Computational advances in reservoir simulation have made possible the simulation of thousands of reservoir cases in a practical time frame. This opens a new avenue to reservoir-simulation studies, enabling exhaustive exploration of subsurface uncertainty and development/depletion options. However, analyzing the results of a large number of simulation cases remains challenging. This paper presents a new method that enables the efficient analysis of massive reservoir-simulation results by discovering interesting patterns of relationships among variables in large data sets. The method uses association-rule mining together with high-dimensional visualization.
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