One of the leading challenges in hydrocarbon recovery is predicting rock types/fluid content distribution throughout the reservoir away from the boreholes because rock property determination is a major source of uncertainty in reservoir modeling studies. Spatial determination of the lateral and vertical heterogeneities has a direct impact on a reservoir model because it will affect the property distributions. An inappropriate determination of the facies distribution will lead to unrealistic reservoir behavior. Because these data can take different forms (lithologs, cuttings, and for seismic, poststack, and prestack attributes) and have different resolutions, the manual integration of all the information can be tedious and is sometimes impractical. We developed a new neural network-based methodology called democratic neural network association (DNNA). The DNNA method was trained using lithology logs from wells simultaneously with prestack seismic data. This technique, using a probabilistic approach, aims to find patterns in seismic that will predict lithology distribution and uncertainty. IntroductionThe economic viability of a field is dependent on the quality and accuracy of lithology distribution prediction, as well as by the heterogeneity of a potential reservoir. These components are the keys to successful hydrocarbon exploration and production. The rise in unconventional resource prospecting and the increasing complexity of conventional plays have made accurate lithology prediction even more critical. All relevant data must be used optimally to determine lithology at the prospect scale with the highest degree of resolution, resulting in the most geologically meaningful lithology distribution. Risk increases with complexity, however, and the probability of success and the integration of uncertainty into the nature and distribution of lithology must be taken into account in any approach that tries to predict lithology.Conventional approaches are mainly based on 2D or 3D analyses of inverted data to describe the elastic properties of the reservoir. However, precise lithology description in such attribute spaces often overlaps. This makes it difficult to clearly differentiate, for example, intermediate-type facies such as thin interbedded layers. The result is nonunique and highly sensitive to facies interpretation. It, therefore, becomes critical to estimate reservoir connectivity, as some lower qual-

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This paper illustrates a new reservoir characterization approach using seismic pattern recognition methodology based on principal component analysis, trace shape and 3D multi-attributes classification on a set of 3D seismic data volumes. We develop the reservoir interpretation workflow (figure 1) as follows: 1) Seismic facies maps obtained by Kohonen s Self Organizing Map (SOM) Neural Network method to seismic facies volume from a set of various seismic attributes, obtained by Hierarchical classification method; and 2) Statistical analysis of available seismic attributes by application of Principal Component Analysis (PCA) statistical method before classification.How to discriminate the amount of information that each geophysicist is dealing with, in a reservoir characterization process and what is the suitable procedure to help to discriminate between data and information?We present some results of its application on the Paleocene/Eocene reservoirs of a Campos Basin field.Keywords: seismic attributes, seismic facies, reservoir characterization RESUMOEste trabalho ilustra uma nova abordagem para caracterização de reservatórios usando metodologia de reconhecimento de padrão sísmico baseado em análise do componente principal, forma do sinal e classificação de multi-atributos 3D em um volume de dados sísmicos 3D. Nós desenvolvemos o fluxograma de interpretação do reservatório como se segue: 1) Mapas de fácies sísmicas obtidos pelo método de Rede Neural Mapa Auto-Organizável de Kohonen (SOM) para volume de fácies sísmicas, a partir de um conjunto de vários atributos sísmicos obtidos pelo método de classificação Hierárquica; e 2) Análise estatística dos atributos sísmicos disponíveis pela aplicação do método estatístico de Análise de Componente Principal (PCA) antes da classificação.Como discriminar a quantidade de informações em que cada geofísico está envolvido, em um processo de caracterização de reservatório e qual o procedimento indicado para ajudar a discriminação entre dado e informação?Apresentamos alguns resultados desta aplicação nos reservatórios do Paleoceno/Eoceno de um campo na Bacia de Campos.Palavras-chave: atributos sísmicos, fácies sísmicas, caracterização de reservatórios.

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