Copyright 2001, Society of Petroleum Engineers Inc.

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AbstractUsually, seismic data is used in a qualitative approach todetect changes in the waveform and to pick acoustic continuityof a peak and/or a through as a structural mapping tool. Theseismic interpretation is a qualitative process for building ageological model. Today, many works try to use the seismicinformation in a quantitative approach. Seismic interpretationin a quantitative approach is a key process in the integration ofgeoscience data at scales from basin-wide studies, reservoirfocused and field-development process. Quantitative modelingcould be deterministic and/or probabilistic. We use, in manysteps of a seismic processing sequence, examples ofquantitative deterministic modeling like seismic migration,some seismic inversion methodology, etc. Probabilisticmodeling can be gathered in two groups: multivariate statisticsand geostatistics approaches. Close to probabilistic modeling,we have also the neural network method. In this paper, wefocus on the application of neural network modeling forseismic pattern recognition (seismic facies analysis) applied anultra-deepwater turbidite oilfield reservoir in Campos Basin,offshore Brazil.

IntroductionA 3-D reservoir architecture characterization requires the

integration of different data types to define a more detailedand realistic geological interpretation. Well logs and core dataprovided detailed information about the vertical variation ofmany reservoirs properties but they are restricted to regionsadjacent to the borehole. 3-D seismic data play an importantrole in describing external and internal complexities ofreservoirs away from the wellbore and to define geometricdescription of structural and stratigraphic aspects of the

reservoirs (ref. 1). Seismic amplitude variations are linked tochanges in acoustic impedances that we can be trying to relateto reservoir properties. This paper demonstrates amethodology for seismic pattern recognition in a target-oriented approach to aid the reservoir architecturecharacterization in a more detailed and accurate 3-D seismicinterpretation.

Seismic pattern recognition techniques are used todistinguish important geological features from seismicinformation. The methods of seismic pattern recognition canprovide solution to practical problems in reservoircharacterization in terms of automatic mapping of mainfeatures of seismic morphology related to geologicalenvironment. The automatic interpretation of subsurfacegeology from seismic data is possible by analyzing of thenature of waveform cycles and their termination with respectto adjacent reflections. The geometry and the terminations ofwaveform styles help to locate boundaries between zonescorresponding to different types of depositional units eachassociated with characteristics of seismic morphology understudy.

In this paper a target-oriented automatic patternrecognition methodology is applied to 3-D seismic data set aseismic stratigraphic unit of an ultra-deepwater turbiditesandstones reservoir. The pattern recognition method isapplied in two approaches: unsupervised and supervised. Theunsupervised approach to exploit the statistically commoncharacteristic underlying seismic traces segments at theseismic stratigraphic unit. The supervised approach uses thestratigraphic knowledge to guide the pattern recognition.

The seismic pattern recognition methodology used iscarried out in six main steps: (1) spatial and temporalsampling, (2) attributes selection; (3) definition of the numberof classes and iteration; (4) training and classification with acompetitive learning algorithm (unsupervised approach) and(5) training and classification with a back-propagationalgorithm (supervised approach) and (6) interpretation ofseismic facies.

Work on artificial neural network has been motivated fromthe results obtained in terms of useful computation of learningprocess of seismic waveform. To achieve good performance,neural network employ a massive interconnection of simplecomputing cells referred to as neurons or processing units. The

SPE 69483

Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfieldin Campos Basin, Offshore BrazilPaulo Johann, Dayse D. de Castro and Alberto S. Barroso, Petrobras S. A.

2 JOHANN, P.R.S.; CASTRO, D. AND BARROSO, A. SPE 69483

definition of neural network is a massively parallel distributedprocessor that has a natural propensity for storingexperimental knowledge and making it available for use (ref.2).

Geology data set descriptionThe ultra-deepwater turbidite oilfield analyzed in this

study was discovered in October 1996, located 125 miles fromthe coast in the northeast portion of Campos basin, offshoreBrazil, in water considered to be ultra-deep (between 1,500-2,000m) (Fig. 1).

Campos basin is located in the southeastern coast of Braziland it covers an area of about 100,000 km2 from the coast tothe 3,400m isobath. The Vitória high separates it from EspíritoSanto basin to the north and from Santos basin by the CaboFrio high to the south.

This field is a large oilfield with a complex hydrocarbondistribution (OOIP around 2.0 billions m3 with API from 18.6ºto 31.5º). The external geometry of the field is defined to thenorth and east by dipping and to the south and west directionsby stratigraphic pinchout. The oil entrapment is composed bystructural and stratigraphic framework.

Two main factors controlled deep and ultra-deepwatersedimentation in Campos basin: thermal subsidence patternwhich control turbidite sedimentation to certain preferableareas and salt movement which allowed stacking ofsandstones in depositional lows. Unconformities due to sea-level variation and submarine paleocanyons are the additionalfactors that control reservoir distribution (ref. 3).

Hydrocarbon accumulation are Miocene to Maastrichtianages. The lithology is a turbidite sandstones deposited in awidespread structure depression. The halokinesis processcontrolled the structural framework. The stratigraphic zonationfrom well logs measurements divided the reservoir in threemain sequences, with internal sub-division (ref. 4). Five mainzones can be characterized with average thickness of 30m byzone. Total oil net sand in the field is very thick, with averagearound 160m.

In this paper we focus in the Maastrichtian 1 reservoirzone. This reservoir was used in the beginning of thedelimitation of the field to build the initial structural map andto estimate from seismic data the reservoir distribution (ref. 5).

Development strategy overviewThe decision by Petrobras to develop the field immediately

after its discovery was based upon the success of the drillingof exploration wells (ref. 3). The improved of 3-D seismicdata and the technological training program that has givenPetrobras the capability to produce oil and gas in ultra-deepwater in Campos basin.

The Pilot System start on January 1999 and the fieldbecame the holder of world record for oil production in ultra-deepwater (1,853m water depth). The objective of PilotSystem was to collect fundamental information about thereservoir and test new technologies to be applied in theproduction system. Also this system has furnished information

that allow optimization of the subsequent field exploitationstages, thus helping reduce technical, economic andenvironmental risks during these phases, when large volumesof oil and gas are being produced.

The Phase1 of permanent production system start on April2000 and produce the North and Southeast portions of thereservoir. Between the first 9 wells drilled in this phase ofproduction, that confirmed the oil with excellent quality 31.5ºAPI, Petrobras had the world record for oil production in ultra-deepwater (1,877m, Fig. 1).

Principles of methodology - seismic facies analysisThe pattern recognition methodology is carried out in six mainsteps (ref. 7): (1) spatial and temporal sampling, (2) attributesselection; (3) definition of the number of classes anditerations; (4) training and classification with a competitivelearning algorithm (unsupervised approach) and (5) trainingand classification with a back-propagation algorithm(supervised approach) and (6) interpretation of seismic facies.

Spatial and temporal sampling of the data set. The 3-Dseismic volume available over the reservoir has 414 km2. Thecell dimension is 13,6m by 26,6m, with a spatial density of100.000 traces/km2. The record length is from 0 to 6 seconds,with 2ms of sample rate. The seismic data used in the patternrecognition is migrated pre-stack in time. The first step in themethodology is the choice of the representative sampling ofthe seismic data available over the reservoir. The reservoirexternal geometry is the first point to take in account to definethe area of analyzes. In our study the external geometry of theMaastrichtian 1 reservoir was the guide to define the polygonof study. Figure 2 shows the spatial volumetric distribution ofMaastrichtian 1 reservoir in terms of seismic amplitude pre-stack migrated in time. The focus in the area of the reservoirdistribution reduces to 130 km2 (367.460 seismic traces). Thedata to apply the pattern recognition algorithm. The temporalsampling was a sub-volume cut from 10ms above and 30msbelow the Maastrichtian 1 reservoir top, respectively. Thewindow of 40ms used in the pattern recognition also reducesthe input data from 3000 samples/trace in the raw data to 20samples/traces under study (7.349.200 seismic samples). Theseismic horizon representative of Maastrichtian 1 reservoirwas carefully picking before the definition of the temporalwindow for the pattern recognition. Figure 3 shows thetemporal window over a representative seismic line used forpattern recognition algorithm.

Attributes selection. Six volumetric seismic attributes wereselected for carried out the seismic facies analysis. Allattributes were calculated over the volume inside the windowaround the Maastrichtian 1 reservoir. The attributes analyzedwere integrated seismic amplitude, integrated instantaneousfrequency, integrated reflection strength, integrated cosine ofphase, integrated apparent seismic polarity, and integratedseismic magnitude and rms amplitude. In this step somestatistical analysis was carried out, matrix of correlation

RESERVOIR GEOPHYSICS: SEISMIC PATTERN RECOGNITION APPLIED TO ULTRADEEPWATERSPE 69483 OILFIELD IN CAMPOS BASIN, OFFSHORE BRAZIL 3

between the attributes analyzed, to define the attributes mostrelevant for the discrimination of waveform characteristics.This quality control is very important to apply before the usedof cluster and classification algorithms. In this study theintegrated seismic magnitude was remove because thecoefficient of correlation with rms amplitude is 0.9. Figures 4ato 4c shows four seismic attributes map used as input data tothe pattern recognition algorithms. In this step we define theentire data set in the attribute space. In the case of theMaastrichtian 1 reservoir were 7.349.200 seismic samples ofsix seismic attributes, 6-dimensional vector space.

Definition of the number of classes and iteration number.Before the definition of the number of classes someconsideration are necessary to understand the target of thestudy. In the unsupervised approach we can used more classesthan the number of geological facies expected in the area ofthe study. Some seismic waveforms are not related togeological information, like multiples, diffractions or anotherseismic artifacts from acquisition and/or seismic processing.Otherwise, in the supervised approach will be interesting tokeep a number of classes related to the geological facies in thearea under study. In the case of the Maastrichtian 1 reservoirstudied in this project the number the classes analyzed were 2,3, 4 and 5. The number of iterations was from 100 to 1000.We used a standard workstation to run the neural networkalgorithm. The time to run 1000 iterations is less the fiveminutes.

Unsupervised approach for seismic facies analysis. Theunsupervised approach for seismic facies analysis used in thisstudy base their classification on the clustering of the entiredata set. The results do not depend on geological and/or welllog information. The seismic waveform will be clusters interms of its statistical characteristics. The unsupervisedapproach is appropriate where no reliable geologicalinformation is available, like in an exploratory context; or inorder to verifies the existence of clusters and their respectiveseparation in an un-biased cluster analysis. The unsupervisedapproach used a neural network competitive learningalgorithm. Like the non-parametric statistical methods thecompetitive learning algorithms do make any assumptionsabout the statistical distribution of the data set. In thebeginning of the classification the user specifies the number ofclasses. During training, attribute vectors are iterativelypresented and the cluster vector that is closest to the attributevector is updated so that it is even more likely to win the nexttime the particular attribute vector is presented. Training isstopped after a certain number of iterations, or when thecluster vector only changes marginally. Three parameterscontrol the algorithm: (1) number of iterations; (2) learningrate and (3) conscience. The numbers of iterations control howmany times the input vectors are iterated through in order tofind that stable cluster vectors. The learning rate adjusts howfast the competitive network is allowed to adjust its weights inorder to map the input vectors into pre-determined number of

classes. The conscience parameter reduces greediness from thealgorithm. Conscience mechanism ensures that output classesthat are winning a lot, get a “bad consciousness” andtemporarily withdraw from the competition (ref. 7).

Competitive Learning Method. Figure 5 shows theschematic neural network with input and output neurons(cells) that represents in this study samples of seismic traces.In the competitive learning method the output neurons of aneural network compete among themselves for being the oneto be active (or fired). Thus, in the case of competitivelearning only a single output neuron is active at any one time.It is this feature that makes competitive learning highly suitedto discover those statistically salient features that may be usedto classify a set of input patterns, like seismic waveform in ourstudy. There are three basic elements to a competitive learningrule (ref. 6):

- A set of neurons that are all the same except for somerandomly distributed synaptic weights and which thereforeresponse differently to a given set of input patterns.

- A limit imposed on the “strength” of each neuron.- A mechanism that permits the neurons to compete for right

to respond to a given subset of input, such that only oneoutput neuron, or only one neuron per group, is active at atime. The neuron that wins the competition is called awinner-takes-all-neuron.The individual neuron of the network learns to specialize

on sets of similar patterns, and thereby become featuredetectors. In the simplest form of competitive learning, theneural network has a single layer of output neurons, each ofwhich is fully connected to the input nodes. The network mayinclude lateral connections among neurons (Fig. 5). Toillustrate the essence of competitive learning, we used thegeometry analogy (Fig. 6, ref. 6). It is assumed that each inputpattern x has some constant length, so that we may view it as apoint on an N-dimensional unit sphere, where N is the numberof input nodes; N also represents the dimension of eachsynaptic weight vector. It is further assumed that all neurons inthe network are constrained to have the same Euclidean length(norm). Thus, when the synaptic weights are properly scaled,they form a set of vectors that fall on same N-dimensional unitsphere. Figure 6a shows three natural groupings (clusters) ofthe stimulus pattern represented by dots; this figure alsoincludes a possible initial state of the network (represented bycrosses) that may exist before learning. Figure 6b shows atypical final state of the network that results from that use ofcompetitive learning (refs. 2 and 6).

Supervised approach for seismic facies analysis. In thegeoscience research geologist and geophysicist have a lot of apriori information about the data set under study, particularlyif we have well logs drilled in the field. Normally, in thereservoir studies we have a number of well log informationsavailable when we make seismic pattern recognition study.Thus, detailed and reliable information is available at well logposition. This information is too important not be considered

4 JOHANN, P.R.S.; CASTRO, D. AND BARROSO, A. SPE 69483

in a seismic classification process. The classes defined withauxiliary data like geological knowledge are given by aninterpretative analysis of entire system rather than a statisticaldefinition. Usually, the a priori information is fed into thesystem as training data set, i.e. attribute vectors, that have aknow class relationship. It is evident that a substantial numberof attribute vectors have to be made available for each class, inorder to get a proper statistic description of that datadistribution. Under the assumption that sufficient data areavailable for each class, a subset can be taken away from thetraining data and be reserved as validation data. A successfulclassification will predict the correct class at the validationdata locations. The method of supervised seismic facies used aback error propagation algorithm (ref. 7).

Error Back-Propagation Method. The back-error-propagation algorithm is the most popular and widely usedlearning method in neuron-computing today. It works bydoing iterative gradient descend in weight space, minimizingthe square error between the desired and actual outputs. Back-propagation type networks work by constructing hyperplanes,thus dividing the N-dimensional hyperspace into decisionregions. The advantage of the error back-propagationalgorithm: can resolve non-linear attribute or class relationsand no assumptions about data distribution. The network istrained using the back-propagation technique, in which theactual output is compared with the desired output and errorsare propagated backwards through the network in order toupdate the weights.The perceptron is the simplest form of a neural network (Fig.7a) used for the classification of a special type of patterns saidto be linearly separable (i.e., patterns that lie on opposite sidesof hyperplane, ref. 2). The algorithm used to adjust the freeparameters of these neural network firsts appeared in alearning procedure developed by Rosenblat (1958, 1962) forhis perceptron brain model. The multilayer perceptronnetwork consist of a set of sensory units (source nodes) thatconstitute the input layer, one or more hidden layers ofcomputation nodes, and an output layer of computation nodes.The input signal propagates through the network in a forwarddirection, on a layer-by-layer basis. These neural networks arecommonly referred to as multilayer perceptron (Fig. 7b).

Multilayer perceptrons have been applied successfully tosolve some difficult and diverse problem by training them in asupervised manner with a highly popular algorithm known asthe error back-propagation algorithm. This algorithm is basedon the error-correction learning rule.

The error back-propagation algorithm consists of twopasses through the different layers of the network: a forwardpass and a backward pass. In the forward pass, an activitypattern (input vector) is applied to the sensory nodes of thenetwork, and its effect propagates through the network, layerby layer. Finally, a set of outputs is produced as the actualresponse of the network. During the forward pass the synapticweights of the network are all fixed. During the backwardpass, the synaptic weights are all adjusted in accordance with

the error-correction rule. The actual response of the network issubtracted from a target response to produce an error signal.This error is the propagated backward through the network,against the direction of synaptic connections – “error back-propagation” (ref. 2).

The training of the perceptron to distinguish between thedifferent classes results in a set of node weights, which definethe trained network. The network topology must be decided atthe training stage. The input layer will have one node for eachattribute in the vector, or if more than one vector is used at atime, in a contextual approach, one node for each attribute ofeach vector. The number of hidden layers will depend uponthe complexity of surface decision required.

The training of the network can be tested by the used of averification data set, the validation data set, which is knownbut excluded from the training data. The error rateclassification using the neural network can then be found foreach class. If the error rate is too high the network must beretrained, using different learning parameters or even adifferent network topology, or some new attribute set selected.

The are four parameters to control the error-back-propagation algorithm (ref. 7): (1) learning rate, (2)momentum, (3) epochs and (4) error limit. The learning ratetells network how much its weights can be adjusted at eachlearning step. Momentum is used to increase learning speed.Epochs control how the network processes many times thewhole set of input vectors. The error limit controls when tostop training. The value signifies the total rms error in thenetwork output.

Application of seismic facies analysisThe Maastrichtian 1 reservoir of the ultra-deepwater

turbidite in Campos basin focused in this study was divided inthree main areas (Fig. 8): North, Southeast and West portions.The seismic pattern recognition algorithms was applied first,in the entire data set and second, in each portions of the mainoccurrence of the reservoir.

Unsupervised approach for seismic facies analysis. In theunsupervised approach we seek to estimate the statisticalgroups underlying all seismic traces segments at seismicstratigraphic unit. Figures 9a to 9d shows the results of theapplication of the unsupervised seismic facies algorithm,competitive learning, over the North portion, entire data set,Southeast portion and West portion of the Maastrichtian 1reservoir, respectively. After tested from 2 to 5 classes withdifferent number and combination of seismic attributes wedecide to keep 4 classes for each portion of the reservoir. Forthe entire data set with strong presence of diffractions andnoise in the faults region we decide to keep 2 classes asrepresentative of Maastrichtian 1 reservoir in the unsupervisedapproach. Typically, we used from 100 to 1000 iterations,with good results with 1000 iterations. The interest in theresults of unsupervised approach is the very well correlationbetween the classes and the geological facies interpretation offield. The red class is associated with turbidite sandstones

RESERVOIR GEOPHYSICS: SEISMIC PATTERN RECOGNITION APPLIED TO ULTRADEEPWATERSPE 69483 OILFIELD IN CAMPOS BASIN, OFFSHORE BRAZIL 5

amalgamated lobes. In the West portion is associated with thegas sandstones drilled in the middle of this portion or thicksandstones lobes. The orange class is interpreted as distalsandstones lobes already drilled in the North portion of thefield. The another two classes can be interpreted as distal lobeswith reducing thickness with interference of some seismicartifacts not directly related with geological information of thereservoir.

Supervised approach for seismic facies analysis. In thesupervised approach wells were regrouped in four classes atseismic stratigraphic unit (Maastrichtian 1 reservoir). In thisapproach the seismic traces segments nearest the wells wasused for built the learning data set. In the North portion of thereservoir the figures 11a to 11d shows the four seismic linescrossing the wells used as reference for the learning data set.Type I is characterized by strong through amplitude at the topand strong peak at the bottom of reservoir. The well 2represent this class. The geology information from well 2, inthe Maastrichtian 1 reservoir, is interpreted as a deposition ofturbidites amalgamated lobes. Type II is characterized byamplitude anomaly at top of reservoir less important than thetype I, particularly, in the bottom (peak) of the reservoir. Thewell 5 represent this class. The geological interpretation of thewell log information allows interpreting the sandstonesdeposited in a distal portion of sandstones lobes. Type III ischaracterized by a weak amplitude anomaly. The well 18represent this class. The geological information from the welllogs allow interpret this class close to the type III, distalportion of turbidites lobes, but with an important reducing ofreservoir thickness. Type IV is characterized by a seismicmorphology relatively without anomalies. It is represents thereservoir limit or absence. For the Southeast and the Westportions of the reservoir this seismic morphology can be used,but in the West portion the seismic anomaly is also associatedwith the gas sandstones (red facies in the middle of thatregion). In the Southeast portion the most important variationcaptured by seismic pattern recognition is related to thicknessvariation in the reservoir distribution.

The Figures 10a to 10d shows the results of the applicationof the supervised seismic facies algorithm over the Northportion, entire data set, Southeast portion and West portion ofthe Maastrichtian 1 reservoir, respectively.

Comparison of unsupervised and supervised approachesThe unsupervised and supervised 2 facies maps and 4 faciesmaps shows a good correlation in both approaches (Figs. 13and 14). Consequently, the seismic morphology representsstatistically very well the geological knowledgement of thefield with the well log information available. In the Northportion of the field the red facies (Type I) represent turbiditesandstones deposited in amalgamated lobes. In the Westportion represent gas sandstones. In the Southeast portion redfacies represent an increasing of sandstones thickness. Someparts of the facies map with the red facies in the West portionrepresent also turbidite sandstones with oil.

In the supervised approach the red facies (Type I) wasbetter defined, the sandstones lobes are have a more morerealistic distribution. Yellow facies (Type II) representturbidite sandstones deposited in the distal part of the lobes. Inthe supervised approach the distribution of this facies is moreconnectected between the left and right side. In terms ofreservoir volume this facies is more present in the supervisedmap. Green facies (Type III) represent the distal part of lobeswith a reduced thickness. The volume of this facies is morepresent in the unsupervised map. Blue facies (Type IV)represent reservoir limit or absence (Fig 14).

ConclusionThe successful application of the neural network

methodology for 3-D seismic reservoir characterization usingseismic pattern recognition facies analysis in the ultra-deepwater oilfield makes it an interesting approach for otherturbidites fields in Campos Basin, offshore Brazil.

The facies maps are automatic and very fast to apply hugereservoirs. These maps help the seismic interpret to define thereservoir distribution in the seismic scale and to aid theintegration of seismic information with the geologicalknowledge of the field. The results can be used for understandthe seismic stratigraphy and sequence stratigraphy on theturbidites systems studied.

The perspective will be the application of the seismicfacies analysis in the another four stratigraphic sequence of thereservoir and to compare the neural network used in this studywith another algorithm available in the petroleum industry.

AcknowledgmentsWe thank Petrobras for permission to publish this paper.

Special thanks to the geologist Carlos Varela Stank and DarciJosé Sarzenski for the support in the geological discussionabout the reservoir studied.

References1. Johann, P. et al.: 3-D, 1996, “Reservoir Characterization by

Stratigraphic Inversion and Pattern Recognition”, SEG AnnualTechnical Conference and Exhibition, Denver, November 11-16,Expand Abstracts.

2. Haykn, S., 1994, “Neural Networks- A ComprehensiveFoundation”, Prentice Hall.

3. Rangel, H.D. et all., 1998, “Roncador Field. A New Giant inCampos Basin, Brazil”, OTC, Houston: 579-587.

4. Barroso, A. et al., 2000, “Roncador Giant Oilfield: Exploration andProduction from a heterogeneous Maastrichtian turbiditereservoir in ultra-deepwater Campos basin, Brazil”, AAPG.

5. Santos, P.R. et all., 1999, “Geophysical and Log Characterizationof Roncador Field, Campos Basin, Brazil” SBGf Rio’99.

6. Rumelhart, D. E. and Ziper, D., 1985, “Feature discovery bycompetitive learning”. Cognitive Science 9, 75-112.

7. Geoframe – SeisClass, 2000, Technical documentation.8. Rosenblat, F. (1958), “The perceptron: A probabilistic model for

information storage and organization in the brain.”PsychologicalReview 65, 386-408.

9. Rosemblat, F. (1962), “Principles of Neurodynamics.”Washington,DC: Spartan Books.

6 JOHANN, P.R.S.; CASTRO, D. AND BARROSO, A. SPE 69483

Fig. 1a – Localization map for the oil fields from Campos Basin; b) 3-D seismic volume with the sea bottom (1.5s upperleft side) and the well log position of the world record for oil production in ultra-deepwaters 1,877m (corner right side).

Fig. 2 – The spatial volumetric distribution of Maastrichtian 1 reservoir (seismic amplitude pre-stack time migratedversion). This volume has 10km x 13km of dimension with a dominate orientation NE-SW. The reservoir is divided in threemain portions: North, Southeast e West. The North and Southeast portions characterize the Modulo I and West portionthe Modulo II of the Production System, respectively. The dot shows the position of the world record produce well.

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RESERVOIR GEOPHYSICS: SEISMIC PATTERN RECOGNITION APPLIED TO ULTRADEEPWATERSPE 69483 OILFIELD IN CAMPOS BASIN, OFFSHORE BRAZIL 7

Fig. 3 shows seismic section with the temporal window (40ms) used for applied the pattern recognition algorithm. The blue horizonrepresents the Maastrichtian 1 top reservoir. The first black picks below the top represents the bottom the Maastrichtian 1 zone. Thestrong amplitude anomaly characterizes the gas sandstones drilled by the well in the dark blue line position.

Fig. 4 - The volumetric seismic attributes integrated within of the 40ms window; a) seismic amplitude; b) instantaneous frequency;c) Reflection strength and d) cosine of phase. The white line represents the location of the seismic line of the Fig. 3.

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8 JOHANN, P.R.S.; CASTRO, D. AND BARROSO, A. SPE 69483

Fig. 5 – Architectural graph of a simple competitive learning network with feedforward (excitatory) connections from the source nodes(x1 a x4) to the neurons, and the lateral (inhibitory) connections among the neurons (from Haykin, 1994).

a) b)

Fig. 6 – Geometric interpretation of the competitive learning process. The dots represent the input vectors, and the crosses representthe synaptic weight vectors of three output neurons. (a) Initial state of the network, (b) Final state of the network (Rumelhart and Zipser,1985 in Haykin, 1994).

imput cells output cells

RESERVOIR GEOPHYSICS: SEISMIC PATTERN RECOGNITION APPLIED TO ULTRADEEPWATERSPE 69483 OILFIELD IN CAMPOS BASIN, OFFSHORE BRAZIL 9

Fig. 7 – The perceptron. (a) The single layer perceptron with a single neuron. Such a perceptron is limited to performing patternclassification with only two classes. (b) Architectural graph of a multilayer perceptron with two hidden layers. Signal flow through thenetwork progress in a forward direction, from left to right and on a layer-by-layer basis (Haykin, 1994).

Fig. 8 – Seismic amplitude anomaly at Maastrichtian 1 reservoir with the three areas of seismic facies analysis.

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10 JOHANN, P.R.S.; CASTRO, D. AND BARROSO, A. SPE 69483

Fig. 9 – Unsupervised facies analysis. (a) North portion with 4 classes. (b) Maastrichtian 1 reservoir with 2 classes. (c) Southeastportion with 4 classes. (d) West portion with 4 classes.

North Area

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RESERVOIR GEOPHYSICS: SEISMIC PATTERN RECOGNITION APPLIED TO ULTRADEEPWATERSPE 69483 OILFIELD IN CAMPOS BASIN, OFFSHORE BRAZIL 11

Fig. 10 – Supervised facies analysis. (a) North portion with 4 facies. (b) Maastrichtian 1 reservoir with 2 facies. (c) Southeast portionwith 4 facies. (d) West portion with 2 facies.

North Area

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West AreaN

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12 JOHANN, P.R.S.; CASTRO, D. AND BARROSO, A. SPE 69483

Fig. 11 – Seismic trace morphology in the neighborhood of the 4 well used to training the neural network.

Fig. 12 – Structural view of supervised facies in the North portion of the field.

0 50 m

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c)Type III: well 18 se ism ic trace m orphology. d)Type IV: m orphology w ithout am plitude anom alies.

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RESERVOIR GEOPHYSICS: SEISMIC PATTERN RECOGNITION APPLIED TO ULTRADEEPWATERSPE 69483 OILFIELD IN CAMPOS BASIN, OFFSHORE BRAZIL 13

Fig. 13 – Unsupervised and supervised 2 facies in the entire data set, respectively. The good correlation of both approaches showshow seismic morphology represents statistically the geological knowledgement of the field at present time. In the North portion of thefield the red facies represent turbidite sandstones deposited in amalgamated lobes. In the West portion represent gas sandstones. Inthe Southeast portion red facies represent an increasing of sandstones thickness.

Fig. 14 – Unsupervised and supervised 4 facies in the North portion of the field, respectively. Again the good correlation of bothapproaches shows how seismic morphology represents statistically the geological knowledgement of the field at present time. The redfacies (Type I) represent turbidite sandstones deposited in amalgamated lobes. In the supervised approach this facies was betterdefined, the sandstones lobes are more realistic in the supervised approach. Yellow facies (Type II) represent turbidite sandstonesdeposited in the distal part of the lobes. In the supervised approach the distribution of this facies is more connectect between the leftand right side. In terms of volume this facies is more present in the supervised map. Green facies (Type III) represent the distal part oflobes with a reduced thickness. The volume of this facies is more important in the unsupervised approach. Blue facies (Type IV)represent the border of sandstone distribution with very few meters or absence of reservoir.

Amalgamated lobes / gas sandstones D istal lobes Amalgamated lobes / gas sandstones Dis tal lobes

NN

Amalgamated lobesDistal lobes

Thin lobesVer y thin or absent

Amalgamated lobesDis tal lobes

Thin lobesVery th in or absent

N N