Bayesian decision theory is a statistically based theory that is used to assess the degree of certainty and the potential costs when making decisions. This paper presents a methodology, based on the Bayesian decision theory, used to infer subsurface lithofacies and saturation fluid by integrating different data sources, such as well logs data and seismic attributes, which are derived from an elastic seismic inversion. This methodology was applied on a data volume from an offshore Brazilian field to generate, as a final product, a lithofacies model and a fluid indicator for this field. Uncertainty quantification of the models was also analyzed at this work. To infer the subsurface lithofacies, the existing facies were identified from well logs data, using the expectationmaximization (EM) algorithm. This step defines the lithofacies behavior in seismic attributes domains through the use of probability density functions (PDF). Next, the subsurface lithofacies were classified by applying the maximum posterior probability (MAP) classification, using the seismic attributes as input and the PDFs computed previously. The environment was divided into cells, then the probability and uncertainty was assessed to infer the lithofacie for each cell. After inferring the subsurface lithofacies, the fluid was inferred for the cells identified as reservoir lithofacies. Assuming an oil-water system, the fluid substitution theory and the Bayes theorem were applied to the well log data to determine the PDFs for each scenario. Following the Bayesian decision theory, the most likely fluid and the associated error was determined for each cell identified as reservoir.
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