Abstract

This paper presents a novel Bayesian inversion technique combining rock physics and multiple-point geostatistics (MPG) to predict lithofacies and saturations from seismic data. Rock-physics principles are incorporated at the beginning, defining the links between reservoir properties (e.g., lithology, saturation) and physical properties (e.g., compressibility, electrical conductivity). MPG is used to define and explore the space of solutions, in terms of geologically plausible spatial patterns of lithofacies. The proposed method can be applied in its current implementation to any inverse problem that can be approximated as a series of 1D forward-modeling operators. The method can be extended to satisfy multiple physical constraints simultaneously; in other words, the solutions can be conditioned with different types of geophysical data. Results of two synthetic tests and a real data application demonstrate the validity and applicability of the proposed inversion technique.