SPE Copyright 2001, Society of Petroleum Engineers Inc.

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1 SPE Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfield in Campos Basin, Offshore Brazil Paulo Johann, Dayse D. de Castro and Alberto S. Barroso, Petrobras S. A. Copyright 2001, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the SPE Latin American and Caribbean Petroleum Engineering Conference held in Buenos Aires, Argentina, March This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box , Richardson, TX , U.S.A., fax Abstract Usually, seismic data is used in a qualitative approach to detect changes in the waveform and to pick acoustic continuity of a peak and/or a through as a structural mapping tool. The seismic interpretation is a qualitative process for building a geological model. Today, many works try to use the seismic information in a quantitative approach. Seismic interpretation in a quantitative approach is a key process in the integration of geoscience data at scales from basin-wide studies, reservoir focused and field-development process. Quantitative modeling could be deterministic and/or probabilistic. We use, in many steps of a seismic processing sequence, examples of quantitative deterministic modeling like seismic migration, some seismic inversion methodology, etc. Probabilistic modeling can be gathered in two groups: multivariate statistics and geostatistics approaches. Close to probabilistic modeling, we have also the neural network method. In this paper, we focus on the application of neural network modeling for seismic pattern recognition (seismic facies analysis) applied an ultra-deepwater turbidite oilfield reservoir in Campos Basin, offshore Brazil. Introduction A 3-D reservoir architecture characterization requires the integration of different data types to define a more detailed and realistic geological interpretation. Well logs and core data provided detailed information about the vertical variation of many reservoirs properties but they are restricted to regions adjacent to the borehole. 3-D seismic data play an important role in describing external and internal complexities of reservoirs away from the wellbore and to define geometric description of structural and stratigraphic aspects of the reservoirs (ref. 1). Seismic amplitude variations are linked to changes in acoustic impedances that we can be trying to relate to reservoir properties. This paper demonstrates a methodology for seismic pattern recognition in a targetoriented approach to aid the reservoir architecture characterization in a more detailed and accurate 3-D seismic interpretation. Seismic pattern recognition techniques are used to distinguish important geological features from seismic information. The methods of seismic pattern recognition can provide solution to practical problems in reservoir characterization in terms of automatic mapping of main features of seismic morphology related to geological environment. The automatic interpretation of subsurface geology from seismic data is possible by analyzing of the nature of waveform cycles and their termination with respect to adjacent reflections. The geometry and the terminations of waveform styles help to locate boundaries between zones corresponding to different types of depositional units each associated with characteristics of seismic morphology under study. In this paper a target-oriented automatic pattern recognition methodology is applied to 3-D seismic data set a seismic stratigraphic unit of an ultra-deepwater turbidite sandstones reservoir. The pattern recognition method is applied in two approaches: unsupervised and supervised. The unsupervised approach to exploit the statistically common characteristic underlying seismic traces segments at the seismic stratigraphic unit. The supervised approach uses the stratigraphic knowledge to guide the pattern recognition. The seismic pattern recognition methodology used is carried out in six main steps: (1) spatial and temporal sampling, (2) attributes selection; (3) definition of the number of classes and iteration; (4) training and classification with a competitive learning algorithm (unsupervised approach) and (5) training and classification with a back-propagation algorithm (supervised approach) and (6) interpretation of seismic facies. Work on artificial neural network has been motivated from the results obtained in terms of useful computation of learning process of seismic waveform. To achieve good performance, neural network employ a massive interconnection of simple computing cells referred to as neurons or processing units. The

2 2 JOHA, P.R.S.; CASTRO, D. AD BARROSO, A. SPE definition of neural network is a massively parallel distributed processor that has a natural propensity for storing experimental knowledge and making it available for use (ref. 2). Geology data set description The ultra-deepwater turbidite oilfield analyzed in this study was discovered in October 1996, located 125 miles from the coast in the northeast portion of Campos basin, offshore Brazil, in water considered to be ultra-deep (between 1,500-2,000m) (Fig. 1). Campos basin is located in the southeastern coast of Brazil and it covers an area of about 100,000 km 2 from the coast to the 3,400m isobath. The Vitória high separates it from Espírito Santo basin to the north and from Santos basin by the Cabo Frio high to the south. This field is a large oilfield with a complex hydrocarbon distribution (OOIP around 2.0 billions m 3 with API from 18.6º to 31.5º). The external geometry of the field is defined to the north and east by dipping and to the south and west directions by stratigraphic pinchout. The oil entrapment is composed by structural and stratigraphic framework. Two main factors controlled deep and ultra-deepwater sedimentation in Campos basin: thermal subsidence pattern which control turbidite sedimentation to certain preferable areas and salt movement which allowed stacking of sandstones in depositional lows. Unconformities due to sealevel variation and submarine paleocanyons are the additional factors that control reservoir distribution (ref. 3). Hydrocarbon accumulation are Miocene to Maastrichtian ages. The lithology is a turbidite sandstones deposited in a widespread structure depression. The halokinesis process controlled the structural framework. The stratigraphic zonation from well logs measurements divided the reservoir in three main sequences, with internal sub-division (ref. 4). Five main zones can be characterized with average thickness of 30m by zone. Total oil net sand in the field is very thick, with average around 160m. In this paper we focus in the Maastrichtian 1 reservoir zone. This reservoir was used in the beginning of the delimitation of the field to build the initial structural map and to estimate from seismic data the reservoir distribution (ref. 5). Development strategy overview The decision by Petrobras to develop the field immediately after its discovery was based upon the success of the drilling of exploration wells (ref. 3). The improved of 3-D seismic data and the technological training program that has given Petrobras the capability to produce oil and gas in ultradeepwater in Campos basin. The Pilot System start on January 1999 and the field became the holder of world record for oil production in ultradeepwater (1,853m water depth). The objective of Pilot System was to collect fundamental information about the reservoir and test new technologies to be applied in the production system. Also this system has furnished information that allow optimization of the subsequent field exploitation stages, thus helping reduce technical, economic and environmental risks during these phases, when large volumes of oil and gas are being produced. The Phase1 of permanent production system start on April 2000 and produce the orth and Southeast portions of the reservoir. Between the first 9 wells drilled in this phase of production, that confirmed the oil with excellent quality 31.5º API, Petrobras had the world record for oil production in ultradeepwater (1,877m, Fig. 1). Principles of methodology - seismic facies analysis The pattern recognition methodology is carried out in six main steps (ref. 7): (1) spatial and temporal sampling, (2) attributes selection; (3) definition of the number of classes and iterations; (4) training and classification with a competitive learning algorithm (unsupervised approach) and (5) training and classification with a back-propagation algorithm (supervised approach) and (6) interpretation of seismic facies. Spatial and temporal sampling of the data set. The 3-D seismic volume available over the reservoir has 414 km 2. The cell dimension is 13,6m by 26,6m, with a spatial density of traces/km 2. The record length is from 0 to 6 seconds, with 2ms of sample rate. The seismic data used in the pattern recognition is migrated pre-stack in time. The first step in the methodology is the choice of the representative sampling of the seismic data available over the reservoir. The reservoir external geometry is the first point to take in account to define the area of analyzes. In our study the external geometry of the Maastrichtian 1 reservoir was the guide to define the polygon of study. Figure 2 shows the spatial volumetric distribution of Maastrichtian 1 reservoir in terms of seismic amplitude prestack migrated in time. The focus in the area of the reservoir distribution reduces to 130 km 2 ( seismic traces). The data to apply the pattern recognition algorithm. The temporal sampling was a sub-volume cut from 10ms above and 30ms below the Maastrichtian 1 reservoir top, respectively. The window of 40ms used in the pattern recognition also reduces the input data from 3000 samples/trace in the raw data to 20 samples/traces under study ( seismic samples). The seismic horizon representative of Maastrichtian 1 reservoir was carefully picking before the definition of the temporal window for the pattern recognition. Figure 3 shows the temporal window over a representative seismic line used for pattern recognition algorithm. Attributes selection. Six volumetric seismic attributes were selected for carried out the seismic facies analysis. All attributes were calculated over the volume inside the window around the Maastrichtian 1 reservoir. The attributes analyzed were integrated seismic amplitude, integrated instantaneous frequency, integrated reflection strength, integrated cosine of phase, integrated apparent seismic polarity, and integrated seismic magnitude and rms amplitude. In this step some statistical analysis was carried out, matrix of correlation

3 RESERVOIR GEOPHYSICS: SEISMIC PATTER RECOGITIO APPLIED TO ULTRADEEPWATER SPE OILFIELD I CAMPOS BASI, OFFSHORE BRAZIL 3 between the attributes analyzed, to define the attributes most relevant for the discrimination of waveform characteristics. This quality control is very important to apply before the used of cluster and classification algorithms. In this study the integrated seismic magnitude was remove because the coefficient of correlation with rms amplitude is 0.9. Figures 4a to 4c shows four seismic attributes map used as input data to the pattern recognition algorithms. In this step we define the entire data set in the attribute space. In the case of the Maastrichtian 1 reservoir were seismic samples of six seismic attributes, 6-dimensional vector space. Definition of the number of classes and iteration number. Before the definition of the number of classes some consideration are necessary to understand the target of the study. In the unsupervised approach we can used more classes than the number of geological facies expected in the area of the study. Some seismic waveforms are not related to geological information, like multiples, diffractions or another seismic artifacts from acquisition and/or seismic processing. Otherwise, in the supervised approach will be interesting to keep a number of classes related to the geological facies in the area under study. In the case of the Maastrichtian 1 reservoir studied in this project the number the classes analyzed were 2, 3, 4 and 5. The number of iterations was from 100 to We used a standard workstation to run the neural network algorithm. The time to run 1000 iterations is less the five minutes. Unsupervised approach for seismic facies analysis. The unsupervised approach for seismic facies analysis used in this study base their classification on the clustering of the entire data set. The results do not depend on geological and/or well log information. The seismic waveform will be clusters in terms of its statistical characteristics. The unsupervised approach is appropriate where no reliable geological information is available, like in an exploratory context; or in order to verifies the existence of clusters and their respective separation in an un-biased cluster analysis. The unsupervised approach used a neural network competitive learning algorithm. Like the non-parametric statistical methods the competitive learning algorithms do make any assumptions about the statistical distribution of the data set. In the beginning of the classification the user specifies the number of classes. During training, attribute vectors are iteratively presented and the cluster vector that is closest to the attribute vector is updated so that it is even more likely to win the next time the particular attribute vector is presented. Training is stopped after a certain number of iterations, or when the cluster vector only changes marginally. Three parameters control the algorithm: (1) number of iterations; (2) learning rate and (3) conscience. The numbers of iterations control how many times the input vectors are iterated through in order to find that stable cluster vectors. The learning rate adjusts how fast the competitive network is allowed to adjust its weights in order to map the input vectors into pre-determined number of classes. The conscience parameter reduces greediness from the algorithm. Conscience mechanism ensures that output classes that are winning a lot, get a bad consciousness and temporarily withdraw from the competition (ref. 7). Competitive Learning Method. Figure 5 shows the schematic 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 a neural network compete among themselves for being the one to be active (or fired). Thus, in the case of competitive learning only a single output neuron is active at any one time. It is this feature that makes competitive learning highly suited to discover those statistically salient features that may be used to classify a set of input patterns, like seismic waveform in our study. There are three basic elements to a competitive learning rule (ref. 6): - A set of neurons that are all the same except for some randomly distributed synaptic weights and which therefore response 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 one output neuron, or only one neuron per group, is active at a time. The neuron that wins the competition is called a winner-takes-all-neuron. The individual neuron of the network learns to specialize on sets of similar patterns, and thereby become feature detectors. In the simplest form of competitive learning, the neural network has a single layer of output neurons, each of which is fully connected to the input nodes. The network may include lateral connections among neurons (Fig. 5). To illustrate the essence of competitive learning, we used the geometry analogy (Fig. 6, ref. 6). It is assumed that each input pattern x has some constant length, so that we may view it as a point on an -dimensional unit sphere, where is the number of input nodes; also represents the dimension of each synaptic weight vector. It is further assumed that all neurons in the 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 -dimensional unit sphere. Figure 6a shows three natural groupings (clusters) of the stimulus pattern represented by dots; this figure also includes a possible initial state of the network (represented by crosses) that may exist before learning. Figure 6b shows a typical final state of the network that results from that use of competitive learning (refs. 2 and 6). Supervised approach for seismic facies analysis. In the geoscience research geologist and geophysicist have a lot of a priori information about the data set under study, particularly if we have well logs drilled in the field. ormally, in the reservoir studies we have a number of well log informations available when we make seismic pattern recognition study. Thus, detailed and reliable information is available at well log position. This information is too important not be considered

4 4 JOHA, P.R.S.; CASTRO, D. AD BARROSO, A. SPE in a seismic classification process. The classes defined with auxiliary data like geological knowledge are given by an interpretative analysis of entire system rather than a statistical definition. Usually, the a priori information is fed into the system as training data set, i.e. attribute vectors, that have a know class relationship. It is evident that a substantial number of attribute vectors have to be made available for each class, in order to get a proper statistic description of that data distribution. Under the assumption that sufficient data are available for each class, a subset can be taken away from the training data and be reserved as validation data. A successful classification will predict the correct class at the validation data locations. The method of supervised seismic facies used a back error propagation algorithm (ref. 7). Error Back-Propagation Method. The back-errorpropagation algorithm is the most popular and widely used learning method in neuron-computing today. It works by doing iterative gradient descend in weight space, minimizing the square error between the desired and actual outputs. Backpropagation type networks work by constructing hyperplanes, thus dividing the -dimensional hyperspace into decision regions. The advantage of the error back-propagation algorithm: can resolve non-linear attribute or class relations and no assumptions about data distribution. The network is trained using the back-propagation technique, in which the actual output is compared with the desired output and errors are propagated backwards through the network in order to update the weights. The perceptron is the simplest form of a neural network (Fig. 7a) used for the classification of a special type of patterns said to be linearly separable (i.e., patterns that lie on opposite sides of hyperplane, ref. 2). The algorithm used to adjust the free parameters of these neural network firsts appeared in a learning procedure developed by Rosenblat (1958, 1962) for his perceptron brain model. The multilayer perceptron network consist of a set of sensory units (source nodes) that constitute the input layer, one or more hidden layers of computation nodes, and an output layer of computation nodes. The input signal propagates through the network in a forward direction, on a layer-by-layer basis. These neural networks are commonly referred to as multilayer perceptron (Fig. 7b). Multilayer perceptrons have been applied successfully to solve some difficult and diverse problem by training them in a supervised manner with a highly popular algorithm known as the error back-propagation algorithm. This algorithm is based on the error-correction learning rule. The error back-propagation algorithm consists of two passes through the different layers of the network: a forward pass and a backward pass. In the forward pass, an activity pattern (input vector) is applied to the sensory nodes of the network, and its effect propagates through the network, layer by layer. Finally, a set of outputs is produced as the actual response of the network. During the forward pass the synaptic weights of the network are all fixed. During the backward pass, the synaptic weights are all adjusted in accordance with the error-correction rule. The actual response of the network is subtracted 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 backpropagation (ref. 2). The training of the perceptron to distinguish between the different classes results in a set of node weights, which define the trained network. The network topology must be decided at the training stage. The input layer will have one node for each attribute in the vector, or if more than one vector is used at a time, in a contextual approach, one node for each attribute of each vector. The number of hidden layers will depend upon the complexity of surface decision required. The training of the network can be tested by the used of a verification data set, the validation data set, which is known but excluded from the training data. The error rate classification using the neural network can then be found for each class. If the error rate is too high the network must be retrained, using different learning parameters or even a different network topology, or some new attribute set selected. The are four parameters to control the error-backpropagation algorithm (ref. 7): (1) learning rate, (2) momentum, (3) epochs and (4) error limit. The learning rate tells network how much its weights can be adjusted at each learning step. Momentum is used to increase learning speed. Epochs control how the network processes many times the whole set of input vectors. The error limit controls when to stop training. The value signifies the total rms error in the network output. Application of seismic facies analysis The Maastrichtian 1 reservoir of the ultra-deepwater turbidite in Campos basin focused in this study was divided in three main areas (Fig. 8): orth, Southeast and West portions. The seismic pattern recognition algorithms was applied first, in the entire data set and second, in each portions of the main occurrence of the reservoir. Unsupervised approach for seismic facies analysis. In the unsupervised approach we seek to estimate the statistical groups underlying all seismic traces segments at seismic stratigraphic unit. Figures 9a to 9d shows the results of the application of the unsupervised seismic facies algorithm, competitive learning, over the orth portion, entire data set, Southeast portion and West portion of the Maastrichtian 1 reservoir, respectively. After tested from 2 to 5 classes with different number and combination of seismic attributes we decide to keep 4 classes for each portion of the reservoir. For the entire data set with strong presence of diffractions and noise in the faults region we decide to keep 2 classes as representative of Maastrichtian 1 reservoir in the unsupervised approach. Typically, we used from 100 to 1000 iterations, with good results with 1000 iterations. The interest in the results of unsupervised approach is the very well correlation between the classes and the geological facies interpretation of field. The red class is associated with turbidite sandstones

5 RESERVOIR GEOPHYSICS: SEISMIC PATTER RECOGITIO APPLIED TO ULTRADEEPWATER SPE OILFIELD I CAMPOS BASI, OFFSHORE BRAZIL 5 amalgamated lobes. In the West portion is associated with the gas sandstones drilled in the middle of this portion or thick sandstones lobes. The orange class is interpreted as distal sandstones lobes already drilled in the orth portion of the field. The another two classes can be interpreted as distal lobes with reducing thickness with interference of some seismic artifacts not directly related with geological information of the reservoir. Supervised approach for seismic facies analysis. In the supervised approach wells were regrouped in four classes at seismic stratigraphic unit (Maastrichtian 1 reservoir). In this approach the seismic traces segments nearest the wells was used for built the learning data set. In the orth portion of the reservoir the figures 11a to 11d shows the four seismic lines crossing the wells used as reference for the learning data set. Type I is characterized by strong through amplitude at the top and strong peak at the bottom of reservoir. The well 2 represent this class. The geology information from well 2, in the Maastrichtian 1 reservoir, is interpreted as a deposition of turbidites amalgamated lobes. Type II is characterized by amplitude anomaly at top of reservoir less important than the type I, particularly, in the bottom (peak) of the reservoir. The well 5 represent this class. The geological interpretation of the well log information allows interpreting the sandstones deposited in a distal portion of sandstones lobes. Type III is characterized by a weak amplitude anomaly. The well 18 represent this class. The geological information from the well logs allow interpret this class close to the type III, distal portion of turbidites lobes, but with an important reducing of reservoir thickness. Type IV is characterized by a seismic morphology relatively without anomalies. It is represents the reservoir limit or absence. For the Southeast and the West portions of the reservoir this seismic morphology can be used, but in the West portion the seismic anomaly is also associated with the gas sandstones (red facies in the middle of that region). In the Southeast portion the most important variation captured by seismic pattern recognition is related to thickness variation in the reservoir distribution. The Figures 10a to 10d shows the results of the application of the supervised seismic facies algorithm over the orth portion, entire data set, Southeast portion and West portion of the Maastrichtian 1 reservoir, respectively. Comparison of unsupervised and supervised approaches The unsupervised and supervised 2 facies maps and 4 facies maps shows a good correlation in both approaches (Figs. 13 and 14). Consequently, the seismic morphology represents statistically very well the geological knowledgement of the field with the well log information available. In the orth portion of the field the red facies (Type I) represent turbidite sandstones deposited in amalgamated lobes. In the West portion represent gas sandstones. In the Southeast portion red facies represent an increasing of sandstones thickness. Some parts of the facies map with the red facies in the West portion represent also turbidite sandstones with oil. In the supervised approach the red facies (Type I) was better defined, the sandstones lobes are have a more more realistic distribution. Yellow facies (Type II) represent turbidite sandstones deposited in the distal part of the lobes. In the supervised approach the distribution of this facies is more connectected between the left and right side. In terms of reservoir volume this facies is more present in the supervised map. Green facies (Type III) represent the distal part of lobes with a reduced thickness. The volume of this facies is more present in the unsupervised map. Blue facies (Type IV) represent reservoir limit or absence (Fig 14). Conclusion The successful application of the neural network methodology for 3-D seismic reservoir characterization using seismic pattern recognition facies analysis in the ultradeepwater oilfield makes it an interesting approach for other turbidites fields in Campos Basin, offshore Brazil. The facies maps are automatic and very fast to apply huge reservoirs. These maps help the seismic interpret to define the reservoir distribution in the seismic scale and to aid the integration of seismic information with the geological knowledge of the field. The results can be used for understand the seismic stratigraphy and sequence stratigraphy on the turbidites systems studied. The perspective will be the application of the seismic facies analysis in the another four stratigraphic sequence of the reservoir and to compare the neural network used in this study with another algorithm available in the petroleum industry. Acknowledgments We thank Petrobras for permission to publish this paper. Special thanks to the geologist Carlos Varela Stank and Darci José Sarzenski for the support in the geological discussion about the reservoir studied. References 1. Johann, P. et al.: 3-D, 1996, Reservoir Characterization by Stratigraphic Inversion and Pattern Recognition, SEG Annual Technical Conference and Exhibition, Denver, ovember 11-16, Expand Abstracts. 2. Haykn, S., 1994, eural etworks- A Comprehensive Foundation, Prentice Hall. 3. Rangel, H.D. et all., 1998, Roncador Field. A ew Giant in Campos Basin, Brazil, OTC, Houston: Barroso, A. et al., 2000, Roncador Giant Oilfield: Exploration and Production from a heterogeneous Maastrichtian turbidite reservoir in ultra-deepwater Campos basin, Brazil, AAPG. 5. Santos, P.R. et all., 1999, Geophysical and Log Characterization of Roncador Field, Campos Basin, Brazil SBGf Rio Rumelhart, D. E. and Ziper, D., 1985, Feature discovery by competitive learning. Cognitive Science 9, Geoframe SeisClass, 2000, Technical documentation. 8. Rosenblat, F. (1958), The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, Rosemblat, F. (1962), Principles of eurodynamics. Washington, DC: Spartan Books.

6 6 JOHA, P.R.S.; CASTRO, D. AD BARROSO, A. SPE ,5 TWT (s) 5,0 Fig. 1a Localization map for the oil fields from Campos Basin; b) 3-D seismic volume with the sea bottom (1.5s upper left side) and the well log position of the world record for oil production in ultra-deepwaters 1,877m (corner right side). 3,2 TWT (s) 4,0 0 2 km Fig. 2 The spatial volumetric distribution of Maastrichtian 1 reservoir (seismic amplitude pre-stack time migrated version). This volume has 10km x 13km of dimension with a dominate orientation E-SW. The reservoir is divided in three main portions: orth, Southeast e West. The orth and Southeast portions characterize the Modulo I and West portion the Modulo II of the Production System, respectively. The dot shows the position of the world record produce well.

7 RESERVOIR GEOPHYSICS: SEISMIC PATTER RECOGITIO APPLIED TO ULTRADEEPWATER SPE OILFIELD I CAMPOS BASI, OFFSHORE BRAZIL 7 3,2 W E TWT (seconds) 3,4 3,5 Fig. 3 shows seismic section with the temporal window (40ms) used for applied the pattern recognition algorithm. The blue horizon represents the Maastrichtian 1 top reservoir. The first black picks below the top represents the bottom the Maastrichtian 1 zone. The strong amplitude anomaly characterizes the gas sandstones drilled by the well in the dark blue line position. a) b) 0 10 km 0 10 km 0 10 km c) d) 0 10 km 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.

8 8 JOHA, P.R.S.; CASTRO, D. AD BARROSO, A. SPE imput cells output cells 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 represent the 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).

9 RESERVOIR GEOPHYSICS: SEISMIC PATTER RECOGITIO APPLIED TO ULTRADEEPWATER SPE OILFIELD I CAMPOS BASI, OFFSHORE BRAZIL 9 Fig. 7 The perceptron. (a) The single layer perceptron with a single neuron. Such a perceptron is limited to performing pattern classification with only two classes. (b) Architectural graph of a multilayer perceptron with two hidden layers. Signal flow through the network progress in a forward direction, from left to right and on a layer-by-layer basis (Haykin, 1994). orth West Southeast Fig. 8 Seismic amplitude anomaly at Maastrichtian 1 reservoir with the three areas of seismic facies analysis.

10 10 JOHA, P.R.S.; CASTRO, D. AD BARROSO, A. SPE orth Area a) Ama lgamated lobes Thin lob es Very thin lobes Southeast Area Ama lgamated lobes Thick sandstones b) Amalgamated lobes Distal lo bes Thin lobes West Area c) d) Gas/thick sand Thick sandstones Thin sa ndstones Very th in or absent sandstones Fig. 9 Unsupervised facies analysis. (a) orth portion with 4 classes. (b) Maastrichtian 1 reservoir with 2 classes. (c) Southeast portion with 4 classes. (d) West portion with 4 classes.

11 RESERVOIR GEOPHYSICS: SEISMIC PATTER RECOGITIO APPLIED TO ULTRADEEPWATER SPE OILFIELD I CAMPOS BASI, OFFSHORE BRAZIL 11 orth Area Amalgamated lobes T hin lobes Very thin or absent a) Southeast Area Thick sandstones b) Amalgamated lobes Very th ick sandstones Sandstones intercalated West Area c) Gas/thick sandst ones d) Fig. 10 Supervised facies analysis. (a) orth portion with 4 facies. (b) Maastrichtian 1 reservoir with 2 facies. (c) Southeast portion with 4 facies. (d) West portion with 2 facies.

12 12 JOHA, P.R.S.; CASTRO, D. AD BARROSO, A. SPE ,5 3,6 3, m 3, m a)type I: well 2 seismic trace morphology. b)type II: well 5 seismic trace morphology. 3,5 3,5 3, m 0 50 m 3,6 c)type III: well 18 seismic trace morphology. d)type IV: morphology without amplitude anomalies. Fig. 11 Seismic trace morphology in the neighborhood of the 4 well used to training the neural network. Well 2 Well 5 Well 18 orth Area Amalgamated lobes Thin lobes Very thin or absent 0 2 km Fig. 12 Structural view of supervised facies in the orth portion of the field.

13 RESERVOIR GEOPHYSICS: SEISMIC PATTER RECOGITIO APPLIED TO ULTRADEEPWATER SPE OILFIELD I CAMPOS BASI, OFFSHORE BRAZIL 13 Amalgamated lobes / gas sandstones Amalgamated lobes / gas sandstones Fig. 13 Unsupervised and supervised 2 facies in the entire data set, respectively. The good correlation of both approaches shows how seismic morphology represents statistically the geological knowledgement of the field at present time. In the orth portion of the field the red facies represent turbidite sandstones deposited in amalgamated lobes. In the West portion represent gas sandstones. In the Southeast portion red facies represent an increasing of sandstones thickness. Amalgamated lobes Thin lobes Ver y thin or absent Amalgamated lobes Thin lobes Very th in or absent Fig. 14 Unsupervised and supervised 4 facies in the orth portion of the field, respectively. Again the good correlation of both approaches shows how seismic morphology represents statistically the geological knowledgement of the field at present time. The red facies (Type I) represent turbidite sandstones deposited in amalgamated lobes. In the supervised approach this facies was better defined, the sandstones lobes are more realistic in the supervised approach. Yellow facies (Type II) represent turbidite sandstones deposited in the distal part of the lobes. In the supervised approach the distribution of this facies is more connectect between the left and right side. In terms of volume this facies is more present in the supervised map. Green facies (Type III) represent the distal part of lobes 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.