In this study, we present an application of textural analy-sis to 3D seismic volumes. Specifically, we combine imagetextural analysis with a neural network classification toquantitatively map seismic facies in three-dimensional data.Key advantages of this approach are:

1) it produces a detailed 3D facies classification volume(whereas manual seismic facies classifications are typi-cally 2D maps),

2) it enables rapid and quantitative analysis of the increas-ingly large seismic volumes available to the interpreter,and

3) it eliminates many time-consuming tasks, thereby free-ing the interpreter to focus on determining seismic faciesand integrating them into a geologic framework.

Finally, we extend our textural analysis-based seismicfacies classification technique to interpretation of AVOattribute volumes, such as “A + B” (AVO intercept + gradi-ent), to reduce the inherent nonuniqueness of seismic faciesto geologic and lithologic facies, and simplify the faciesanalysis of complex, mixed-impedance reservoirs.

Seismic facies analysis. Seismic facies analysis is a power-ful qualitative technique used in stratigraphic analysis fromseismic data and in hydrocarbon exploration. Seismic faciesare groups of seismic reflections whose parameters (such asamplitude, continuity, reflection geometry, and frequency)differ from those of adjacent groups. Seismic facies analy-sis involves two key steps—(1) seismic facies classification(i.e., seismic facies are defined, and lateral/vertical extentsdelineated) and (2) interpretation (i.e., analysis of verti-cal/lateral associations, map patterns, and calibration towells) to produce a geologic and depositional interpretation.This interpretation step is required because there is anonunique relationship between seismic data, seismic facies,and depositional environment or rock property relation-ships (Figure 1).

In the past, the seismic facies mapping or classificationstep has occurred through time-consuming, manual meth-ods. Seismic facies are conventionally delineated in the con-text of mapped horizons (i.e., the interpreter analyzes seismicfacies that occur between mapped horizons). This is doneby examining successive vertical sections through the seis-mic volume to determine the dominant seismic facies thatoccurs between the mapped horizons, and posting this infor-mation on a map. The output of this step is therefore a 2Dmap that generalizes the distribution of seismic facies ver-tically within a mapped interval. In large and complex areas,it may be difficult to map different seismic facies consistently.

Manual seismic facies mapping, although time-con-suming and qualitative, has proven extremely useful forhydrocarbon exploration and reservoir characterization,even when seismic facies cannot be uniquely related tophysical properties. A skilled interpreter’s knowledge andexperience contribute greatly to the success of seismic faciesanalysis. However, with increasingly large 3D seismic vol-umes, a more efficient and quantitative, 3D or volume-basedapproach is required but one which still incorporates inter-

preter skill and experience. We believe that volume-basedseismic facies mapping using textural image analysis andan interpreter-trained neural network is an importantadvance toward that goal.

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Interactive seismic facies classification using textural attributesand neural networksBRIAN P. WEST, STEVE R. MAY, JOHN E. EASTWOOD, and CHRISTINE ROSSEN, ExxonMobil Upstream Research Company, Houston, Texas, U.S.

Figure 1. Examples of seismic facies and potential associated geologicfill. A seismic facies can be defined as a stratigraphic region in theseismic data volume that has a characteristic reflection pattern distin-guishable from those of other areas on the basis of reflection amplitude,continuity, geometry, and/or internal configuration of reflectors.Inherent in a seismic facies analysis, however, is the nonunique rela-tionship between seismic data, seismic facies, environment of deposition(EOD), and rock property relationships. Here, typical deepwater seis-mic facies have been interpreted to represent the stacking patternsillustrated beneath each seismic example.

Figure 2. GLCMs for identical subregions with differing azimuthsresults in very different GLCMs. When constructing GLCMs, compar-ison orientation is a significant variable that must be considered.

Textural analysis and grey-level co-occurrence matrices(GLCMs). Textural analysis can quantitatively describe manyaspects of the classic seismic facies analysis performed by theinterpreter. Stratigraphically-steered, seismic texture is a quan-titative, multitrace attribute suite, that mimics the visualprocess of the interpreter more effectively than traditional, sin-gle trace-based attribute analyses. For textural analysis, theinterpreter examines an ensemble of traces as an image to ren-der a classification rather than examining only one or two adja-cent traces at a time. The goal of textural analysis is tomathematically describe the distribution of pixel values withina subregion of data, effectively quantifying the spatial orga-nization of seismic reflections. Just as the physical texture ofa object can be loosely described as relatively smooth or rough,the visual “texture” of a region can be intuitively thought ofas smooth or rough, continuous or discontinuous. The tech-nique commonly used to quantify data texture employs atransformation that results in “grey-level co-occurrence matri-ces” or GLCMs. GLCMs, also referred to as transition proba-bility matrices or “tally” matrices, quantitatively describe thespatial relationships and relative occurrence of pixel valueswithin a defined region.

GLCMs have dimensions N � N; N is the number of graylevels (dynamic range) used to quantify the data. For exam-

ple, 8-bit seismic data have 256 gray levels, and a GLCM con-structed from 8-bit data will have 256 rows and 256 columns.In many instances even less dynamic range is needed for effi-cient GLCM calculation. For example, in many cases 5-bitdata (32 � 32 GLCM) are sufficient for seismic facies analy-sis. GLCMs are constructed by comparing pixel relationshipswithin a subregion for a specific distance (e.g., 4 pixels apart),in a specific direction (e.g., subparallel to a datumned timesurface). Each element of the GLCM represents the relativefrequency of occurrence of two pixels, the “co-occurance,”within the subregion. If pixel A, for example, has value i andis at defined distance D, strike σ, and dip θ, from pixel B withvalue j, then the GLCM matrix location i,j (row, column) willbe incremented by one. If another example within the subre-gion has the same relationship, then that GLCM element isincremented again. This process of “tallying” relative occur-rence is performed for each existing pixel pair within the sub-region to produce the final GLCM for that area. For a sufficientdescription of the data region, several GLCMs, with varyingorientations and “look distances,” are constructed.

Consider a two-dimensional image, taken from a syn-thetic “checkerboard” data volume (Figure 2). Constructinga GLCM, with a distance of two pixels and a diagonal com-parison direction, begins with the comparison between pixel(“A,” 1, 1 - white) and pixel (“B,” 2, 2 - white). Pixel A has avalue of 1 and pixel B also has a value of 1; thus the matrixelement in the first row and first column will be incrementedby one. This process continues until all possible transitions atthis distance and azimuth are considered.

Next, consider a different possible orientation, a distanceof three pixels and a horizontal azimuth. In this case, pixel“A” (1, 1) again has a value of 1 while pixel “B” (1, 4) has avalue of 8. With this transition, the last row and first columnof the GLCM will be incremented by one and the process willagain continue until all possible pixel transitions for thatazimuth and distance have been recorded into the GLCM.

Seismic-based GLCMs. The structure of seismic-basedGLCMs is quite simple to understand. Homogeneous regions,or instances where the GLCM is calculated in direction of max-imum continuity (e.g., parallel to continuous high signal-to-noise reflections) exhibit a tight distribution along the diagonal(top left to bottom right) of the GLCM. This results from com-parison of pixels with similar values, effectively a cross-plotof correlated values. Less homogeneous or discontinuousregions will have more occurrences (counts) farther awayfrom the GLCM diagonal, resulting from disparate pixel com-parisons. Pixel value magnitude is also captured in the GLCM.Regions of low amplitude have GLCMs with values clusterednear the center (effectively the zero crossing). Regions thatinclude higher amplitudes will have more broadly distributedvalues within the GLCM either along the diagonal for con-tinuous textures, or throughout the GLCM in more discon-tinuous textures (Figure 3).

Careful inspection of the GLCMs in Figure 3 demonstratesthat despite the variability in the character, there is always sym-metry about the upper left-lower right diagonal. In the cur-rent study, GLCMs are symmetric matrices about the upperleft-lower right diagonal due to transitions from pixel a to pixelb being considered equally important as a transition from bto a. Further research is exploring the possibility of exploit-ing anisotropic GLCMs in the context of depositional “grains”to extract geometries with geologic significance. Geometriessuch as “onlap,” “mounding” from differential compaction,and “shingles” representing progradation are of particularinterest.

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Figure 3. GLCMs calculated from the extracted seismic data subre-gions for the four different seismic facies of Figure 1. The left GLCMfor each seismic facies class is calculated along the direction of dip at auser-defined distance. The right GLCM is calculated in the direction ofdip at twice that distance to effectively characterize reflection continu-ity. As illustrated in this figure, a number of trends emerge. First,continuous regions exhibit a tighter distribution along the upper left,lower right diagonal than do semicontinuous regions (the high ampli-tude continuous versus high amplitude semicontinuous examples).Second, higher amplitude regions have a greater extent along this samediagonal (the moderate amplitude versus high amplitude semicontinu-ous examples). These characteristics are quantified through texturalattributes and used for the seismic facies classification. Finally, withina specific seismic facies, the GLCM resulting from greater comparisondistance always exhibits a less continuous character (more dispersedalong the lower left, upper right diagonal). This characteristic illus-trates the similarity of a GLCM calculation to the production of seismi-cally defined variograms.

Dip-steering the analysis. Seismic facies must be consid-ered within the structural/stratigraphic framework of adata set and the desired scale for interpretation. Texturalanalysis applied to seismic data must follow the strati-graphic dip of the reflections to produce satisfactory results.Texture analysis and construction of a subregion’s GLCMis extremely sensitive to the “look-direction” or azimuth inwhich the pixels within the region are related. Followingthe stratigraphic dip in a GLCM calculation, dynamicallychanging θ depending on the stratigraphy, maximizes thecontinuity of the image as expressed in the GLCM. Theprocess of guiding a calculation by stratigraphic dip is calleddip-steering.

Dip-steering of a GLCM calculation requires quantita-tive knowledge of the orientation of seismic reflections atall points within an image. Many methods for dip-steeringcalculations are available. “Coherency” or “discontinuity”based dip-steering methods follow stratrigraphic frame-works through calculated trace to trace shifts in the lag dis-tance. Others are based on an initial “skeletonization” of thereflections within a data volume. To further exploit theimage-based nature of textural analysis techniques, we adopta gradient-based dip-steering algorithm. The multitracenature of the technique is thus exploited, and dips withinan image are estimated with high-accuracy. The first step inthis process requires calculation of the horizontal (dx) andvertical (dy) gradient of pixel values within the image.

The local apparent dip of the reflectors is then calculatedvia:

With dy/dx having units of time per CDP; however, forconvenience, these units can be ignored and the dip can beexpressed in terms of “pseudo-radians” or “pseudo-degrees”relative to a horizontal time slice. For fully three-dimensionalanalyses, two orthogonal apparent dip calculations are per-formed and the results used to estimate the true dip andazimuth for dip and directional steering of the GLCM cal-culation.

Once reflection dip and azimuth are known everywhere,this information guides the dip and azimuth of the lookdirection for the GLCM calculation. Due to the discretenature of a seismic image (CDPs and time samples), a con-tinuum of angular values is not feasible. Because the cal-culation is performed over multitraces, the potential angularresolution is inversely proportional to the distance overwhich the comparison is made, and the size of the analysissubvolume. For example, a dip-accuracy of 15 pseudo-degrees, can be approximated if the width is equal to threepixels.

In our application, the interpreter controls the look dis-tance, the size of the analysis subvolume, and the length scalefor dip-steering. The size of the subvolume depends on thescale of the features the user wishes to analyze, and the com-parison distances depend on the length over which continu-ity must be judged. In reservoir characterization, a typicalsubvolume might be 325 m on a side and approximately 50m vertically. Within this subvolume, we generally calculatethree GLCMs. Two GLCMs will be calculated in the directionof maximum seismic continuity (dip-steered) at distances ofapproximately 40 and 80 m. The third GLCM is calculatedorthogonal to the seismically defined dip and can be used asa metric for frequency content and shape of the seismic datawithin the analysis window. Statistical stability considera-tions also play a factor in deciding the comparison distanceswithin the subvolume. Generally, comparison distances aver-age 20% of the overall subvolume dimension to ensure enough

valid comparisons to produce a representative GLCM.

Textural attributes. GLCMs are not efficiently interpreteddirectly; instead summary statistics calculated from the GLCMare exploited. Textural attributes are divided into first- andsecond-order descriptors. First-order statistics quantify theglobal distribution of pixel values within an image and canbe calculated directly using standard statistical techniqueswithout an intermediate GLCM transformation. Within theregion of interest, average absolute amplitude values andstandard deviation of amplitude values can be considered first-order textural attributes and useful in delineating amplitudeanomalies and reflection strength. Derived attributes such asinstantaneous amplitude, phase, and frequency can also beused to produce first-order statistics. Second-order statisticsof an image quantify the spatial relationships of pixels withinthe image and are calculated via the intermediate transformto the GLCM. Second-order GLCM statistics can capture traceshape characteristics, reflection geometry, and reflection con-tinuity, in addition to amplitude strength. Second-order sta-tistics of a subregion are a multitrace attribute, which allowsreflection geometry and continuity to be captured throughanalysis of the dip-steered GLCM.

The utility of various textural attributes for seismic faciesanalysis continues to be a topic of research. A general texturaldescription can be achieved with four commonly used tex-tural attributes: homogeneity, inertia, entropy, and energy.The mathematical expression of these GLCM attributes is:

where cij is the ith, and jth component of GLCM, c, and n isthe size of the matrix (squared number of gray levels withinthe image). The input GLCM, c is normalized such that:

The GLCM characteristics that these mathematical rela-tionships quantify are simple to understand. Textural homo-geneity measures the similarity of pixels. Homogeneity willbe high for GLCMs with elements concentrated near the diag-onal. Conversely, low homogeneity values result from highlycontrasting pixel values in the comparison orientation. Thesecharacteristics make textural homogeneity particularly use-ful for quantifying reflection continuity.

Textural inertia is also indicative of the contrast of the

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Figure 4. Generalized diagram of the workflow for interactive seismicfacies classification with iterative quality control.

GLCM. Whereas homogeneity will be low for a highly con-trasted data, textural inertia will be high. “Inertia” is a mea-sure of the distance-weighted density of points away fromthe GLCM diagonal, similar to the physical property iner-tia. Although textural inertia and homogeneity are related,experiments with dip-steered calculations demonstrate thattogether they provide more seismic facies discriminationthan either alone.

Textural entropy measures the organization of pixels.Entropy is large when the values of the GLCM are uniform,corresponding to a scenario when all transitions are equallyprobable. Textural entropy is low when a particular ele-

ment or set of elements, corresponding to a particular pixelto pixel transition, is favored.

Textural energy (sometimes called uniformity) is alsoindicative of the spatial organization. Energy is lowest whenall elements of the GLCM are equal, nearly opposite of thetextural entropy and very useful for highlighting regions ofreflector continuity and geometry. Although energy andentropy are metrics of similar characteristics, our experiencehas shown that their combined use leads to more satisfac-tory results.

Classification of textural attributes: probabilistic neuralnetworks. Attribute classification is the mechanism by whicha continuum of quantitative seismic characteristics is relatedto a discrete number of classifications within the volume,effectively a “dimension reduction exercise” to simplify theinterpretation of the attributes. Neural networks are oftenused for statistical analysis and data classification. A neuralnetwork is an interconnected assembly of simple process-ing elements. The processing ability of the network is storedin the connection strengths, or weights, obtained by a processof adaptation to, or learning from a set of examples. Oneadvantage of neural networks is the ability to “train” or mod-

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Figure 6. Examples of two seismic facies training polygons from Figure5, representing high-amplitude continuous (HAC) seismic facies (left)and moderate amplitude semicontinuous (MASC) seismic facies(right). Using the digitized examples, gray-level co-occurrence matrices(GLCMs, lower panels) are extracted and, from these GLCMs, texturalattributes are calculated. Note that the data in these figures wererescaled to 5 bit precision (32 gray levels), thereby reducing the overallsize of the GLCM and expediting subsequent calculations. In thisfigure, three GLCMs are produced from the polygon area, two in thedirection of dip at differing distances (upper subimages), and oneorthogonal to the dip direction (lower subimage).

Figure 5. (upper) A typical trainingsession showing the definition of train-ing polygons on a seismic section. Thetraining regions are provided directlyby the interpreter. (lower) Same seismicsection showing local dip of the seis-mic. Yellow represents dips down to theleft; grays represent dips down to theright.

Figure 7. During the training phase, summary characteristics of thedigitized training polygons (denoted by numbers on the chart) arecalculated from the full suite of textural attributes and are plotted toassist the interpreter in training the probabilistic NN.

ify the connection strengths within the network to producedesired results. Computationally, the connectivity of thenodes within a general neural network (the weights), mod-ify an input “vector” of attributes, and pass the modifiedvalues on to the next layer of the network. Once sufficientlytrained on a number of “calibration” images, the neural net-work can then be applied to the remaining images in a datavolume.

Probabilistic neural networks (PNNs) can efficiently per-form pattern classification. Mathematically, these neuralnetworks are similar to kriging, where proximity to knownpoints (attribute values in the training set), guide the clas-sification and prediction of unknown points. Probabilisticneural networks do not require extensive training. In our

analysis, a seismic facies classification can occur in princi-ple with as little as one good example per facies class withthe textural attributes of the training images supplying theweight vectors in the first layer of the network.

When an input pattern is presented to the probabilisticneural network, the first layer computes distances from theinput vector to the training input vectors, and produces avector whose elements indicate how close the input is to atraining input. The second layer sums these contributionsfor each class of inputs to produce, as its net output, a vec-tor of probabilities. A second advantage of the PNN in pat-tern recognition is the ability to extract classificationprobabilities directly from the second, “hidden” layer, inaddition to the classification of the maximum probabilityfrom the output layer. The resultant data quantifies the con-fidence of the classification output based on the input train-ing set. Finally, a “competitive transfer function” (a functionwhich simply selects the maximum value) on the output ofthe second layer picks the maximum of these probabilities,and produces a “1” for the chosen class and a “0” for theother classes.

Interactive training and iterative QC. Figure 4 shows theworkflow for seismic facies classification using texturalanalysis and neural networks. Two extremely importantaspects of the technique are: (1) interactive training of mul-tiple seismic facies classes simultaneously and (2) the capa-bility to do iterative training and quality control checkingof results between the seismic interpreter and the neural net-work classification prior to full data analysis. This iterativeapproach for multiple, user-defined classes enhances theability of a probabilistic neural network to reproduce thegeologically significant classifications.

Training the neural network begins with the definitionof textural analysis parameters, such as calculation volumesize, dip-steering window size, bit depth of the data, andanalysis distance. Specification of these parameters is basedon the experience of the interpreter, and actual parametervalues are dependent on the frequency and spacing of thedata, as well as the scale of the analysis. To train the neuralnetwork, the interpreter selects one or several key seismiclines, and digitizes polygons of a consistent scale that areexamples of different seismic facies (Figure 5, upper). Thecomputer automatically calculates “on-the-fly” the data anddip/azimuth information associated with the training poly-gon (Figure 5, lower) and performs a textural analysis onthese subregions (Figure 6). The interpreter inspects theresulting GLCMs and can either reject the training polygonor accept it and classify it as a specific seismic facies. The

1046 THE LEADING EDGE OCTOBER 2002

Figure 8. A typicalseismic facies classi-fication using theinterpreter trainedprobabilistic neuralnetwork, wheremultiple seismicfacies classes havebeen identified. Theseismic classifica-tion scheme on theright consists ofhigh amplitude(HA), moderateamplitude (MA),low amplitude (LA),continuous (C) andsemicontinuous(SC) seismic facies.

Figure 9. A relative confidence section resulting from the extraction ofprobabilities from the neural network output. Low confidence values canbe observed in and near fault zones and in areas of facies transition.

Figure 10. Result of the textural analysis using neural networks is aseismic classification volume (upper left). This volume can be examined invertical section or horizon slice mode, and compared to other seismicattribute volumes, in addition to available well or core data, in order todevelop a geologic interpretation of the seismic facies classification. In thisexample from a channelized deepwater reservoir, a slice from the seismicfacies classification volume (middle image) is compared to an equivalentslice from the seismic discontinuity volume (lower right). Calibration ofthese features to available well data results in the environment of deposi-tion interpretation map shown in Figure 11.

interpreter should specify several examples of each seismicfacies class, and these examples should be distributedthroughout the 3D seismic area of interest. In the initialtraining phase, plots of summary attributes, such as ampli-tude and reflection continuity, assist the interpreter in pro-viding consistent training examples to the neural network(Figure 7).

Once initial training is complete, a probabilistic neuralnetwork is constructed from the textural attributes and theirassociated classifications. This initial neural network is thenused to classify a portion of the data set (typically severalkey seismic lines) to facilitate interactive quality control andanalysis. Determining whether the PNN has been trainedadequately occurs in the quality control phase of the work-flow. In this step, the interpreter asks “is the computer doingwhat I would have otherwise done manually and is it geo-logically reasonable?” The interpreter can judge the per-formance of the result supplied by the neural network simplyby examining representative classification and confidenceprofiles (Figures 8 and 9) prior to classification of larger datasets. If results are unsatisfactory, the training set can be

modified through deletion of existing polygons and/oraddition of new polygons. The neural network is then recre-ated with the modified training set, and again checked, untilthe interpreter is satisfied with the results.

After training and QC (i.e., the interpreter is satisfied thatthe algorithms are producing the facies classification whichwould have been arrived at through manual interpretation),the network and textural analysis algorithms can be appliedto the rest of the data volume. The result is a seismic faciesclassification volume which has a seismic facies classifica-tion for every trace and sample within the volume (Figure10) and an associated relative confidence volume. Keyadvantages of the neural net classification volume over amanual classification are: (1) the neural network classifica-tion is volume-based (rather than map-based for manualclassifications), (2) the neural network classification is typ-ically much more detailed than any manual classificationand can be produced in a fraction of the time, (3) the clas-sification is based on quantitative criteria and is reproduciblegiven a particular training set. These characteristics promoteimproved efficiency in the analysis of large data volumes,

OCTOBER 2002 THE LEADING EDGE 1047

Figure 11. Environment of deposition andnet:gross distribution map for slices shownin Figure 10. This discontinuity slice high-lights the lateral edges of the channel (redlines), a broad, older sinuous element (1)and a narrower, younger sinuous element(2). Comparison with the seismic facies sliceshows that sinuous element (1) is composedof HAC to MASC seismic facies, whereassinuous element 2 is primarily composed ofHAC seismic facies.Because the texturalanalysis seismic facies classification is avolume, this type of analysis can be appliedat numerous stratigraphic levels within aninterval of interest, whether or not theseintervals are bounded by mapped horizons.Combining these results with the conceptualrelationships illustrated in Figure 1, the netresult of the analysis is effectively an "envi-ronment of deposition volume" where 3Dregions can delineate differing depositionaland geologic properties.

Figure 12. Example of seismic data formixed impedance sands for near, far, full,and A+B volumes.

and improved capture of lateral and vertical heterogeneitiesin the seismic volume that are due to stratigraphic or otherfactors.

Although seismic facies classification using texturalanalysis and neural networks techniques greatly improvesthe efficiency of seismic classification, the output classifi-cation volume is not the final product. This involves trans-lation of the amplitude and geometric characteristics of thedifferent seismic facies classes into a geologic, depositionalenvironment, or rock property model. This stage of analy-sis still requires strong interpreter input, and typicallyinvolves integration of numerous techniques including:analysis of vertical and lateral seismic facies associations onvertical sections through the classification volume, deter-mination of map patterns on horizontal and stratigraphicsections through the volume, comparison to the input seis-mic and other seismic attribute volumes, and finally, cali-bration to available well-log and core data (Figure 11).

At the final integration stage, the improved efficiencyand quality of the seismic facies classification volume pro-duced using textural analysis and neural networks tech-niques translates into more time for the interpreter to focuson interpretation of the classification volume. This enablesthe interpreter to produce a more robust geologic analysis.

Seismic facies interpretation applied to AVO attribute vol-umes. Mixed impedance reservoirs, typical of some deep-water depositional environments, are composed of sands andshales, which are not uniquely defined with only a near-off-set volume or a full stack volume. In these environments, anintegrated multidisciplinary workflow is especially impor-tant in delineating the seismic-scale distribution of sand andshale for reservoir characterization and input to volume-based geologic models. Seismic interpretation, includingseismic facies analysis, remains a cornerstone of the work-flow. However, with increasing reservoir complexity, relianceon sophisticated multitrace and multivolume (includingAVO) attributes is necessary to capture this geologic com-plexity.

We have extended the method for seismic facies analy-sis to these amplitude-versus-offset attribute volumes todemonstrate how the inherent subjectivity of a facies analy-sis, especially in mixed impedance deepwater environments,can be reduced with reliable prestack seismic data. In par-ticular, the AVO properties of the interval, once fluid con-tacts have been identified, can be fundamental in theseismic-scale placement of sand and shale packages.Deterministic sand placement within facies-based net-to-gross packages can significantly improve the accuracy of avolume-based reservoir characterization workflow.

Among the advantages of using a volume AVO attributeis the reduction in nonuniqueness in the seismic data. Forexample, using an “A + B” AVO attribute cube, high-ampli-tude seismic facies can be directly interpreted as likely HC-charged sands” whereas low-amplitude facies can beinterpreted as shale-prone and/or water-wet. Simplifiedwell-to-facies ties are also a result of this type of application.

We illustrate the predictive power of an AVO attributefacies analysis with an example from a stratigraphically com-plex, mixed-impedance clastic reservoir. In a mixed imped-ance scenario, the seismic expression of sand-prone intervals,in terms of amplitude and reflection continuity, will vary withAVO class of the sands and with the stack of data examined(Figure 12). Laterally amalgamated sands may also havevarying responses depending on the stack of data examined(Figure 12). For example, a well-sorted, fine-grain class IIIsand may be associated with a low-amplitude-continuous

reflection (LAC) on the near offset volume and a high-ampli-tude-continuous (HAC) on the far offset volume. Conversely,a poorly sorted, coarse-grained class I sand may be high-amplitude continuous on the near offsets and dim to a low-or moderate-amplitude, continuous package on the far-off-set volume. The full stack data would be some average ofthe two end-members, complicating the interpretation.

In a properly constructed AVO attribute volume, lateraltransitions between classes I, II, and III can be mitigated. Usingan AVO attribute volume as the basis for seismic facies clas-sification can thus minimize the complexity of interpretingmultiple stacks of data to identify sand-prone intervals. Forthe location described above, the A+ B volume contains a sin-gle high-amplitude continuous reflection denoting a singlesand body regardless of AVO class (Figure 12, lower). Theinclusion of the AVO classification information can be exploitedto further discriminate high-impedance sands from low-impedance sands for rock property assignment.

Potential pitfalls of quantitative facies analyses. As withany quantitative analysis, data quality weighs heavily into theconfidence of the interpretation. In cases where data qualityis suspect, the potential subjectivity of a traditional, manualseismic facies analysis can be an asset when interpreters areaware of the limitations of their data. For example, where dataare known to be nonstationary across a reservoir interval,manual seismic interpretation can compensate for this vari-ability. Automated seismic facies methods, however, may notnecessarily take this data variability into account unless spa-tially variant training is employed.

Further, the use of computer generated facies data with-out appropriate geologic insight and interpreter understand-ing of the technology is dangerous. Such application oftechnology can give automated methods a “black-box” rep-utation and lead to a misunderstanding of the technology beingapplied, its expected benefits, and its limitations. Fundamentalknowledge of the geology and geophysics of an explorationor development target, as well as an appreciation of the tech-nology applied, is critical to the successful application of anytechnique in an integrated workflow.

Conclusions. This article demonstrated:

1) The combination of seismic textural analysis and neuralnetwork techniques can be successfully applied to auto-mate the seismic classification step of traditional seismicfacies analysis, with resulting improvements in the effi-ciency, degree of detail, and reproducibility of the seis-mic facies classification product. Time-efficiencies andquality improvements result in more time for the inter-preter to interpret the classification results, and to trans-late it into a robust geologic framework.

2) Two- and three-dimensional textural analysis provides asignificantly different approach to seismic facies classifi-cation over single, trace-based seismic attributes in thatseismic texture is a multitrace, volume-based attribute thatprovides a quantitative measure of the reflection ampli-tude, continuity, and internal configuration of reflectors.Seismic textural analysis thus provides a means to quan-tify the description of elements contained in seismic faciesanalysis.

3) The use of neural networks to classify textural attributesprovides an efficient method by which the seismic inter-preter can interactively “teach” the computer the seismiccharacteristics of interest. Probabilistic neural networksalso offer the advantage of providing a quantitative mea-sure of the confidence placed in each facies classification,

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thereby increasing the efficiency of iterative training. Theconfidence value can also be used to dynamically reclas-sify until a specified confidence value is achieved.

4) Seismic facies analysis of reliable AVO attribute volumescan significantly reduce the uncertainty and nonunique-ness of lithologic interpretations based on seismic faciesinterpretation. With appropriate data, an AVO faciesanalysis may also be used to position sands in a clasticreservoir model.

Suggested reading. “Segmentation of the Mid-Atlantic Ridgesouth of the Azores, based on acoustic classification of TOBIdata” by Blondel (in Tectonic, Magmatic, Hydrothermal andBiological Segmentation of Mid-Ocean Ridges, Geological SocietySpecial Publication 118, 1996). “Interactive AVO time-align-ment and neural network classification” by Eastwood and West(SEG 2001 Expanded Abstracts). An Introduction to Neural Networksby Gurney (UCL Press Limited, London, 1997). “Statistical andstructural approaches to texture” by Haralick (Proceedings of theIEEE, 1979). “Three-dimensional texture attributes for seismicdata analysis” by Randen et al. (SEG 2000 Expanded Abstracts).“Seismic stratigraphy and global changes in sea level, Part 6:Stratigraphic interpretation of seismic reflection patterns indepositional sequence” by Mitchum (in AAPG Memoir 26, 1977).“Digital imaging processing techniques for enhancement andclassification of SeaMarc II side-scan sonar imagery” by Reedand Hussong (Journal of Geophysical Research , 1989).“Probabilistic neural networks” by Specht (Neural Networks,1990). “3D seismic texture classification” by Vinther et al. (SPE35482, 1996). TLE

Acknowledgments: We thank the numerous ExxonMobil geoscientists whohave contributed to the development and effective use of these techniques:in particular, J. Ardill, C. Dawson, D. Gao, L. Foreman, M. Porter, R.Hill, R. Stephens, and L. Magennis. We also thank ExxonMobil’s affili-ates and partners for permission to publish this work.

Corresponding author: brian.p.west@exxonmobil.com

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