CBM reservoir thickness prediction using the seismic nonlinear stochastic inversion method

Abstract

Due to the complex reservoir conditions and rapid changes in lithological facies in seismic exploration, predicting coalbed methane (CBM) reservoirs is quite challenging. Conventional inversion methods are not highly effective at predicting reservoir thickness, and cannot keep up with current demands. Our aim is to demonstrate how seismic data can be used to forecast coal thickness, as well as the distribution and orientation of subtle structures that may be linked to enhanced permeability zones. In this study, we used a nonlinear stochastic inversion method based on making full use of seismic data and constraining it with known information, such as drilling and logging analysis. This method has been successfully applied in a mining area in Wuxiang County in the southeastern part of Shanxi Province. Compared with the actual geological data, it is found that the prediction accuracy is consistent with the drilling results, and the distribution of the predicted CBM reservoir thickness is consistent with the geological information. Furthermore, due to the randomness of the subsurface medium, the accuracy of reservoir prediction can be increased by observing the target layer seismic reflection wave amplitude, frequency, and other properties.