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Current multiple-point based simulations implementations generategeostatistical models at the scale of the training image; there is anassumption that the categories are exclusive at smaller scales. The goal ofthis paper is to generate models with multiple-point statistics (MPS) at ahigher resolution than that of the available training image. This paperaddresses model resolution enhancement by studying the scale-dependence ofspatial structure in MPS based models -- extrapolating the smaller scale MPSfrom the larger scale MPS, and (2) rescaling the training image directly to thesmaller scale. The first approach investigates the MPS probabilities. A numberof challenges in characterizing smaller scale variability using high-orderstatistics are documented. The paper concludes by advocating the directrescaling of the training image to generate models at higher resolution.

Geostatistical Model Resolution Enhancement in the Context of Multiple-Point Statistics