Direct Petrophysical Property Prediction Through Joint Stochastic Inversion of PP and PS Waves

Abstract

Pre-stack seismic inversion provides an effective approach for estimating reservoir properties from seismic observations. However, the use of PP-wave data alone often suffers from insufficient sensitivity to shear-related information, leading to considerable uncertainty in the estimation of multiple coupled reservoir parameters. To alleviate this limitation, this study proposes a PP–PS wave joint stochastic inversion framework for direct prediction of reservoir properties. A statistical rock physics relationship is established to characterize the nonlinear relationship between seismic elastic responses and key reservoir parameters, including porosity, mineral composition, and water saturation. Based on this relationship, a physical-parameter-based PS-wave reflection coefficient approximation is derived and incorporated into the multi-wave inversion formulation. The proposed framework integrates PP- and PS-wave responses within the inversion scheme, enabling simultaneous estimation of multiple petrophysical parameters while accounting for model uncertainty. Synthetic experiments using representative reservoir models demonstrate that introducing PS-wave information improves parameter discrimination and reduces inversion uncertainty compared with conventional PP-wave-only inversion. Application to pre-stack seismic data from China further confirms the capability of the proposed method for characterizing spatial variations in reservoir properties.