Bayesian Frequency-Dependent AVO Inversion Using an Improved Markov Chain Monte Carlo Method for Quantitative Gas Saturation Prediction in a Thin Layer

Abstract

One of the main objectives in the hydrocarbon reservoir characterization is determining rock and fluid properties that rely extensively on inference from seismic observations. In this letter, we present a novel Bayesian prestack inversion method using frequency-dependent amplitude versus offset (AVO) analysis with the goal to directly estimate gas saturation and porosity of a target thin reservoir zone. The proposed methodology is based on an improved Markov chain Monte Carlo (MCMC) sampling algorithm, which is computationally very coefficient due to its satisfactory acceptance probability and the convergence speed of Markov chains. Using a nonlinear rock physics model (RPM), properly calibrated for the investigating area, and a seismic forward operator based on the frequency-domain propagator matrix approach in the Bayesian inversion framework, we then evaluate the full posterior probability distribution of petrophysical parameters conditioned to seismic data and available prior information, using the MCMC algorithm in which we iteratively sample within the petrophysical property space. The proposed inversion approach is validated through applications to a synthetic reservoir model and the real seismic data from gas-bearing reservoirs with strong velocity dispersion.