| In most reservoir applications, flow simulation is controlled by the connectivity of extreme permeability values associated to specific patterns of geological heterogeneities, e.g., high permeability sand channels forming preferential flow paths. Such geological structures are generally curvilinear, hence their modeling requires multiple-point statistics involving jointly three or more points at a time, much beyond the traditional two-point variogram statistics.;Multiple-point statistics cannot be inferred solely from sparse subsurface data, they must be borrowed from training images depicting the expected patterns of geological heterogeneities. Several training images can be used, reflecting different scales of variability and styles of heterogeneities. The multiple-point statistics inferred from the training image(s) are exported to the reservoir model where they are anchored to the actual subsurface data, both hard and soft, in a sequential simulation mode.;The algorithm and code developed are tested for the simulation of a fluvial hydrocarbon reservoir with meandering channels. The methodology proposed appears to be practical (multiple-point statistics are scanned directly from training images), general (any type of geological heterogeneity can be considered), and fast enough to handle large 3D simulation grids. |
