Bayesian nonlinear rock-physics inversion for reservoirs with complex pore structure based on elastic impedance

Abstract

Carbonate reservoirs have complex pore structures, which significantly alter the elastic properties and seismic responses, and affect the accuracy of physical parameter estimations. However, the existing rock-physics inversion methods are mainly used for clastic rocks, and their objectivities are to invert porosity and water saturation. In addition, the most commonly used data sets are often based on elastic parameters and the most widely used algorithm is mainly the linear approximation. Still, there is a lack of simultaneous inversion method for characterizing pore structure and physical parameters of carbonate reservoirs. To solve these problems, a new elastic-impedance-based nonlinear simultaneous inversion method under the framework of Bayesian theory is first proposed. It integrates the differential effective medium model of multiple-porosity rock, Gasmann equation, AVO theory, Bayesian theory and nonlinear inversion algorithm together to realize the simultaneous quantification of pore structure and physical parameters for complex porous reservoirs. The application of real seismic data shows that our method can accurately predict pore aspect ratio, reservoir porosity and water saturation directly from pre-stack data, and capture the pore structure characteristics of effective reservoirs. Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2021 in Denver, Colorado.