An improved stochastic inversion method for 3D elastic impedance under the prior constraints of random medium parameters

Abstract

Three-dimensional (3D) elastic impedance inversion is widely used in oil and gas exploration with its excellent identification of reservoirs and noise immunity. However, modifying the prior information directly in inversion will undoubtedly destroy the spatial autocorrelation structure of the elastic impedance. Maintaining the elastic impedance spatial autocorrelation structure in seismic inversion is of great significance for guiding the reservoir identification and prediction. The 3D prior information construction method is proposed under the constraint of random medium parameters. This method establishes an intrinsic connection between random medium parameters and elastic impedance based on seismic data and well data, which provides a theoretical basis for simulating prior information satisfying the spatial correlation of the subsurface medium. To ensure the invariance of the spatial structure term in the inversion process, the random term and the spatial structure term in the simulation process are separated by the gradual deformation method (GDM), and the priori constrained objective function is further constructed to reduce the inversion uncertainty. Numerical example shows that the relative error of the proposed simulation method is smaller than that of conventional sequential Gaussian simulation, and the details are more accurately depicted based on the priori inversion results in this study. In addition, the elastic impedance equation based on the p-wave modulus is derived by combining with the reservoir sensitive parameters in field data. The field examples document that this method can effectively perform high-resolution geophysical prediction of underground thin-layer reservoirs.