Application of Grid-Free Geostatistical Simulation to a Large Oil-Sands Reservoir

Abstract

Summary Geostatistical simulation is performed for reservoir characterization to depict local variability in the modeled properties. The conventional simulation methods are implemented in a grid-dependent manner that makes regridding of realizations, refinement of existing grids, and the simulation on irregular grids challenging. The grid-free-simulation (GFS) method has been recently developed for flexible reservoir characterization. The geostatistical realizations of a reservoir are expressed as an analytical function of the coordinates of the simulation locations and, thus, are infinitely resolvable. The resulting model is conditioned to primary scattered point-scale hard data and secondary exhaustively sampled block-scale soft data. The former data are sampled along wells, whereas the latter data are from seismic surveys. The GFS methodology is applied to the Firebag oil-sands thermal project operated in northern Alberta, Canada. The conditioning data are point-scale core measurements, log observations, and block-scale acoustic impedance (AI). The models of correlated porosity, permeability, and water saturation attributes are constructed on three different grids by facies and are consistent with each other. These models are intended for resource estimation, reserves estimation, and subsequent-flow simulation, respectively. The modeling results of the grid-independent simulation method are promising for industrial application to petroleum reservoir characterization.