Reservoir prediction via SVM pattern recognition

Abstract

We present a new classification technique 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, point out the difference between SVM and neural network approaches, and apply the method to a 3D seismic dataset for the “YD” 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 than are neural networks and can be used to distinguish oil and gas reservoirs.