Shear-Wave Velocity Prediction by CNN-GRU Fusion Network Based on the Self-Attention Mechanism

Abstract

Elastic parameters such as compressional-wave velocity and shear-wave velocity are essential for characterizing and predicting oil-gas reservoirs. However, the current commonly used shear-wave velocity prediction methods have problems such as weak generalization of empirical formulas and difficulty in obtaining some rock parameters in various rock physics models. We proposed a deep learning network based on the self-attention mechanism to predict shear-wave velocity. First, we need to extract the spatial and temporal features of well logging data using convolutional neural network (CNN) and gated recurrent unit (GRU), respectively. However, the spatial and temporal features exhibit different correlations in the depth direction due to the gradual variation of sedimentary layers. Thus, we fuse the self-attention mechanism with the deep learning network to enhance the network’s sensitivity to crucial spatiotemporal features. Finally, we take the tight sandstone reservoir of Tarim Basin as the research object to estimate shear-wave velocity using CNN, GRU network, and our optimized method. The results show that the CNN-GRU fusion network based on the self-attention mechanism network we proposed is better than the other two networks in the prediction accuracy and generalization degree.