Spwla 1985 Ww

SPWLA TWENTY-SIXTH ANNUAL LOGGING SYMPOSIUM, JUNE 17-20, 1985

LITHOLOGY DETERMINATION FROM WELL-LOGS: CASE STUDIES

by

Oberto SERRA, Pierre DELFINER and Jean Claude LEVERT(Etudes et Productions Schlumberger, Montrouge, France)

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ABSTRACT

A procedure combining log measurements with a lithofacies database for autom-atic lithofacies determination has been field tested for about two years in verydiverse types of formations. The paper. reports the results obtained and discussesproblems encountered in relation to bad hole, caving, mud conditions, gas effect,missing logs, incomplete data base, overlapping electrofacies and thin beds.

A preprocessing logic has been developed to eliminate or reduce possible electro-facies conflicts and improve the consistency and reliability of the answers. It seg-ments the studied interval into reservoir and non-reservoir zones, identifies the fluidsjand corrects for the effect of light hydrocarbons. This analysis makes use of allavailable logging data, including those not introduced in the lithofacies database(caliper, Ap, spontaneous potential, resistivity logs). Also, it reasons on the wellas a whole rather than level by level, applying rules of expertise and geologicalknowledge.

Several examples covering various environments and lithofacies are presentedalong with core descriptions when available. This confirms the reliability of theapproach.

Applications of this analysis to quantitative interpretation are also discussed.

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1 INTRODUCTION

An automatic procedure combining wireline measurements with a lithofaciesdatabase has been developed [1] and field tested for about two years in very diversetypes of formations. It has been implemented in a program named LITHO*, andmade available at the well site on the CSU”.

This procedure classifies a set of n log readings at a given depth level byreference to a prespecified lithofacies database. In the database, each petrographicrock type is represented as a volume in the n-dimensional space of logs. This isto allow for variations due to both geology and data acquisition. A rock type, bydefinition, has mineral proportions that vzwy between specific limits. Data acquisi-tion introduces variations of its own, independently of lithology, through pad con-tact, borehole rugosity, invasion profile, mud eifect, and bed shoulder influence. Thedimensions of each volume reflect the amplitude of all these variations.

Fluid content. can also cause important variations in log responses for the samelithology. By design, the lithofacies database assumes water filled pores.

The wireline measurements currently introduced in the database are naturalgamma ray, thorium, uranium and potassium contents obtained with the N(2T*tool (natural gamma ray spectrometry), density as measured by the FDC* or LDT”tool, apparent hydrogen index provided by the CNL* tool, sonic transit time, andphotoelectric index (Pe) coming from the LDT tool.

This procedure has been field tested extensively and the results compared withcore descriptions when available. The agreement is generally good. Figure 1 is anexample of lithological description in a complex shale-radioactive sand series withthe corresponding core description alongside.

Despite this success, problems were encountered mostly in relation with badhole or mud conditions, gas or light hydrocarbon effect, incomplete logging suite,incomplete database, overlap of lithofacies volumes, or thin bed situations. Thepurpose of this paper is to report our ongoing effort to overcome these problems.

The basic idea is to apply some crude and fast preprocessing logics beforeturning to the database for more precise answers. These logics make use of theinformation carried by other open-hole logs such as caliper, spontaneous potential,resistivities, density correction (Ap), which were not introduced in the databasebecause they are not really Iithological tools. Also, some amount of vertical analysisis introduced to reason on the studied interval as a whole rather than level by

level, applying rules of expertise, memory of the previous intervals and geologicalknowledge.

●: mark of Schlumberger

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2 PRINCIPLES OF THE CLASSIFICATION METHOD

In the database each lithofacies volume (more preciselyvolume) is represented by an ellipsoid in n-dimensional space,

“electro-lithofacies”which is interpreted

as a 95 % probability region of a multivariate gaussian distribution. This providesstatistical models for the distributions of the log responses expected for each lithofa-cies of the database. Note that some logs may be suitably transformed beforeentering the database; for example the gamma ray is used through its logarithm toaccount for greater dispersion towards high values.

Thus at each level one can compute the probability P(XI F’i) of observing theset of log responses X, given the lithofacies Fi (which we don’t know). Of coursethe interesting quantity is rather the probability P( Fi IX) of the lithofacies giventhe log readings (which we know). This probability, called the posterior probabilityof Fi given X can be computed using the well-known 13ayes

Pi P(XI F’i)P( J’ilx) = ~j~j P(X1 ‘j)

formula:

New elements come into play in this formula: the terms p~j called the priorprobabilities of the lithofacies. They represent the prior information that maybe available on the lithology before attempting the classification. For example,if the problem were one of diagnosing a disease, pi might represent the overallproportion of the i-th disease in the patients’ population - that is, before analyzingany symptom. In our problem the pi’s will be derived from external geologicalinformation (local knowledge), and from an analysis of logs not introduced in thedatabase. These pi’s play an important role as they are the means to pass on to theclassification algorithm the information already available on Iithology.

Why, one might wonder, not simply extend the database to include thoseother logs? The reason is the so-called curse of dimensionality. The ellipsoids ofthe database are constructed from their two-dimensional projections. With eightlogs there are (8x7)/2 = 28 crossplots to consider; adding three more logs, saycaliper, SP, and a resistivity about doubles this number (55). Wth a database ofabout 180 rocks this involves about 10,000 cross-plots! For the purpose of lithologydetermination it suffices to use some of the logs in a somewhat qualitative manner.For example: is there caving or not, mud cake or not, deflection of the SP or not ?There is no need to translate these cutoff operations into ellipsoid regions.

Most of the time, local knowledge or preprocessing logics result in setting someof the pi’8 to zero. For example if there are only carbonates in the went pi may be set

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to zero for all sandstones. The remaining pi’s are then generally set to one, indicatingthat all lithofacies that are present are given an equal chance (pi = 1 or pi = l/lVis equivalent for Bayes formula). Occasionally it may be interesting to boost somelithofacies at the expense of’ others. For example in caved intervals one might wantto make the prior probability of shales double than that of unconsolidated sands.

The classification procedure itself consists in maximizing the posterior probabili-ty of the facies at each depth level. The computed posterior probability provi-des an index of confidence in the result. A point falling outside of all 95 % el-lipsoids is classified as “unidentified”. This procedure (which can be shown tominimize the risk of misclassification) is applied successively to all levels of thestudied interval, in combination with some special logics described in [1].

3 HOLE CONDITIONS

3.1 BAD HOLE

This logic is activated when borehole rugosity, or caving, affect the readings ofpad tools, which in the lithofacies database concerns the density log. Rugosity isdetermined by averaging the derivative of the caliper curve over an interval equalto the pad length, and by Ap exceeding a threshold value. Caving is measured bythe differential caliper (caliper minus bit size).

In intervals where bad hole is identified, the density curve is discarded andthe choice within the database is restricted to a list of lithofacies which have beenspecified by the log analyst as likely to cause bad hole in the given area. This isachieved by setting the prior probalities of all other facies to zero. In general bad holeis due to shales, but unconsolidated sandstones, tight carbonates, sylvite, halites,and fractured rocks are also possibilities. Weighting among these possibilities can beintroduced by the log analyst to reflect their probability under the local conditions.

Figure 2 shows a section of a well before and after the application of the badhole logic. Before the logic two zones of radioactive sand are found on either side ofthe middle shale - this is because the density, due to bad hole, reads low (about 2-2.2 g/cc). Note also the lwo unidentified zoues wl.wu p~ dips (w 1.8 g/cc: such lowvalues would point to coal but the density is still too high for coal and At is toolow (from 80 to 100 ps/~t). By ignoring the density readings in the bad hole zone,shales are correctly identified.

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Caving is different from bad hole because it does not necessarily affect thedensity. Indeed, the pad contact can remain good even in case of substantial cavingduetothe geometry of thetool anditsweight, especially in deviated wells. In thiscase the density log is kept but the presence of the cave is combined with tests onthe SP and gamma ray curves to determine the main lithology of the interval: isit shale, unconsolidated sand, or fractures ? Prior probabilities are set accordinglyand consultation of the database permits refining the identification of the rock.

Figure 3 shows a section of a turbiditic sand-shale series on the scale of 1/40.Caving occurs between 1270 and 1263.5 m with a differential caliper of about 2inches. On the basis of density, neutron, sonic, and gamma ray alone it would beimpossible within this interval to differentiate a shaly sand from a calcareous shale.Indeed, as shown in Figure 4, calcareous shale plots inside the shaly sand region. Thepresence of caving, combined with the absence of deflection on the SP, the decreaseof resistivity, and the increase in neutron allows increasing the prior probability ofshales within this interval, and consequently to diagnose calcareous shale.

An unidentified zone appears at the top of the interval; it is due to a reductionin the density which becomes too low for a shale. The interpretation of this is a lossof pad contact. due to tilting of the tool as it leaves the cave. This effect extendsover a length of about 1.5 m, as expected. In such a case a postprocessing logic isapplied that patches the unidentified interval with the same Iithology as was foundbelow the cave boundary.

3.3 MUD CONDITIC)NS

Some common drilliug LLIUdaddi~ives adversely a~ect some log measurements.It is often very difficult to correct for these effects and usually the best procedureis to ignore the affected logs either completely or over selected intervals.

The presence of. barite in the drilling mud has a strong effect on the readingsof the Pe curve. An amount of about 1% of barite in the mud gives a Pe of about10. Since barite is not soluble in mud, a segregation of barite particles builds upwith time after circulation and as a result the effect on Pe is not constant. Thus itis preferable to ignore the Pe curve completely over the whole well.

Potassium chloride (KC]) in the mud, being radioactive, affects the potassiumreadings of the NGT tool. As KC1 is soluble in mud, its concentration can beassumed constant. However, the correct ion is still difficult as it involves preciseknowledge of: (a) the concwtration of KCl in the mud} (b) the dspth of i~wasjon,

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(c) the depth of investigation of the NGT tool. Again, it is preferable in suchcircumstances to ignore the potassium reading, at least in front of invaded zones.

The mud type is also a factor to take into account in the preprocessing logics.For example oil base mud precludes reasoning on the SP (there is no SP) andon mud cake (there is very little of it). Also, rules to diagnose invasion based onmultiple resistivity measurements generally do not apply since there is usually onlyan induction log. in the case of salt formations, the salinity of the mud has an impacton the conclusions drawn from caving: fresh mud dissolves salt but salt saturatedmud does not.

4 INFLUENCE OF LIGHT HYDROCARBONS

It is well known that light hydrocarbon or gas affects log responses in such away that representative points of hydrocarbon bearing reservoirs are displaced inthe n-dimensional space. As a consequence, they can be erroneously assigned toanother lithofacies of the database, or even fall in an unidentified portion of thespace.

To avoid such problems two different techniques can be used. One is to createlight hydrocarbon or gas bearing lithofacies for each reservoir rock. This approachincreases considerably the size of the database. The other technique consists indetecting the hydrocarbon effect and correcting for it before any attempt to deter-mine lithology. This second technique has been adopted.

Before making any correction of logs affected by hydrocarbon, the first stepis the detection of reservoir zones and the recognition of the nature of their fluidcontent.

4.1 RESERVOIR DETECTION

Intervals that correspond to shales or to compact rocks are distinguished fromthe reservoir rocks by analysis of the SP curve (positive or negative deflectionfrom the SP shale baseline), caliper curve (presence of mm-l cake), and resistivity

responses (separation between resistivity curves coming from different tools, in-dicating invasion profile and consequently porous and permeable formation), andgamma ray responses. These non-reservoir zones are analyzed separately by con-sulting lithofacies databases restricted to the expected shales or compact rocks.

When an interval has been recognized as a reservoir, a crude estimate of theporosity is obtained by cross-plot techniques or empirical relationships such as 4 =(i?#Iv+ I’#~)/9 [2].‘I’hen,knowing&, the saturation is computed using Archie’s

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formula and compared with a threshold value to detect the presence of hydrocarbon(Sw <0.5 default value). To identify the hydrocarbon (oil or gas ), tests are doneon several parameters depending on the data available. These tests check certain

(4N)m/ –h), (Jparameters such as N (= P,_ ~~f W~(~ma)a Covqed to ~e Or (Uma)a. For

example, a high separation between p~ and $$N,as measured by N greater than 0.7,is a strong indication of the presence of gas. This conclusion is reinforced when Swis lower than say, 0.15, and (~ma)= is lower than the expected value suggested byPe or (Uma)a. If N is less than 0.68, say, the reservoir is considered as oil bearing.

4.2 HYDROCARBON CORRECTION

When a gas or light hydrocarbon bearing reservoir has been recognized, acorrection is applied by substituting water for hydrocarbon in the response of thedensity and the neutron. This correction is made using S.0 obtained by Archie’sformula from R.O, or calculated from SWif no microdevice is available. Here, eitherthe density of the hydrocarbon is known or, if not, it must be estimated. This isdone either from pressure and temperature charts for gas, assuming a standardcomposition, or if a water bearing interval exists directly below, by assuming bothhave the same lithology.

Correction for gas or light hydrocarbon effects on the sonic being quite difficultit is generally preferable to simply ignore the sonic log in these intervals.

11 I.uusl be emphasized that corrections of lhe log readiugs mxxl UO1be veryprecise because the size of the lithofacies ellipsoids is such that it allows for moderateerrors in the corrections. Likewise, a correction for shale is not required because ahigh shale content would affect SP, gamma ray, and resistivity in such a way thatthe zone would no longer be recognized as a reservoir. Further, the shale effect wouldmask the gas effect.

The application of these logics and corrections is now illustrated by two fieldexamples.

Figure 5 shows a set of open-hole logs in a sand-shale series. From the inter-pretation of these logs, the lower reservoir can be divided into three zones: thelower zone (from 2456 to 2412 m) is water bearing; the middle interval (from 2412to 2402 m) is oil bearing; and the upper zone (from 2402 to 2378 m) is gas bearing.The LITHO program correctly identifies the lithology in the two lower zones. Itmeans that the oil does not affect the log responses too much and consequently nocorrection is needed. The upper interval, however, is unidentifkd. This is due to theinfluence of gas on the density measurement and the neutron response. The pb vs

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#N cross-plots for each zone @igure 6) ShOW that the representative points for thewater and oil bearing intervals fall into the ellipses corresponding to clean quartzsand, but the points corresponding to the gas bearing interval are completely outin the North-West part of the plot. The Pe value shows that the lithology shouldbe the same. After applying the detection logics and the corrections, the points fallback into the same ellipses and are now recognized as clean quartz sand.

Figure 7 shows a set of open-hole logs recorded in a carbonate-evaporite series.The lithology description given by the LITHO program before applying the reservoirand hydrocarbon detection logic is displayed in the central track. As can be seenjthere is an apparent complete change of lithology, from limestone to sandstone,in the hydrocarbon bearing reservoir. This is due to the influence of the lighthydrocarbon present in the reservoir (ph = 0.2 g/cm3), which moves the points,on a pb vs #N cross-plot from the e]iipses corresponding to limestone to the onescorresponding to sandstone. The absence of Pe values given by the LDT toolprecludes recognition that the lithology remains the same. Consequently, correctionsfor light hydrocarbon influence must be applied, and after that the LITHO programgives the correct answer.

5 CONFLICTS

Conflicts between lithofacies occur due to the fact that, to ensure good coveragein the space of logs, the multi-dimensional volumes have been designed with someoverlap. Also, volumes which are well separated in n-dimensional space may appearsimilar in a lower dimensional space (missing logs).

5.1 MISSING LOGS

A given set of logs may not allow, by itself, differentiation between two or even

several lithofacies. For example if the database contains different shale lithofacieswhich differ by their clay types, it will not be possible to distinguish them in theabsence of thorium and potassium measurements from the NGT tool.

The technique used is to deactivate certain lithofacies automatically (by settingtheir prior probabilities to zero) if the logs required to identify them are missing.Thus clay typing is attempted only if gamma ray spectroscopy data are available.Likewise, the diagnosis of heavy minerals is switched off if the bulk density readingis absent or dubious (bad hole).

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SPWLA TWENTY-SIXTH ANNUAL LOGGING SYMPOSIUM, JUNE 17-20,1985

5.2 REGIONAL GEOLOGICAL KNOWLEDGE

Often the main Iithofacies present in the well are known because a lot ofwells have been drilled in the area. In such cases it is recommended to constructa restricted database including only the expected lithofacies. This will eliminatepossible conflict between lithofacies, especially if the logging set is incomplete. As aside benefit, it will also make processing faster.

Regional databases may also be adapted to reflect local conditions. This mayinvolve renaming of some lithofacies or addition of special rocks (e.g. glauconitic orphosphatic facies).

5.3 GEOLOGICAL RULES

Some logical and simple geological rules are also introduced to reduce thedatabase to the lithofacies which have the highest probability of being present. Forexample, halite cemented sands are activated only if halite beds have been found inthe well (this is because halite cement comes from dissolution of halite beds by waterand reprecipitation in the pore space). In the same manner, radioactive evaporatesare deactivated if no anhydrite or halite beds have been encountered lower in thewell (this is because in a sequence of evaporates halite or anhydrite are depositedbefore potassic evapcrites). Radioactive igneous plutonic rocks, which can have thesame log responses as some radioactive evaporates or evaporitic mixtures, are limitedto basement zones.

6 THIN BEDS

When beds are too thin compared to the vertical resolution of the tools, logreadings are not representative of each bed individually, but of the mixture whichitself depends on the parameters of each bed and their volumetric percentage in theinterval investigated by each tool, In such cams the set of log responses can belongto a lithofacies which will give a rough idea of the mixture but not of the manner inwhich it is distributed, and, of course, will not be representative of each individualbed.

To have a correct idea of the structure of the interval, information brought bydipmeter is necessary. It reflects the internal organization of the interval, and allows

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a more detailed description of the formation by analysis of the resistivity curves ofthe dipmeter. In the example of Figure 3, the lithology resulting from the processingof the interval with the LITHO program is shaly sand between 1273.5 and 1269.9 m.As reflected by the dipmeter result display, this interval is a laminated sand-shaleseries, confirmed by core analysis (Figure 8 ). Medium resistive levels correspond tosand beds, low resistive beds to shale, and very resistive peaks to calcite cementedsandstones.

7 CONCLUSION

The preprocewiug logics introduced allow a more precise delermiuatiou ofthe lithofacies crossed by a well. The interest in this lithofacies definition is notpurely academic. It will help geologists to select the intervals where they requestsidewall coring or fluid sampling. It will allow log analysts to correctly select themineralogical model (main minerals present in the rocks, their concentration range,and of course their well log parameters) for quantitative interpretation. Indeed, theapproach used in the LITHO program is a powerful extension to multidimentionalspace of the traditional cross-plot analysis used to pick interpretation parameters.Further, including dipmeter information provides geological basis for selecting thetextural model (type of pore distribution and geometry, and consequently the mvalue), and the structural model (internal organization of the beds - massivej hetero-geneous or laminated -, type of clay distribution, and of course the related saturationequation). All combined this leads to more reliable and accurate answers.

ACKNOWLEDGEMENTS

The authors are particularly thankful to R. Bronnec and D. Aitken for theirhelpful advice and their contribution to this work. Thanks are also extended to theoil companies for granting permission to publish the examples.

1- DELFINER P., SERRA 0., PEYRET O. (1984): Automatic Determination of Lithologyfrom WeJl Logs 59th Ann. Tech. Conf. SPE of AWIE, Houston, paper SPE 13290.

2- SFRVICES TECHMQUES SCHLUMBERGER ( 1972): The essen tiah of log interpretationpractice.

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,-ABOUT THE AUTHORS

fromM.S.

Paris University with a B.S. degree anddegree in geology, then graduated as an

OBERTO SERRA graduatedfrom Besan~on University with aengineer, specializing in geology, from the Ecole Nationale Sup6rieure du P6troleet des Moteurs (IFP), in Paris. He started his career in 1958 with the ELF groupon field and well site supervision missions in the Sahara. In 1966, he joined theWell Logging Department of ELF, becoming its He,ad in 1969. In 1978 he joinedSchlumlxrger in Paris, where he. spent three years with the marketing group. In1981, he was transfered to Singapore as manager of Interpretation Development inGeology. Since October 1983 he works with the Interpretation Engineering groupin Etudes et Productions Schlumberger in Montrouge. In 1973, he won the MarcelRoubault prize awarded by the Union Fran~aise des G4010gues. He his a member ofSPWLA, AAPG, AFTP and UFG.

PIERRE DELFINER is program manager for Geology at Schlumberger EPSInterpretation Engineering, in Montrouge, France. Prior to that he was in chargeof Reservoir Description software development at EPS in Clamart, France. Beforejoining Schlumberger in 1980 he worked on oil industry research projects in Prof.Matheron’s Center for Geostatistics at the Ecole des Mines de Paris where he becameMaitre de Recherches in 1978. A graduate from the Ecole des Mines (1968), he holdsa doctorate degree from the University of Nancy (1971) and a PhD in statistics fromPrinceton University (1977). He is a member of SPE of AIME.

JEAN-CLAUDE LEVER’C joined Etudes et Productions Schlumberger, in Clamart,in 1969. Until 1982 he worked within the Computing Center to ensure generalprogram maintenance and provide technical assistance to users. In this capacityhe was involved in most of the log interpretation programs developed at Clamart,including GLOBAL. Since 1982 he is a software engineer within the Geology groupof Interpretation Engineering; in Montrouge. Jean ClaudeL-rt holdsa degreein computer science from the Conservatoire National des Arts et Metiers in Paris.

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_ OPEN-HOLE LOGS

Oberto Serra

Pierre Delfiner

Jean-Claude Levert

LEGEND 0 s”;;:$:;;E 1-1 SHALE

m COMPACTSHALE 0 SHALYSAND

Fig. 1 Example of results produced bv the LITHO program compared with the core description.

12 -

STRUCTURE


,,,’

,<,,,

DEPTH(ft )

7100

OPEN-HOLE LOGS I LITHO FACIES I

‘=’------’F-------”-iw- hr.. - 1 +—_ >

Y“— —I .- sJ-, -.-- -. -r-..-&_. –—

-c

---- ‘ f-- : “-‘‘: -“” “-” ~“> k:=---~.~-.–

4:.\

r-i [.-- t <

41 r

—L---

I IMH 1:111I Ill. .+,, II \ I I \ I I * II

1’i PM I 1 HI HI4: l>\ I 1 I I !:+-4[1

L-L-Pi

5.--+.

L... -.. . . -----------

-------------------

. . . . . . -. -

4-------- b-. 2-----.. .-.-,---- ----.-. ..,- -, . . -.-..-.,--- ------ --4-> ?-L2-.-.T-------- .-. - -. ----------- ------------- .-- L.-

,-, - --- .

~

- -s?..>=.--------. .----- --------. . . .--J. -4-

RADIOACTIVELEGEND n SANDSTONE ~ SHALY SANDSTONE n SILTY SHALE

-[ COMPACT SHALE m ARKOSE

-+---7<,,..: j

E—-..———.———..__-.———-.———-.—.——..———.——————-——

1————....—__L-,__——.———.—.-....—-_———-._——————-.——-.——..———.-.—-----— ..——.—— ———:-==.=

-.—.:~_—_-.——

......---------.._.:........4 -..

-------------

.+ -- . . . . . . . .,. .=,.. -

..: ,+

---.- -. .+---------------------- -...+-,---------- . . . .. . . . . .. --------------- . . .=-: :.~-~.;::+:+:-~.-.-.-.- ..-.-.-,.-. . . . ... . . --------------------..--.. ----- ---.-.- ----------

-1 UNIDENTIFIED

Fig. 2- Before correction for bad hole shale intervals are erroneously assigned to sandstones.

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m(m)

1265

1270

1275

SP-90 (rev)

---. !G4L ---6 (in)

~

!111’

II_+ . ..-., -;

~-- ,,- .-+.

i—-—: --

‘.

1’,.+ .;.

~

.. —..- 1.\)

I

4.t

:

/I

.).(\

,f

1,. . .

[

\,’

/’

~

r

Y

i

! -

\

,;

!. -

II

II

I

II

LEGEND

OPEN-HOLE LOGS

Sw00(%

..

3

),-

4-

-1-

LITHO FACIES I .-.,I

+BEFORE

CORRECTIONI Y

GR(API) 15

AFTER

CORRECTION

~~

- CALCAREOUS n UNIDENTIFIED

Fig. 3 - Poor pad contact when leaving the cave results In a too low density and unidentified Iithofacies.

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..,9.-, ,’

I,. .’.

‘AND*”SHALY

CAiCAREOUS SHALE

. I:.5,00 ~:oo ~:. 10.0 15.0 20.0 25.0 30,0 35.0 uO. O 45.0

NPHI

,.,.. ,-, ---

(~SHALY SAND,,, .

.

. :.::&. . .. . ... . . .. ...

mm CALCAREOUS SHALE$

.u

I

.L-40.0 60 0 80,0 100,031

120.0 ,,,.; At

pb: . . . . . . . ..

1,“”’:’

.

.

.,

,.. . ., .,

m SHALY ‘AND.$

mi “.. -.

CAiCAREOUS SHALE

. .,. . . .....” .. .. ..... . . . .X

.‘.5.00 0.00 5.0 10.0 !5.0 20.0 25.0 30.0 35.0 qO.0 w.: ON

NPHI

,,..,,

Fig. 4- Caving logic is required to separate overlapping Iithofacies.

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LITHO FACIES‘EPTI(m)

?35(

400

1

OPEN-HOLE LOGS

~) 1 (Win] 1,00017 (g/crn3) 2 ;

5P

-60 (rev) %s.

00[”! ) GRI (API) 150

BEFORE GAS

CORRECTION

(::1) 15P,

I (b,/~) 1 }60_ _d CNL (

l;——-

AFTER GAS

CORRECTION

3.-----------;_=:~=-.--

S .=.

“i

——..

t

“i-7/:>.—.

,.—.

—

} ““”’””’.,...

— .-..,.,

,.,

\m QUARTZ SAND= COMR4CTEDSHALE

EAVY MINERALS

LEGEND D UNIDENTIFIED n GRAYWACKE

M ~A:Y :N:M = sHALE= SHALE WITH HE

Fig. 5- A correction is applied to identify Iithology in gas bearing zones.

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SP

WL

AT

WE

NT

Y-S

IXT

HA

NN

UA

LL

OG

GIN

GS

YM

PO

SIU

M,

JUN

E17-20,

1985

,.,

I o.Ao...:

0.10.2

h.z

9.2~.z?

2“2E

OH

H

..

In.-lnmm‘50~co‘5

.-

L

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mft )

200

300

1400

Cly __ ,x-—_-(in)

—.-

———

—.—.

—

——

—.

—.—

——

_—–:.—i

,

--—..—i

i,,..—— —.

——

,

---+ ----

—-L—

—-

—

OPEN-HOLE LOGS

LEGEND

Fig

LITHO FACIES I

SEFOREGASCORRECr~ I

I

=-lAFTER GAS

CORRECTION

- ANHYDRITE m WL:S!’W: ~ TIGHT LIMESTONE

cd f!wwruc m LIMESTONE ~ DOLOMITE

m ;:::;;;;ICm %XI%”’T’C m t&GE’k%%”s

= SANDSTONE m UNIDENTIFIED

7- Before gas correction limestones are assigned to sandstones.

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PROGRAM LITHO PROGRAM DLIALDIP PROGRAM SYNDIP

EbEEidl i I DIP ANGLE U DIRECTION

1I 111 r

EMEX-CORIIECTEDSHOT CURVES

,, ., >,“.”r

““”A “.. .1.., !,!”--’, . . . ,,, aw,,,m

Fig. 8- Higher At readings identifies limestone as chalk. High Pe values indicate heavy minerals and hardground.

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Spwla 1985 Ww

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Transcript of Spwla 1985 Ww