Perpustakaan Digital ITB

Estimation of rock pore-?uid properties in subsurface reservoirs using seismic re?ection data is an important topic in exploration geophysics. Prestack seismic inversion is a key approach for reservoir characterization and ?uid discrimination. In this study, we propose a simultaneous estimation approach for discrete ?uid facies (i.e., gas, oil, water) and continuous ?uid indicators via a Bayesian seismic nonlinear inversion and the differential evolution Markov Chain Monte Carlo model. We derived one novel nonlinear exact PP-wave re?ection co-ef?cients equation characterized by Gassmann ?uid 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 ?uid facies is estimated via the posterior weights of each probability component directly, which can reduce the uncertainty and the accumulation errors of seismic ?uid 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 ef?ciently based on the simultaneous optimization of multiple Markov chains, which is helpful for the uncertainty assessment of the inversion results and ?uid discrimination. Synthetic examples are provided to demonstrate the feasibility and stability of the proposed method. Furthermore, one ?eld case of oil-gas exploration is presented to show the practicability of this method in reservoir ?uid discrimination.