Preselection of Reservoir Models From a Geostatistics-Based Petrophysical Seismic Inversion

Abstract

Summary A new geostatistics-based seismic-inversion method is introduced in this paper for determining reservoir models consistent with base seismic information. The proposed methodology entails two steps, with only the second being examined in this paper. First, cubes of acoustic velocities or impedances are derived from seismic inversion. Second, these data are incorporated into a matching process to identify reservoir models leading to acoustic responses close to the reference acoustic data. The parameterization of the facies and petrophysical properties populating the reservoir models is based upon the gradual-deformation method (GDM), which relies on geostatistical concepts. This particular feature makes it possible to change the spatial distribution of the property of interest from a few parameters while preserving its spatial variability. The matching process is driven from a global optimization algorithm known as the particle-swarm optimization (PSO). Such a global approach is reasonable for the problem considered because the forward modeling is very fast. A variant of the PSO algorithm is implemented to take advantage of the GDM properties. This approach yields reservoir models that honor the seismic data better than those derived from stochastic simulation only with seismic used as a secondary variable. A numerical experiment is then presented to stress the applicability of the proposed matching methodology: The GDM-based PSO approach is used to identify facies reservoir models and water/oil contact consistent with some reference acoustic P-wave impedances.