Modeling complex reservoir geometries with multiple-point statistics

Abstract

Large-scale reservoir architecture constitutes first-order reservoir heterogeneity and dietates to a large extent reservoir flow behavior. It also manifests geometric characteristics beyond the capability of traditional geostatistical models conditioned only on single-point and two-point statistics. Multiple-point statistics, as obtained by scanning a training image deemed representative of the actual reservoir, if reproduced properly provides stochastic models that better capture the essence of the heterogeneity. A “growth” algorithm, coupled with an optimization procedure, is proposed to reproduce target multiple-point histograms. The growth algorithm makes an analogy between geological accretion process and stochastic process and amounts to restricting the random path of sequential simulation at any given stage to a set of eligible nodes (immediately adjacent to a previously simulated node or sand grain). The proposed algorithm, combined with a multiple-grid approach, is shown to reproduce effectively the geometric essence of complex training images exhibiting long-range and curvilinear structures. Also, by avoiding a rigorous search for global minimum and accepting local minima, the proposed algorithm improves CPU time over traditional optimization procedures by several orders of magnitude. Average flow responses run on simulated realizations are shown to bracket correctly the reference responses of the training image.