In this paper a pattern-based stochastic simulation method is presented to model 2D and 3D heterogeneous reservoirs. The method uses a spatial template to extract information in the form of patterns from a training image; i.e., a prior representation of the reservoir. The patterns are grouped into a pattern database and are classified to reduce the computational time. The classification algorithm proposed consists of reducing the dimension of spatial patterns and performing a class centers selection in the lower dimensional space. Conditional and unconditional simulations of reservoir channels are presented. The method is compared with the Filtersim method. Results show that the proposed method is better at producing the multipoint configurations and the main characteristics of the reference images including continuity of channels, and it is very stable with respect to the number of classes and spatial templates used in the simulations.


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