Fast Geostatistical Stochastic Inversion in a Stratigraphic Grid

We have developed an efficient stochastic AVA inversion technique that works directly in a fine-scale stratigraphic grid, and is conditioned by well data and multiple seismic angle stacks. We use a Bayesian framework and a linearized, weak contrast approximation of the Zoeppritz equation to construct a joint log-Gaussian posterior distribution for Pand S-wave impedances. We apply a Sequential Gaussian Simulation algorithm to sample the posterior PDF. We perform a trace-bytrace decomposition of the global posterior into local posterior distributions, conditioned by previously simulated traces. Trace-by-trace sampling of the local PDFs generates multiple, high-resolution realizations of the elastic properties. The new sequential algorithm has been implemented to take full advantage of parallel architectures and scales approximately linearly with the number of CPUs. The technique has been successfully tested using real data and a large layered model containing more than 30×106 grid-cells.