GEOPHYSICS, VOL. 52, NO. 12 (DECEMBER 1987); P. 1665-1676, 14 FIGS.

Automated stratigraphic interpretation of well-log data

Xuanzhi Wu* and Edo Nylandt

ABSTRACT

A computerized well-log stratigraphic interpretationsystem based on artificial intelligence can be seen as twosteps, contact recognition and interval identification,Unlike previous approaches to mathematical zonationwhich are essentially statistical, consideration of thegeologic environment is included for effective interpreta­tion, Following the logic of human experts, the systemfirst determines the log signature of the contacts, Analgorithm incorporating linear regression with variablebreakpoints is used to describe the structure of logcurves. Log features are mapped to the assigned signa­ture of contacts, Multiple logs are taken into account toset up the final set of contacts, which divides the testborehole into a string of intervals with attributes. Apattern string of intervals is based on the integratedgeologic column for the section where the borehole islocated, A string-to-string matching program then de­termines the optimal map of the test borehole as thepattern to generate a geologic column based on thecomputer suggestion.

The cost formulas for matching represent knowledgeabout the stratigraphy of the area under study, and thestring-to-string matching algorithm includes this knowl­edge of stratigraphy, In test boreholes in an oil-sanddeposit, 86 percent of the computer identifications ofintervals were consistent with those determined by geol­ogists from core descriptions, suggesting that the designconcept and computer algorithm are consistent with thecharacteristics of the interpretation problem,

INTRODUCTION

Most existing computerized well-log interpretation systemsdeal with the maintenance of a well-log data base and evalu­ation of formation fluids, but rarely, if ever, with stratigraphicinterpretation, Stratigraphic interpretation begins with zon-

ation and zone correlation, and there is an extensive literatureon this subject (Shaw and Cubitt, 1979), The majority of thealgorithms are statistical, and the interpretation is basedupon the maximum crosscorrelation of zones in two wells,Crosscorrelation techniques run into trouble, however, whenstratigraphic units differ in thickness or are truncated due toerosion, faulting, or thickness changes from differing rates ofsedimentation, Stretching of zones alone or moving-windowcorrelation, or a combination of both, cannot ensure geologi­cally meaningful correlation,

Research in this area has turned to pattern recognition ofstructure and artificial intelligence techniques (Davis et al.,1981; Smith and Young, 1984; Bonnet and Dahan, 1983;Cheng and Lu, 1985), The difficulty in these approaches is thatintegrated induction, reasoning, and judgment of contrary evi­dence play an essential role in geologic interpretation, Theproblem is basically an intellectual one, and it is generallyaccepted that common numerical processing techniquescannot deal effectively with such kinds of problems,

The key to computer geointerpretation is to explore thelogic of human interpreters and follow this logic in designingthe computer software, The relevant techniques such as pat­tern recognition, expert systems, and image analysis have beenunder development for more than two decades, They havebeen applied to medical diagnoses and identification oforganic compounds. For example, the PROSPECTOR system(Duda et al., 1979) provides consultation services for mineralexploration. Although many other powerful algorithms havebeen published, we should apply them to our problem withcaution, Their underlying axioms do not always coincide withthe way the geointerpretation of log data is done,

We have developed a system (well-log interpretation usingartificial intelligence techniques) (Figure 1) to show what acomputerized stratigraphic interpretation system can do,There are two subtasks: contact recognition (zonation), andinterval (zone) identification. The next section describes ourcontact recognition routine, and a section on the intervalidentification approach follows. We include an example of ap­plication of the system using data from boreholes near anoil-sand open-pit mine in Athabasca, Canada,

Presented at the 55th Annual International Meeting, Society of Exploration Geophysicists, Washington, D. C. Manuscript received by the EditorDecember 19, 1985; revised manuscript received May 14, 1987,*Instituteof Mineral Deposits, Chinese Academy of Geosciences, Beijing, People's Republic of China,;Department of Physics, University of Alberta, Edmonton,Alberta, Canada T6G 211.e1987 Society of Exploration Geophysicists. All rights reserved.

1665

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1666 Wu and Nyland

Segmentation oflog curve

in FORTRAN

Geologicinterpretation

inUTI LISP

defaultparameters

edit

display

user controlover parameters

Users

ContactRecognition

inUTI LISP

Interval Parameterextraction

in FORTRAN

edit

Contact revisiondisplay and editing

in FORTRAN-IG

FIG. 1. A schematic of the design.

CONTACT RECOGNITION

FIG. 2. The contact recognition scheme.

Zonation is a critical step in stratigraphic interpretation ofboreholes. Shaw and Cubitt (1979) divided the previous workin the area of zonation or segmentation into two groups:variance-defined zones and mean-value-defined zones. Bothmethods try to determine the zones directly by dividing aborehole into segments which are in some sense homogeneousand which have significant differences in such statistics asmean or variance from its neighbors. These two methodssucceed only with data sets with favorable characteristics(Hawkins and Merriam, 1979). Instead of using such statisticaldecision making, we apply a heuristic approach. Rules describ­ing the log signature of contacts are retrieved from log ana­lysts as a pattern, and the computer recognizes the contacts bymatching the features of a log curve to such signatures.

An iterative linear regression with a program of variablebreakpoints is run to segment the log curve (Figure 2). Thealgorithm is similar to earlier suggestions, but our segmenta­tion is different from zonation. Segmentation describes fea­tures of the log curve, and the breakpoints defined are notequivalent to contacts. This simplifies the segmentation prob­lem and leaves enough room to encode expertise on contactrecognition. The well-log signature of a contact includes, butis not limited to, any significant change in mean or variance ofa log between adjoining segments.

The procedure is a piece-wise curve fitting in which seg­ments of a log are fit by straight lines. The allowable deviationfrom the line is defined by the noise associated with the welllog, where noise is the variation of response caused by factorsnot related to the contacts. This noise level is calculated bymoving-window smoothing and then computing the residual.The width of the window is equal to the minimum thickness of

ILRV

CR

input:name of log, data

controlling parameters

1downward

linear regression

1try upward and

downward to improve

segment encoding Ifeature of contact

recognition and editing

to contact editor

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Automated Well-!og Interpretation 1667

coo

oo

I'Vo

65.

o.

oo

coo

WELL: AA06-0 1-94-12W4L

Gamma PorosityN ... Ol CD a I

a a a a d a 0I I

C11 ;; C;; '" ec w wC11 0 0 C11 0 C11

0 0

I'Vo

.j:>. 20. .j:>.

0 0

0CD"0-zr- en en3 0 0--

FIG. 3. Segmented gamma and porosity logs; the thin lines are log curves and thick ones are the regression lines.

the expected segments, which is closely related to the mini­mum thickness of resulting zones.

Many other segmentation approaches are available (Haw­kins and ten Krooden, 1979). The most general approach, thesplit-and-merge algorithm (Pavlidis, 1977), usually works wellbut consumes large computer resources, We choose a moretime-effective method. Variance analysis and tests of indepen­dence locate the initial breakpoints of segments. Iterative pro­cessing then refines the locations of the breakpoints andvalues of parameters in the regression equations. In Figure 3,the thin lines are the log curves of the test borehole and thethick lines are regressions defined by the computer. Very littleiterative processing was needed and computing effort was neg­ligible.

A contact recognition program (written in UTILISP 1) is

applied after the segmentation program. It first describes theattributes of the segments, in which the essential parameter isthe slope of the fit line. A rapidly changing geologic environ-

1 University ofTokyo Interactive LISP.

ment containing a contact might yield a rapidly changing re­sponse. Four thresholds sl' S2' S3' and S4' where SI < S2 <o < s 3 < 54' are assigned to encode the slope. If 5 denotes thelog response slope of a segment,

the log response of a segment is rapidly increasing

if s > S4;

the log response of a segment is increasing

if S4 2 s > S3 ;

the log response of a segment is flat if s3 2 s > s2 ;

the log response of a segment is decreasing

if S2 2 s > SI;

the log response of a segment is rapidly decreasingif SI 2 S.

Another important parameter is the increment defined asthe change of the response through a segment (in­crement = slope x thickness).

Obviously, more attributes of the segment could be intro­duced, but we have found that for contact recognition, themean, the slope, and the increment are adequate.

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1668 Wu and Nyland

A set of production rules distinguishes those segmentswhich might contain a contact. Another set of rules is used toedit the defined segments and to locate the contacts with con­fidence levels based on their log-response features. Such a pro­duction rule has an IF (condition) THEN (action) form. Twoof several rules used are

IF (I) response is rapidly increasing, and(2) response increment is great,

THEN add the segment into the list of candidatesegments containing a contact.

IF (1) two adjoining segments are both candidatesegments with steep slopes, and

(2) their slopes have the same sign,

THEN (1) let the contact be in the middle of thesteeper one, and

(2) combine the two segments, and(3) set the slope of the combined segment

equal to the steeper.

Estimates of the degree of confidence of a contact are basedon an essentially empirical use of the features of the preceding

segment. If the preceding segment had a slope Sj and an in­crement Ii' the confidence of the contact is calculated as

I 4 Is'lc, = Wi ---'- + J.v, - arctan ---'- .t; 1t sm

Here 1m is the expected mean value of the increments. Thismean value might be the mean of increments of the segmentsin the test borehole, or a value preassigned in light of knowl­edge about the area. Sm is the average slope assigned in asimilar way. The second term yields the angle arctan IS;/SmIof the best-fit line with normalization factor 4/1t, and W; andJ.v, are weights of increment and slope, respectively. This for­mula is chosen to emulate human interpreters' visual zonationin the case where the increment and slope of the log responseof a segment play a major role in contact definition. In­terpreters commonly choose a proper scale to plot the log andmake judgments according to the angle the segment makeswith the depth axis rather than the slope of the segment de­fined as the rate of change with depth. We attempt to mimicsuch evaluation by the second term in the confidence formula.Obviously assignment of parameters is subjective and otheritems should be included when more attributes are used torecognize a contact. After all contact confidences have been

Gamma Resistivity Porosity

06-01-9~-12W~

- 20. +-_-'---.

-60.

Es:o..CDQ

-80.

-100.

BLOW UP

HOVE UP

OOWN

SHOW RLL

GRHHR

RESISTIVI

POROSITY

OENSITY

SELF POTE

THRESHOLO

ROD NEW

DEL OLD

STOP

l.-.!.L,J----1I----'r.m:SH 0L0·0 . 33

FIG.4. Adapted from the screen display of a machine interpreted sedimentary sequence showing all contacts picked bythe program in the fifth column from the left and those accepted by our interpreters in the sixth column. The townshipnumber of the test bore hole is at the top right above the edit menu. From top to bottom the edit comments are:expand the scale of the current display, display the logs for a shallower depth, a deeper depth, show all logs in thecurrent suite, show the gamma log, the resistivity log, the porosity log, the density log, the self-potential log, change thethreshold for contact detection, add a new contact, delete an existing contact, stop running the program. The last lineindicates the threshold is currently at 0.33.

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Automated Well-log Interpretation 1669

(a)

(b)

a,blI

I

• ··e.. ·o..a j bi

al

bl··• ··aH ·b.

I a ~ bJI

The introduction of the notion of confidence is crucial tothe definition of computer-recognized contacts. It is difficult toset absolute thresholds on the segments that contain contactsand those that do not. If, however, we systematically reducethe contact-recognition threshold, more subtle geologic transi­tions can be indicated. The final set of edited contacts can bedefined by raising the confidence threshold or by choosingcontacts with the highest confidence from the initial set. Thenumber of remaining contacts should be somewhat greaterthan the reasonable number of geologic contacts in order toensure that all true contacts are included. The final set ofcontacts divides the test borehole into a set of intervals, orzones, and the subtask of zonation is accomplished. Usefulattributes of zones are then extracted (Figure 1).

INTERV AL IDENTIFICATION

FIG. 5. Illustrates the idea of the cost of a match.

calculated, they are normalized to their maximum value toensure that the maximum is 1.0.

After all the logs have been processed, a contact-editingmodule first relates the recognized contacts from different logs.Two contacts defined by different logs within a range of lessthan the minimum thickness of zones are combined, and aweighted sum of their confidences is assigned to the combinedcontact. The weights clearly reflect the sensitivities of logs tocontacts. The procedure runs recursively, suggests a set ofrefined contacts, displays them on a graphics terminal screen,and waits for comment and editing.

The editing functions include expanding the plot in a depthrange, moving the range up and down, showing all availablelogs or part of them, changing the threshold of confidence,adding a new contact, and deleting an existing contact. Alloperations are done on a graphics screen.

Figure 4 is a copy of the screen at this point. It showsgamma-ray, resistivity, and porosity data (the latter from thedensity log). Computer-recognized contacts from each log aredrawn with the log curve. The refined set of contacts is alsoillustrated by a set of lines proportional to their confidence.On the right is the well name and the contact-editing menu.After raising the confidence threshold from the default (0.01)to 0.33, the edited set of contacts is displayed. To evaluate theperformance of the program, the geologic column based onthe core description is also drawn next to the computer­recognized set of contacts. The comparison shows that themachine recognized almost all the stratigraphic contacts andthe edited set contains the significant ones.

JD(i,]) I--

1at

FIG. 6. String-matching operations.

a

Stratigraphic identification of intervals is another patternrecognition operation, but it is much more complicated be­cause the ranges of responses of different types of rocks anddifferent stratigraphic units overlap. Unambiguous recognitioncan only be achieved if the evaluation considers the particulargeologic environment in which the test borehole is located. Ina different environment the same response could representdifferent units. Problems also occur because human judgmentis based on broader knowledge and an individual well inter­pretation is biased by the interpreter's foreknowledge of thegeology.

The key to geointerpretation with a computer is to mimicthe logic of human experts. In addition to this criterion ofplausibility of the inference, there is another important con­sideration. Geologic interpretation of log data requires con­sideration of the whole log, not just the local part beingexamined. For example, in interpretation of a borehole, theborehole is divided into several intervals and their litho­stratigraphy is identified. However, no interpreter identifies aninterval without considering its adjacent intervals, becausethere is always a preferred order of geologic beds in the sec­tion where the test borehole is located. As a consequence, each

a1 b1··· ··aI_I ·b.,a1 b1

(c)

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1670 Wu and Nyland

~t:)

!!l!:;

~~O)tJ4'\ ~

<"/lo;)

FIG. 7. The cost of various matches, the measure of dissimilarity.

FIG. 8. The transference tree associated with Figure 7.

I»-lI)-

Z0::­w­E-<

b:1»a....

lI)

3 5 7 9 11 13 15 17 19TEST

interval identification will affect the interpretation of the adja­cent interval. In fact, the data set of a borehole is treated as acolumn and matched to a pattern column which includeslithostratigraphic column and log responses. We emulate thisprocess with a string-to-string matching algorithm.

The string-to-string matching technique, first proposed forthe analysis of natural language, compares two strings of sym­bols and uses substitution, deletion, and insertion to convertone string to another. One of the measures of similarity of twostrings, the Levenshtein distance (Levenshtein, 1966), is thesmallest number of such operations required. Smith andWaterman (1980) used this technique for stratigraphic corre­lation of outcrop sequences, and Liu and Fu (1982) introducedthe algorithm into seismic discrimination. The algorithm isadopted here and extended to form the framework of ourborehole stratigraphic identification module.

As the result of contact recognition, a test borehole and thepattern columns are each divided into several intervals withattributes. We now compare the set of strings and find the bestmatch. Let Am = at' a2 , ••. , ai' ... , am and Bn = bl , b2 , ••• , bj ,

... , b; denote the string of intervals of the test borehole andthe string of pattern intervals, respectively. Ai = a r- a2 , ... , a,is a substring of Am' and Bj = bl , b2 , ... , b, is a substring ofBn • The matching of Am to Bn can be decomposed into partialmatching of their substrings. Three kinds of operations(Figure 5) are used to correlate Ai to Bs

(1) Matching. This simply adds the identification ofinterval a i with bj on the basis of matching of substring

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Automated Well-log Interpretation 1671

mmimum cost of all such possible correlations. Formally ifD(i,j) is the measure of dissimilarity between A; and Bj :

D(l, I) = Cm(l, I)

D(i, I) = D(i = I, 1) + Cd(i, I), m ~ i > 1

D(I,j) = D(l,j = I) + C;(I,j), n ~j > 1

D(i,j) = min [D(i = I,j = I) + :- (i,j), D(i - l,j)

+ ~ (i,j), D(i,j = I) + C;(i,j)}

Ai-I to substring Bj _ l • Since Q j would rarely be thesame as hj , the penalty for this difference is estimatedand denoted as Cm(i,j).

(2) Deletion. This deletes the contact between Qj_1

and a, and identifies the combination with b, The costof this operation is Cd(i,j).

(3) Insertion. This inserts an interval like hj into Ambetween the Qi and Q;+ I' and lets A j and Bj match. Thecost of this operation is Cj(i,j).

The accumulation of the costs of matching at each stepbecomes the total cost of the proposed match. It is obviousthat the best matching is the one which costs the least. Wedefine the measure of dissimilarity between substrings as the m ~ i > 1 and n ~j > 1.

I NTERBEODED COARSEN 1NG UPWARD

1N18EDOEO COARSE GRA INED

The dissimilarities of n x m pairs of substrings form amatrix (Figures 6 and 7). The definition is recursive and thecalculation moves from the lower left corner to the upper rightcorner. For each calculation of partial matching, the chosendirection is recorded by adding a branch to a transference tree(Figure 8). The eventual result is the dissimilarity between thetest borehole and the pattern. The optimal correlation of theirintervals is found by tracing back the transference tree. Figure7 shows the dissimilarity matrix of a test borehole by plottingit as a three-dimensional surface, and Figure 8 shows its trans­ference tree. The best-matching path is the thick line in thesetwo plots. The correlation process is rather like finding a waythrough a rugged range of mountains, where the best way isthe one that follows the major valley.

The best match shows the optimal correlation of the twostrings and suggests how the geologic column of the patternshould be mapped onto the test borehole. In most oil fields thestratigraphy is not too complicated and the depth of a strati­graphic unit does not vary much. In this case, limits of thedifference in depth between the test interval and the correlatedpattern interval can be assigned. The upper left area and thelower right area of the dissimilarity matrix are therefore un­reachable, and the useless branches of the transference treecan be truncated to save computing time.

It is important that the string-to-string matching algorithmsare only taken as a framework for stratigraphic identificationby computer. One cannot expect that a geologic interpretationproblem could be formulated as a single syntactic matchingalgorithm. The key technique is still use of prior geologicevidence. All the data available about a test interval, as well asall the knowledge relevant to the correlated pattern interval,should be considered in matching. Enough room is left for thesystem designer and the user to apply their expertise to guidethe identification of stratigraphy.

One problem is the estimation of the costs Cm, Cd' and C j '

It is not necessary for different pattern intervals to use thesame formula to estimate the cost of matching or to assign thesame limits on depth differences between correlated intervals,because different pattern intervals might be characterized bydifferent kinds of log responses and their stability might vary agreat deal. It is also not necessary for different test boreholesto match the same pattern. The working pattern will usuallychange from one section to another.

All this knowledge is encoded into a rule base in the form ofproduction rules and an inference engine searches for the solu­tion in the production system.

INTERBEDDED r INE GRA INED

fiNE" GRAINED

I :

! INTERBEOOEO r IN( GRR INEO

CORRSEN I NG UPWARDS

rlNE GRAINED I

I INTERBEDDED CORRSEN JNG UPWARD

r!N[ GRAINED I

!NTBEDDED COARSE GRRINEDI

COARSENj NG UPWARD

INTBEOOED COARSE GRR INED

I r!N[ GRAINED

i

I NTF::RBEDDED 11[0I UM GRR I NED

'.jo

MEDIUH GRAINED

FIG.9. An automated interpretation of a gamma log.

GAMMA

0",m 0

~I

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1672 Wu and Nyland

GAMMA GAMMA GRMMR

'"o

2.

'" '" '"a 0 0

~

0- 0- ..a a

0CD"'0-+ .:Jtr !

'SCo s d. cc <;o 0

~;4.

10-

i/.

(a) (b) (c)

FIG. 10. (a) Machine-generated segments. (b) Machine-edited segments. (c) The result of editing by the user.

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Automated Well-log Interpretation 1673

FIG. 11. The heavy oil deposits of Alberta.

~ Athabasca

PARAMETERS OF THE SYSTEM

Our interpretation system contains both rules and parame­ters. The values of the parameters are knowledge as much asare the rules; and we summarize the free parameters and theirmeanings here.

The user can assign the minimum thickness at the strata,which determines the degree of detail of the segmentation aswell as the details of the features of the log curve. Thresholdsof log features encode the features of log response of segmentsin degrees such as "rapidly increasing" and "large increment"for gamma ray, or "flat" and "low" for porosity, etc. Theseparameters are usually determined by examining the well logsof the test suite.

We use the allowable difference of depths of contacts andthe weights of logs when summing the confidences of thesecontacts to calculate the confidence of the combined contact.The default depth difference is the minimum thickness. Theweights of logs in the confidence formula reflect knowledgeabout the sensitivities of logs to contacts. A default set ofweights can also be assigned for certain geologic environ­ments, but local experience is useful in refining these values.

We can also use the confidence threshold of the final set ofthe contacts. Instead of a threshold of confidence, the numberof final contacts can be assigned and the computer then

~Suffield

Lloydrninster

Wabasca

oIo

Calga.'Y

Edmonton.

Peace River

"Carbonate Trend

fiB}] Cretaceous Oil Sands

~ Cretaceous Heavy Oils

AbandonedChannel

EstuarineEnvironment

Fluvial Channel

• -. C::;;PondSc::::=: MudsS--

FIG. i2. Hypotheticai deposition model of the McMurray formation, from O'Donnell and Jodrey (1984) courtesy of theSociety of Mining Engineers, American Institute of Mining, Metallurgical and Petroleum Engineers.

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1674 Wu and Nyland

chooses the contacts with highest confidence. This number isusually somewhat greater than the expected number of geo­logic contacts.

Parameters assigned to represent local knowledge forstring-to-string matching include the weights of logs in costformulas and the allowable range of depth differences betweenthe pattern interval and the test interval. The former are partof the signature of this pattern interval, and the latter is anindex of the complexity of the stratigraphy. An index of thestability of the sedimentary environment in which a stratum islocated is the weight of the insertion operation in the costformula. A high weight makes the insertion of a pattern inter­val into the test borehole, or the deletion of that pattern inter­val from the test borehole, more difficult and therefore a highvalue represents a stable sedimentary environment.

The subtask of recognition of true geologic contacts is ac­complished after mapping by simply accounting for the differ­ence in depths between a geologic contact and its log contactpattern (Figure 14). Thus we make the determination of pa­rameters even easier because the log contacts of the patternare recognized according to the pattern suite of the logs by thesame process with which the parameters are then applied tothe test suite of logs. The same effects of the chosen parame­ters (for example, an overrecognition of contacts caused by alow threshold of confidence) are then present in the pattern setof log contacts as well as in the test set of log contacts and theresulting stratigraphic interpretation can still be reasonable.

TEST RESULTS

Natural gamma radiation records from 1212 to 1818 mdepth are shown in Figure 9 (for convenience, we subtracted1212 m from all of the depths). Initially the program dividedthe curve into 65 segments (Figure lOa). The contact recogni­tion program recognized and edited contacts to display 24intervals on a screen (Figure lOb). The user deleted some con­tacts and modified the location of the contact between inter­vals 9 and 10, and the contact petween intervals 21 and 23.Figure lOc displays the final contact definition. The systemretrieved the mean, slope, and variation of the gamma radi­ation about the regression line from each interval and contin­ued with the geologic interpretation. It found in its knowledgebase that mean value specified fine or coarse grained; slopeindicated increasing fineness, increasing coarseness, or notrend; great variation of the log indicated an interbedded in­terval. The program mapped the features of the intervals usingthese rules and suggested the interpretation in Figure 9.

In the second test we considered log suites in 17 boreholeslocated near an oil-sand open-pit mine in the Athabasca area,northern Alberta, Canada (Figure II). The design of the back­ground knowledge depends on knowledge of the geology ofthe area. The lower Cretaceous McMurray formation is acomplex succession of fluvial, estuarine, and marine depositsunderlain by the Waterway formation of the Upper Devonian,a marine carbonate formation. Overlying the McMurray for-

FORM.

SEDIMENTRRY FACIESTRRNSGRESSION..

LOG RESPONSESGAMMA RESISTIVITY POROSITY

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MRRINESYMBOL

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el

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..,/ e2

el

~ f

~-----

REGRESSION

FIG. 13. Pattern column of the test area.

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Automated Well-log Interpretation 1675

mation is the Clearwater formation, a marine clastic depositsuccession. The oil-impregnated strata are contained withinthe McMurray formation. The regional geology has been de­veloped from a model originally suggested by Stewart andMacCallum (1978) and refined by Syncrude's geologists(O'Donnell and Jodrey, 1984). The model (Figure 12) indicatesa tripartite layered sequence of fluvial, estuarine, and marinedeposits, with internal lateral complexities and unconformities.

On the basis of the regional geology and a discussion withSyncrude geologists, a type column (Figure 13) was developed.Major sedimentary facies in the McMurray formation in thetest area are (Table 1) simplified from O'Donnell and Jodrey(1984).

It is suggested that under an overall transgressive sequencethere are three subcycles of transgressive and regressive de­posits within Middle McMurray (Km2), and two in UpperMcMurray (Km3) with corresponding cycles of log responses.More detail about the pattern building and knowledge repre­sentation can be found in Wu and Nyland (1986).

A test section containing seventeen boreholes was used totest our system in this context. A gamma radiation log, poros­ity derived from a density log, and resistivity log are availablein all holes. To evaluate the system, the computer suggestedstratigraphic columns be compared to those defined by geol­ogists on the basis of core descriptions (Figure 14).

It seems that neither the rules nor the data used are sharpenough and a refined procedure is required. Nevertheless, thepreliminary performance is encouraging. A total of 350 inter­vals were identified by computer in the 17 holes we processed,and 302 (86 percent) of these intervals were consistent with the

Gamma Resistivity Porosity TestColumn Pattern Column Gamma ResistivityPorosity

-1,\0.

g~

-SO.

G>Q

-80.

FIG. 14. The result of a string-matching exercise in a sedi­mentary sequence is shown as a hard copy of the graphicsterminal screen. On the left are the logs of the test borehole, ageologist-defined geocolurnn, and computer-recognized con­tacts. The correlation lines at the middle of the plot indicatethe stratigraphic units and their boundaries as suggested bythe machine. A number of facies in the pattern column weredeleted. and only two of the three subcycles of transgressiveand regressive deposits were recognized.

interpretation of the geologic core. We achieved a contactdetermination error of less than 2 m. The 14 percent differencebetween human interpreters and machines would be reduced ifthe computer did detailed interpretation, which would requiremore geologic information than we have had and more precisedepth correction of the logs. In some cases it is also possiblethat the geologists should reexamine the classification as­signed to the cores.

CONCLUSION

The interpretation system discussed here is still in prelimi­nary form and the test run is limited. The results describedabove suggest that the design concept and the algorithm areconsistent with the characteristics of the problem. The heuris­tic contact recognition approach and string-to-string matchingalgorithm as a framework of stratigraphy identification workquite well.

Stratigraphy is an essential basis of well-log analysis. Con­ventional evaluations of formation fluid and lithology and es­timation of hydrocarbon grade can be greatly improved inlight of the result of computerized stratigraphic interpretation(Wu and Nyland, 1986).

The technique is a natural tool for stratigraphic correlationbetween boreholes and could also be extended to other geo­logic and geophysical profile correlations.

ACKNOWLEDGMENTS

The work originated with the visit of X. Wu to the Univer­sity of Alberta as a Visiting Scholar from the People's Repub­lic of China. It benefited greatly from discussions with mem­bers of the TEXPERT group at Teknica, Gerry Lobb atSyncrude, John Root of Root Resources, and Dirk Kassenaarof University of Waterloo. The digitized well logs were sup­plied by the Energy Resources Conservation Board of Alberta,and matching core descriptions came from Syncrude.

The research was supported by funds from the Natural Sci­ences and Engineering Research Council of Canada.

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1676 Wu and Nyland

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Wu, X., and Nyland, E., 1986, Well log data interpretation usingArtificial Intelligence technique: Trans., Soc. Prof. Well Log Ana­lysts 27th Ann. Log Sympos., Houston.

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