Direct estimation of discrete fluid facies and fluid indicators via a Bayesian Seismic Probabilistic Inversion and a novel exact PP-wave reflection coefficient

Abstract

Estimation of rock pore-fluid properties in subsurface reservoirs using seismic reflection data is an important topic in exploration geophysics. Prestack seismic inversion is a key approach for reservoir characterization and fluid discrimination. In this study, we propose a simultaneous estimation approach for discrete fluid facies (i.e., gas, oil, water) and continuous fluid indicators via a Bayesian seismic nonlinear inversion and the differential evolution Markov Chain Monte Carlo model. We derived one novel nonlinear exact PP-wave reflection coefficients equation characterized by Gassmann fluid term, shear modulus and density and the equation is more accurate and suitable for large incident angles. Besides, a mixture probability model is utilized as the prior probability density function (PDF) of the model parameters in the Bayesian seismic probabilistic inversion. Furthermore, the differential evolution Markov Chain Monte Carlo (DE-MCMC) model is developed to optimize the mixture posterior PDF in Bayesian seismic inference. The discrete fluid facies is estimated via the posterior weights of each probability component directly, which can reduce the uncertainty and the accumulation errors of seismic fluid discrimination. The main advantage of the DE-MCMC seismic probabilistic inversion is that it can simulate the posterior probability density distributions of the model parameters efficiently based on the simultaneous optimization of multiple Markov chains, which is helpful for the uncertainty assessment of the inversion results and fluid discrimination. Synthetic examples are provided to demonstrate the feasibility and stability of the proposed method. Furthermore, one field case of oil-gas exploration is presented to show the practicability of this method in reservoir fluid discrimination.