Shear wave velocity prediction of shale oil formations based on machine learning and improved rock physics model
FANG Zhijian1, BA Jing1, XIONG Fansheng2, YANG Zhifang3,4, YAN Xinfei3,4, RUAN Chuantong1
1. School of Earth Sciences and Engineering, Hohai University, Nanjing, Jiangsu 211100, China; 2. Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China; 3. China National Petroleum Corporation Exploration and Development Research Institute, Beijing 100083, China; 4. China National Petroleum Corporation Key Laboratory of Geophysics, Beijing 100083, China
Abstract：Conventional shear wave （S-wave） velocity prediction methods include empirical formulas and rock physics model methods. The former is suitable for reservoirs with relatively simple rock mineral compositions, and it is affected by areas and some other factors. Therefore, it is difficult to be widely applied for different formations and has low prediction accuracy. The latter requires selecting appropriate rock physics models based on different situations, so as to achieve the expected goals. Most machine learning methods for S-wave velocity prediction aredriven by pure data, and the quality and quantity of the dataset directly determine the accuracy of the S-wave velocity prediction model, which are in lack of sufficient physical insights. Therefore, based on the deep neural network （DNN） methods, this paper assumes that the mathematical form of wave propagation equations for the reservoir in the study area is known, but the elastic parameters are unknown and are learned through a DNN training on the basis of well logging data, so as to establish the wave propagation equations of the target layer. The corresponding compressional wave （P-wave） and S-wave velocities are obtained with the plane wave analysis method to connect the neural networks and the theoretical model. In addition, to address the shortcomings of the conventional Xu-White model, an improved rock physicsmodel for S-wave velocity prediction is proposed by considering the pore aspect ratio varying with depth. By using the adequate well logging data in the study area, the established DNN model and the improved rock physics model for S-wave velocity prediction are used to predict the S-wave velocity, and the results are compared with the conventional Xu-White model. It shows that both the DNN model and the improved rock physics model can help obtain high-precision S-wave velocity prediction results, and the former has better prediction performances.
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