Bayesian inversion for effective pore-fluid bulk modulus based on fluid-matrix decoupled amplitude variation with offset approximation

Abstract

Fluid indicators estimated from seismic data play important roles in reservoir characterization and prospect identification. Traditionally, there are a variety of fluid indicators proposed, but they are very likely to provide ambiguous results for fluid identification due to the fact that their sensitivity is dependent upon the mixed effect of pore fluid and rock porosity. To raise the sensitivity of fluid indication, we used the effective pore-fluid bulk modulus as a fluid indicator. Starting with the poroelastic amplitude variation with offset (AVO) theory and the corresponding rock-physics model with the homogeneous sorting trend, we derived a new AVO approximation that allowed us to estimate the effective pore-fluid bulk modulus in a direct fashion. The inversion for the fluid indicator is formulated in Bayesian framework with the Cauchy distribution as a prior constraint. We tested the method on synthetic data and analyzed the feasibility and stability of the inversion. A field data example shows that the effective pore-fluid bulk modulus can reduce the ambiguity caused by the rock porosity and improve the quality of fluid discrimination in a clastic reservoir. Further research needs to be done on the reservoirs that do not fit the rock-physics model without a sorting trend.