplex, and varied from zone to zone as well as regregion. The complexities associated with the reef cardevelopment had made it impossible to predict the natthese relationships in advance. This, in turn, made stparametric multiple regression an unsuitable applicationproblem. As an alternative, we resorted to the use of thparametric optimal regression techniques torelationships between variables, detect inherent non-lineand finally to develop correlations to predict permeabilitApproach. The ACE algorithm provides a methoestimating optimal transformations for multiple regressioresult in a maximum correlation between a depe(response) random variable and multiple indepe(predictor) random variables. Such an optimal transformcan be derived by minimizing the variance of arelationship between the transformed response variablepermeability) and the sum of transformed predictor varxl, . . . .Xp (say, well logs). Our approach proceeds as foll(i)

(ii)

Develop optimal non-parametric transformspermeability and well log variables based on coredWV, 41(X1),..,4)(XP).A Win95~T based soGRACE7 developed at Texas A&M University wafor this purpose.For an uncored well, given a set of well log res{xli>...~ni} first estimate the corresponding trans{+l*(x,jj...j@p*(xp)} from step (1).(iii) Estimate the optimal transform for permeability usifollowing relationship.b*(Ji) = $+;(x/i)/=1(iv) Finally, predict permeability through back transformYr =~-[1~@/*(x/i)

Thus, our calculation involves p forward transformatio{xii,.. .~pi} to {~l(xli),... ,~P+(xPJ, and a bactransformation, Eq. 2. By restricting the transformationresponse variable to be monotone, we can ensure thatinvertible.

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PE 39667 PERMEABILITY PREDICTIONS USING NON-PARAMETRIC TRANSFORMATIONS AT THE SALT CREEK FIELD

The power of non-parametric transformations as a tool forrrelation lies in their ability to handle variables of mixedpe. For example, we can easily incorporate categoricalriables such as rock types and lithofacies into the correlationd also, handle missing data values without additionalplications. Our experience has shown that for most of theplications considered by us (petrophysical and PVT data),e non-parametric transformations ~,(1 and ~~ can betted by simple functions such as polynomials, power57This provided us with a rapid and.mctionsor cubic splines .werful alternative to traditional multiple regressionchniques for building correlations for a variety ofplications, particularly in the presence of several predictor

The Salt Creek Field Unit (SCFU) can be dividedea[ly into two major regions: the South Main Body and therthwest Extension. From a geologic setting an evaluation ofpositional environments also led us to believe that weould generate separate correlations for each area. Weerefore divided the field into these two regions andveloped separate correlations for each. We furtherbdivided the field stratigraphically into several verticalnes: C5, C4, C3, C2b, C2a, Clb and Cl a, which were allll defined by paleontological picks. The quantitative naturethe relationship between permeability, porosity, and variousher log parameters are different for each zone and each area.arious checks throughout the study allowed us to refine theeal and zonal divisions to achieve an optimum set ofrrelations, Correlations were developed with variousoupings to achieve the best overall result. The ACEchnique allowed us to integrate our geologicalderstanding of the reservoir into the numerical optimizationocess. A separate correlation was developed for each of thenes shown in Table 1.Table 1- Salt Creek Field Unit ZonesSouth Main Body Northwest Extension

zone No. of Data zone No. of DataPoints Points

C5 82 C5 207C4 120 C4 163C3 365 C3 262C2b 428 C2 126C2a 265 Clb 108Clb 308 Cl a 76Cla 294Total 1862 Total 942

s can be expected in a reef carbonate reservoir, zoneickness varied dramatically across the unit. The relativeickness of zones C2b and C2a were smaller in Northwesttension when compared to other zones. Hence, these twones were lumped together and simply named C2.

Depth Shifting. Before performing any type of regressanalysis on log responses or core data, all data mustcarefully depth shifted to assure that values are propecompared. Like traditional regression, the ACE methodsensitive to data misalignment and the rough nature of rawand core data. We compared density derived porosity val(DPHI) with core porosity data to depthshift the core dadjusting the reported core data depth to match the welldepth. As a final check, we found core porosity and DPvalues to match well.Data acquisition and correction. A regional map ofSCFU showing both well and core locations is shown in F3. We used data from 15 cored wells from South Main Boand 6 cored wells ffom Northwest Extension giving usdata point distribution between zones shown in Table 1.left out one cored well (N411) in the South Main Bodytwo cored wells (G517 and G520) in the Northwest Extensto independently verifi our correlations. The previoudiscussed zones were identified for all the wells, and data swere classified according to zones for the two areal regioWe made no attempts to group the data according to faciesprevious studies were unable to consistently correlate facfrom well to well across regions. Although further divisifrom zones into facies for correlation development mayoptimal from a geologic perspective, such divisions woreduce the data population in each division and may affectstatistical significance of the correlation. Thus, we found tzonal divisions provided both a good geolocharacterization of the reservoir while maintaining data bintegrity for the ACE technique.In general, we tried to honor the original data as muchpossible and reject only those points which appeared tooutliers. Although data smoothing is necessary for stabiltwo problems emerge from the process. First it reducesnumber of data points in the data population, and secondhomogenizes the database making it less representative ofreservoir. Thus, the tradeoff is to smooth the data enoughachieve a stable correlation, and at the same time not to lthe heterogeneous character of the high permeability strewhich plays a critical role during the COZflooding process.Log Selection. Although a variety of well log data wavailable from wells drilled after 1970, the majority ofwel[s at SCFU were driIled earlier and did not have a lasuite of modem well logs. By using only modem logswould have severely reduced the data base population, oagain compromising the ultimate correlations. We were forto strike a balance between log quality and quantity tultimately led us to limit the variety of independent variab(individual log types) in favor of a consistent field wrepresentation of data. As a result, we were unable tosome of the new, more sophisticated well logs for

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4 BARMAN l., SHARMA A.K., WALKER R.F. AND DAITA-GUPTA A. SPE

correlation because of their limited availability in a majorityof the wells.The log types selected were related to permeability, andwere available across the field. These logs were:1. Density log derived porosity (DPHI).2. Gamma ray (GR).3. Lateral Log Deep (LLD).4. Lateral Log Shallow (LLS).5. Neutron log derived porosity (NPHI).The DPHI and NPHI are indirect measures of porosity andthus indicative of permeability (although poorly correlated).

Gamma ray (GR) logs can identify shale content (particularlyin sandstones) and thus are a measure of permeability(particularly low permeability). The LLS and LLD logsmeasure the resistivity in the immediate vicinity of the welland deeper into the formation respectively. In the presence ofigh permeability zones, fluid invasion can be indicated by theatio of LLS and LLD (which will decrease). The LLS andLD together are another indirect measure of permeability. Inact, our studies found that the inclusion of LLS and LLD datain the analysis improved the correlation, better identi~ingigh and low permeability streaks as well as suddenrmeability changes in relatively thin vertical sections.

rrelation Development, The ACE algorithm attempts toinearize the relationship between the dependent and the sumf independent variables in the transformed space. Thus, aatural choice for the measure of correlation would be therelation coefficient defined as follows:P= +0)+,,(4] (3)4s(4=54/(0 (4)

[=1 or evaluating regression models, we can then estimate thefficient of determination, R2 =&. However, caution mustexercised in interpreting R2 since a good correlation in theansformed space does not necessarily imply good correlationetween the untransformed variables. We must examine thealidity of the correlation using independent means, forample, blind tests or statistical approaches such as bootstrapr cross-validations There are no hard and fast rulesncerning the interpretation of R2 since different data setsquire various acceptable bounds of error.910For our study ofrmeability in heterogeneous carbonate reservoirs we foundhat an R2>0.5 is acceptable. Porosity/permeability fits inore homogeneous sands commonly have an R2 of around7. We expect the correlation R2 in heterogeneous carbonateso be less. However, readers should be aware of severalations of R2 as a measure of correlation development.After classifying the data for each zone in both regions, wepplied the ACE algorithm. A visual basic stand-alonerogram, known as GRACE7 (graphical ACE), was used to

perform the ACE regression. The independent variableDPHI, NPHI, LLD, LLS and GR and the dependent vwas the natural log of permeability (in(K)). The Indepevariables are named for mathematical purposes only, anot necessarily strictly independent in our case. For exNPHI and DPHI are both measurements of porosity anin turn, indicative of permeability, but in reality they arelated to each other. We used these two porosity logstheir difference could be a measure of permeability.final analysis we realized an improvement by using theporosity variables in conjunction with each other rathejust one. Although the R2 between DPHI and In(K) isthan NPHI and in(K) in most of the zones, theseindividually were below our acceptance level.The ACE algorithm was used to derive optimal transfor each independent (well log) variable. Finally, the sall the transformed independent variables were linearly rto the transformed permeability, allowing us to depermeability correlationsExample. To illustrate our zonal permeability corretechnique, we will focus on the SCFUS South Mainspecifically zone C1a. From Table 1, we note that 294permeability values were available for the C1a, along wdata for all five selected well logs. Figs. 4a through 4escatterplots of the logarithm of permeability (in(K)) vs.GR, LLD, LLS and NPHI respectively. A linear regressIn(K) on individual well log variables yields a maximuof 0.49 (with DPHI) and a minimum R2 of 0.05 (withNext, using the GRACE software we derived optransformations to maximize the correlation bepermeability and the well log variables. The restransformations are shown in Figs. 4f through 4k, Noticthe transformations are non-parametric in the sense thaare derived from the data alone without assuming funcforms. Of all the measured transformed log variables,(DPHI,,) is maximum in magnitude indicating that DPHthe highest correlation with core permeability. Thconsistent with the results from individual correlation beIn(K) and the well log variables. Fig. 41 showtransformed core permeability vs. the sum of transformedlog variables. In accordance with the ACE algorithm, a ptransformed dependent variable vs. the sum of transfoindependent variable should yield a straight line havingto unit slope. A linear regression in the transformedresulted in the following equation,ln(K),,=l .0362[DPHI,.+GR,,+LLD,,+LLS,,+NPHI,,]with an R2 of 0.71. In comparison, a conventionalmultiple variable regression using SAS [ yielded ifollowing equation,In(K)=-l .53+30.0*DPHI-0.0 19*GR-0.002*LLD+0.00 1*

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39667 PERMEABILITY PREDICTIONS USING NON-PARAMETRIC TRANSFORMATIONS AT THE SALT CREEK FIELD

-4.57*NPH1 (6)th an R2 of 0.52. The ACE technique improved the fit by%, resulting in a carbonate permeability match as good asny acceptable sandstone permeability matches. Finally, wedict permeability from the well log data using thelowing equation,

DPHI~k+GR~~+LLD~~+LLS~~+NPHI~~] (7)Fig. 4k, In(K) is plotted against ln(K)~~, allowing us todict permeability by knowing ln(K)~~, which in turn isual to the sum of the transformed independent variables.

our knowledge, this study is the first full-scale, field-wideof the ACE technique for reservoiraracterization. Our experience shows that non-parametricsformations quantitatively identify the complex nature ofrelationship between petrophysical variables andmeability. Such relationship varies from zone to zone andion to region. The method is powerful, yet simple to use,remely computationally efficient (typical run time is lessn one minute) and enables us to develop correlations ins where traditional regression techniques lead to less thanirable results, for example, in heterogeneous carbonatesthe SCFU.In Table 2, ACE derived correlation R2 values arepared to the R2 from traditional porosityI)/permeability correlations.Table 2- Comparison of /? using DPHI only and

using ACE with 5 well logs.Zone R2 using /?2 using 0)4improvementDPHI only. ACE.South Main Body

C5 0.44 0.68 55%C4 0.65 0.76 17?loC3 0.49 0.68 39%C2B 0.45 0.54 20%C2A 0.59 0.67 14V0Cl B 0.45 0.63 40%CIA 0.49 0.71 45!40

Northwest ExtensionC5 0.35 0.51 4670C4 0,56 0.68 21%C3 0.14 0


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BARMAN l,, SHARMAA:K:, WALKER-RF. AND DATTA-GUPTA A. SPE

is is common for heterogeneous reef carbonate reservoirs.lthough we were unable to predict the whole range ofariation of permeability for one value of porosity, we didtain a fairly wide range of permeability similar to that foundthe core data.

he permeability correlation developed in this study waslidated using blind tests. The results are shown in Fig. 9.ells N41 1, G517 and G520 were not included in the data seted to generate our final correlation. The permeability valuest these wells were predicted using the ACE derivedorrelation and compared with core data. Overall, theredicted permeabilities based on log data agree well withore data excepting for small scale variations below thesolution of the well logs.

A field scale applicationtechniques, specificallydemonstrated its power topetrophysical variables in

of non-parametric regressionthe ACE algorithm, hasidentify relationship betweencomplex carbonate reservoirsystems. This has greatly facilitated development ofpermeability correlations from well logs at the SCFU, atask that has effectively eluded us in the past,. The ACE technique has been shown to be much morepowerful than conventional multiple variable linearregression techniques for deriving permeabilitycorrelations at the SCFU.. The ACE algorithm, although quite useful and versatile,does not eliminate the need for sound geologicunderstanding and careful screening during data analysis.

Non-parametric regression techniques have the potentialto become a valuable tool to predict permeability inheterogeneous reservoirs where conventional methods failto obtain an acceptable correlation,

ACE =C02 =DPf-11=GR =K=LLD ==LLS =In(K) =NPHI =R2 =~Rl .

X,,.. .,xp =Xl,... J,, =y =

Y=p.

alternating conditional expectationcarbon dioxidedensity log derived porosity, fractiongamma ray log, API unitpermeability, mdlateral log deeplateral log shallownatural logarithm of permeabilityneutron log derived porosity, fractioncoefficient of determinationinverse transformindependent data observationindependent or predictor random variablesdependent data observationdependent or response random variablescorrelation coefficient

OperatorsE() = mathematical expectation~0 ) = transformation for dependent variable@( ) = optimal transformation for dependent vari~l(o ) = transformation for independent variable 1$,(. ) = optimal transformation for indepevariable 1

Subscripts and Superscriptsi,j = indices for data observationI = index for independent or predictor variablpre = predicteds = summationTR = transformed variableAcknowledgmentsWe thank Mobil Exploration and Producing U.S.management for the permission to publish this study.References1.

2.

3.

4.

5.

6.

7.

8.

Wendt, W.A., Sakuri, S., and Nelson, P.H.: PermeaPrediction From Well Logs Using Multiple regressReservoir Characterization, Lake, L.W. and CaH.B.Jr.(eds.), Academic Press, Inc., Orlando, Fl(1986) 659.Johnson, W.W.: Permeability Determination fromLogs and Core Data, paper SPE 27647 presented a1994 Permian Basin Oil & Gas Recovery ConferMidland, TX, March 16-18.Molnar D.L., Aminian K. and Ameri S.: The UWell Log Data for Estimating PermeabilityHeterogeneous Reservoir, paper SPE 29175 presentthe 1994 Eastern Regional Conference and ExhibCharleston, WV, November 8-10.Yao C.Y. and Holditch S.A.: Estimating PermeaProfiles using Core and Log Data, paper SPE 2presented at the 1993 Eastern Regional ConferenceExhibition, Pittsburgh, PA, November 2-4.Datta-Gupta A., Xue G. and Lee S.H.: Non-paramTransformations for Data Correlation and IntegraFrom Theory to Practice, Fourth International ReseCharacterization Technical Conference ProceedHouston, Texas, March 2-4, 1997.Breiman, L. and Friedman, J.H.: Estimating OpTransformations for Multiple RegressionCorrelation, Journal of the American StatiAssociation (1985), 580.Xue G., Datta-Gupta A., VaIko P. and BlasingamOptimal Transformations for Multiple RegresApplication to Permeability Estimation from Well LSPE Formation Evaluation, June 1997.Xue G. and Datta-Gupta A.: A New ApproacSeismic Data Integration During Rese

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Characterization Using Optimal non-ParametricTransformations, paper SPE 36500 presented at 1996SPE Annual Technical Conference and Exhibition,Denver, Colorado, October 6-9.Milton, J.S. and Arnold J.C.: Introduction to Probabilityand Statistics: Principles and Application for Engineeringand the Computing Sciences, New York: McGraw-Hill,1995.. Jensen J.L., Lake L.W., Corbett P.W.M. and Goggin D.J.:Statistics for Petroleutn Engineers and Geoscientists,New Jersey: Prentice Hall, 1997.. SAS Statistical Analysis System, SAS Users Guide:Statistics, Version 5 Edition, SAS Institute Inc., Cary,North Carolina, 1985.

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BARMAN l., SHARMA A.K., WALKER R.F. AND DAll_A-GUPTA A. SPE

wig. l-Location of Salt Creek Field Unit, Kent County, TX.

Fig. 2-Structural Map of Salt Creek Field Unit, Kent county, TX.

Fig. 3- Regional map of Salt Creek Field Unit, Kent County, TX.

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0 BARMAN L, SHARMA A.K., WALKER R.F. AND DAITA-GUPTA A. SPE

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0.001 -, -0.001 0. 01 0.1 1 10 100 1000Core Permeability,md

Fig. 4m-Core Permeability vs. the Predicted Permeability using ACE, Zone CIA, South Main Body.D073 G029

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Fig. 5-Predicted vs. Measured Permeability of Well D073, South Main Body, and Well G029, Northwest Extension.

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BARMAN l., SHARMA A.K., WALKER R.F. AND DATTA-GUPTA A. SPE

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7-

.=

E!J==p:,!,!,:: ,,, ,,,,, ,,, ,, ,::,,::,;:,,,,,, ,,, :,,#,,.,,.::, :,,,,,,,,, ,,,,,,,:,,::

:,:.,

0. 01

,,, 1,,~ ~,:,;-. ,,

,1,,,

0. 1 1 10 100 1000Permeability, md

Fig. 6-DPHI and Predicted Permeability of Well G029, Northwest Extension.

0. 001 0. 01 0. 1 1 10 100 1000Core Permeabllty, md

Fig. 7-Predicted vs. Core Permeability of All Data.

140


13/13


1000 ..I . xxJ .% .Xx

6550

5&C0

eS50

6700

6750

6600

100

102g.6 1~

0. 1

0. 010 5 10 16 20 25 30 35

core Pormlty, Y.Fig. 8-Core Porosity vs. Predicted and Core Permeability of All Data.

N4116200

6250

6300

eEgn

6350

6400

64500. 01 0,1 1 10 100 1000

Permeability, md

G517,,, ,,.,,,:,, , ,::: Predicted,,,,,,,

,,,,:,,, ,., .,, .,,, , . , ,,: :1,,,,,,,,,, ,,,,,:,:

!::,,, ,::,,, ,,, ,,, ,,,,, ,, !:: ,,, ,,,,,,,,, ,,,,, ,,, ,,,,, ,,, ,,,

:&: :,,.

,:,,,,,, ,,: ,,,1 * -.,::,,, ,

I

:,,,

,1

,,

001 01 1 10 100 1000Permeability, md

62&3

6250

6300=s-gn

6350

6400

6450

6503

G520

0.01 0.1 1 10 100Permeability, md

9-Verification of Correlation by Predicting Permeability of Wells N411, South Main Body, G5f7 and G520, Northwest Extension. Theses are not used for Correlation Development.

Volume Issue 0 1998 [Doi 10.2118_39667-Ms] Barman, Indranil; Sharma, Arun; Walker, Richard;...

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Transcript of Volume Issue 0 1998 [Doi 10.2118_39667-Ms] Barman, Indranil; Sharma, Arun; Walker, Richard;...