Reservoir Parameter Prediction Using Optimized Seismic Attributes Based on Gamma Test

In the field of oil and gas exploration, reservoir parameter prediction is often affected by multi-solution of the seismic attribute combination, which leads to low prediction accuracy. In this paper, a feature selection method based on Gamma test is proposed to optimize the attribute combination and then combine it with deep neural network to accomplish reservoir parameter prediction. By computing the value of the statistics, it not only provides the best combination of the corresponding attributes to predict the target but also provides the proper training mean square error of neural network and the proper size of the training set. With this guide, over-fitting can be effectively avoided and the prediction accuracy is improved. The selected seismic attributes combination is used as the optimized network input, then use extreme learning machine to accomplish the regression problem. Through the analysis of the real seismic data experimental results, it is proved that the Gamma test is an effective nonparametric tool for feature selection.