Structure analysis of shale and prediction of shear wave velocity based on petrophysical model and neural network

Accurate shear wave velocity is very important for seismic inversion. However, few researches in the shear wave velocity in organic shale have been carried out so far. In order to analyze the structure of organic shale and predict the shear wave velocity, the authors propose two methods based on petrophysical model and BP neural network respectively, to calculate shear wave velocity. For the method based on petrophysics model, the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale, and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio, porosity and density. The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity. The optimal porosity aspect ratio and the shear wave velocity are predicted. For the BP neural network method that applying BP neural network to the shear wave prediction, the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network, and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship. The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province. The predicted shear wave velocities of the two methods match well with the actual shear wave velocities, indicating that these two methods are effective in predicting shear wave velocity.