Seismic facies classification and identification by competitive neural networks

Abstract

We present an approach based on competitive neural networks for the classification and identification of reservoir facies from seismic data. This approach can be adapted to perform either classification of the seismic facies based entirely on the characteristics of the seismic response, without requiring the use of any well information, or automatic identification and labeling of the facies where well information is available. The former is of prime use for oil prospecting in new regions, where few or no wells have been drilled, whereas the latter is most useful in development fields, where the information gained at the wells can be conveniently extended to the interwell regions. Cross‐validation tests on synthetic and real seismic data demonstrated that the method can be an effective means of mapping the reservoir heterogeneity. For synthetic data, the output of the method showed considerable agreement with the actual geologic model used to generate the seismic data; for the real data application, the predicted facies accurately matched those observed at the wells. Moreover, the resulting map corroborates our existing understanding of the reservoir and shows substantial similarity to the low‐frequency geologic model constructed by interpolating the well information, while adding significant detail and enhanced resolution to that model.