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A Visual Analytics Framework for Exploring Uncertainties in Reservoir Models

Geological uncertainty is an essential element that affects the prediction of hydrocarbon production. The standard approach to address the geological uncertainty is to generate a large number of random 3D geological models and then perform flow simulations for each of them. Such a brute-force approach is not efficient as the flow simulations are computationally costly and as a result, domain experts cannot afford running a large number of simulations. Therefore, it is critically important to be able to address the uncertainty using a few geological models, which can reasonably represent the overall uncertainty of the ensemble. Our goal is to design and develop a visual analytics framework to filter the geological models and to only select models that can potentially cover the uncertain space. This framework is based on the mutual information for the calculation of the distance between the models and clustering for the grouping of similar models. Interactive visualization tasks have also been designed to make the whole process more understandable. Finally, we evaluated our results by comparing with the existent brute force approach.