Abstract - Due to computational advances in reservoir simulation utilizing high performance computing, it is now possible to simulate more than thousands of reservoir simulation 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 still remains a challenging and overwhelming task. We propose a new method that enables the efficient analysis of massive reservoir simulation results, often consisting of thousands of cases, by discovering interesting patterns of relationships among variables in large datasets. The method uses a well-known data mining method, called association rule mining, together with a high-dimensional visualization technique. We demonstrate the capability of the proposed method by utilizing it to analyze the reservoir simulation results from the SAIGUP (Sensitivity Analysis of the Impact of Geological Uncertainty on Production) project, which is an interdisciplinary reservoir modeling project carried out by Manzocchi et al. earlier. To investigate the influence of geological features on oil recovery in shallow marine reservoirs, numerous reservoir models were built and flow-simulated in the SAIGUP project. In this paper, we analyze the simulation results from an ensemble of 9072 models, which cover all possible combinations of several structural and sedimentological parameters individually varied to describe geological uncertainty. To be able to analyze the simulation results from such exhaustive sampling from high-dimensional model parameter space, it is crucial to efficiently decompose complex interactions between model parameters and discover hidden impacts on flow response. By coupling the association rule mining algorithm and high-dimensional visualization, such interactions and impacts are rapidly extracted and visualized in such a way that engineers and geoscientists can interpret meaningful sensitivities "at a glance". This methodology provides a novel way to rapidly interpret flow response from large ensemble of reservoir models without being overwhelmed by massive data. It is also applicable for the analysis of production data from unconventional wells consisting of more than thousands.
SPE Annual Technical Conference and Exhibition, SPE-174774-MS, doi: 10.2118/174774-MS, 2015.