Chapter 22 Soft computing: Tools for intelligent reservoir characterization and optimum well placement

Abstract An integrated methodology has been developed to identify nonlinear relationships and mapping between 3D seismic data and production log data. This methodology has been applied to a producing field. The method uses conventional techniques such as geostatistical and classical pattern recognition in conjunction with modern techniques such as soft computing (neuro computing, fuzzy logic, genetic computing, and probabilistic reasoning). An important goal of our research is to use clustering techniques to recognize the optimal location of a new well based on 3D seismic data and available production-log data. The classification task was accomplished in three ways; (1) k-mean clustering, (2) fuzzy c-means clustering, and (3) neural network clustering to recognize similarity cubes. Relationships between each cluster and production-log data can be recognized around the well bore and the results used to reconstruct and extrapolate production-log data away from the well bore. This advanced technique for analysis and interpretation of 3D seismic and log data can be used to predict: (1) mapping between production data and seismic data, (2) reservoir connectivity based on multi-attribute analysis, (3) pay zone estimation, and (4) optimum well placement.

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