MRE-based Bayesian Inversion of Seismic And EM Data For Identification of Reservoir Parameters

A stochastic joint inversion approach for estimating reservoir fluid saturations and porosity is proposed. The approach couples seismic amplitude versus angle (AVA) and marine controlled source electromagnetic (CSEM) forward models into a Bayesian formalism, which allows for integration between complementary information. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of Minimum Relative Entropy (MRE) is employed. Instead of single-valued estimate provided by deterministic methods, the approach gives a probability distribution for any unknown parameter of interest, such as reservoir fluid saturations or porosity at different locations. The distributions means, modes, and confidence intervals can be calculated, providing a more complete understanding of the uncertainty in the parameter estimates. The approach is tested using two case studies: one is a synthetic case, and the other uses data from the North Sea Troll field. Results show that joint inversion using seismic and EM data gives better estimates of reservoir parameters than estimates from either geophysical data set used in isolation.