Multiattributes pattern recognition for reservoir prediction

A new classification technique is presented to recognize and predict reservoirs from seismic data using support vector machine (SVM) pattern recognition. As the method is data-driven it is especially suitable for use with non-linear multiattributes. The method has good generalization ability for cases where the populations are small. In this paper, we describe the method, and apply the method to a 3D seismic dataset for the “Large Save” oilfield. First, we train the SVM using 3D seismic multiattributes at known well locations with well test results. The resulting SVM structure is used to make predictions away from the wells. It is demonstrated that the method is less subject to overtraining difficulties and can be used to distinguish oil and gas reservoirs.