Shale pore pressure seismic prediction based on the Hydrogen generation and compaction-based rock physics model and Bayesian Hamiltonian Monte Carlo inversion method

Abstract

The seismic prediction of formation pore pressure is critically important for the exploration and appraisal of shale oil and gas reservoirs, as well as for identifying optimal drilling targets within these formations. In particular, the complex pressure regimes of deep shale reservoirs introduce substantial challenges to traditional seismic prediction methodologies. A Bayesian Hamiltonian Monte Carlo pre-stack seismic inversion approach is proposed to enhance the accuracy of pore pressure estimations based on an advanced rock physics model that integrates compaction and hydrocarbon generation effects. Initially, a comprehensive petrophysical model is developed to account for the effects of hydrocarbon generation and compaction, establishing a robust framework for the normal compaction trend simulation. Subsequently, a novel formation pressure prediction model is derived based on integrating the Eaton model and the Bower stress hypothesis. Then, the Bayesian HMC seismic inversion method is proposed to predict formation pore pressure in shale reservoirs with elastic impedance data set. The proposed methodology is validated through application to actual well log and seismic data from the Fuling shale oil and gas reservoirs in the Sichuan Basin, which demonstrates significant potential of this method for enhancing the prediction of pore pressure in complex geological settings.