Bayesian joint inversion of seismic and electromagnetic data for reservoir litho-fluid facies including geophysical and petrophysical rock properties

A Bayesian approach is proposed to estimate litho-fluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Since computing this probability for all possible facies configurations may be impractical, a Markov Chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geological scenario and a well from a selected marine oil field. Two other wells from the same reservoir were used to gather prior distributions. Data from the well, calibration of the rock physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward modeling adaptations on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical datasets are then inverted for litho-fluid facies models under different prior inputs. Two challenging geoelectric scenarios were also tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating both geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also show potential for quantitative reservoir interpretations.