Abstract
Clastic reservoir characterization typically starts with the modeling of the facies distribution and geometry. The architecture of the reservoir, governed by the facies geometry, is a major source of heterogeneity in such clastic systems. Seismic data potentially provide valuable information about the areal distribution of different facies. However, seismic data are available only at coarse vertical resolution, more closely representing interval average rock properties, whereas well log facies data more closely represent point rock properties. This scale difference or “volume support” difference between the seismic data and the facies data available along the wells makes direct integration difficult. A recently developed algorithm based on the concept of cokriging with block average data is woven into probability field simulation for building facies models. The seismic data at its coarse vertical scale, equivalent to attribute maps, can be fully accounted for without any implicit or explicit vertical duplication to match the fine vertical scale of geologic modeling. The cpu-speed advantage of probability field simulation is also retained. The algorithm is demonstrated on a clastic petroleum reservoir. The results are compared with those obtained from facies indicator simulation without integrating seismic data and those using seismic data duplicated along the vertical direction.
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