Interactive Channel Segmentation From 2-D Seismic Images Using Deep Learning and Conditional Random Fields

Abstract

In the exploration of oil and gas reservoirs, channels are important locations for storing oil and gas, and their distribution in the subsurface is usually heterogeneous. Therefore, interpreting channels from seismic data is very meaningful for oil and gas exploration. Currently, methods for extracting channels from seismic data mainly rely on seismic attributes, edge detection algorithms, and deep learning. However, these methods cannot fully and accurately delineate the boundary details and structural characteristics of channels when faced with poor-quality seismic data. To address this issue, we proposed an interactive interpretation method for 2-D channels based on multiattributes and a convolutional neural network (CNN) that could more accurately identify and segment channel bodies with fuzzy boundaries and poor continuity. First, we selected seed points from seismic data to indicate the presence of channels in the area. To highlight the channel structures and reduce the difficulties in identification, we used the geodesic distance map calculated from the seed points and two seismic attributes commonly used for channel identification as the inputs to the CNN model. Next, the probability map of the channels was output from the CNN model to obtain the preliminary results of the channel recognition. Finally, we judged whether additional seed points needed to be added according to the preliminary results, and we combined the conditional random field (CRF) to fuse the geodesic distance map of the additional points with the probability map of the CNN model, ultimately obtaining accurate channel results. Compared to the automatic CNN method, this method extracted more complete channels and improved the continuity of the channel boundaries. In the case of complex seismic data, this method can effectively interpret channels and has important practical significance.