Multiple seismic attributes fusion approach with support vector regression and forward simulation for sand body prediction and sedimentary facies interpretation — A case of the X gas field in Xihu Sag

Abstract

Marine exploration and production play a vital role in petroleum industries. It is difficult to acquire sufficient well data in marine settings, so seismic data become the most important interpretation data in research. In general, the seismic data in marine exploration have low quality because of the deep depth from the surface. To partly address this, the support vector regression (SVR) algorithm is proposed to fuse multiple seismic attributes for sand thickness prediction. First, we use forward modeling to establish virtual wells for improving the training data set. Second, we select the optimal attributes by correlation analysis. Third, we apply the SVR algorithm to learn the relationship between seismic attributes and sand thickness. Fourth, we use the SVR model to predict the sand thickness between wells by calculating a fused attribute. The results indicate that the fusion attribute with SVR has a higher correlation coefficient with sand thickness than the original attributes by statistical method. The approach can be widely used for improving the seismic interpretation quality of the research area with few wells.