Abstract
Uncertainty in reservoir geological properties has a major impact on reservoir modeling and operations decision-making, and it leads to the generation of a large set of stochastic models, called geological realizations. Flow simulations are then used to quantify the uncertainty in predicting the hydrocarbon production. However, reservoir flow simulation is a computationally intensive task. In a recent paper, we proposed a visual based analytical framework to select a few models from a large ensemble of geological realizations. In this paper, we extend our prior framework by introducing the region of interest concept, that helps to perform the entire analysis only based on a specific portion of the reservoir. The effectiveness of region of interest selection techniques is shown on two case studies. We also performed a complete user study with the engineers. User feedback suggests that usefulness, usability and visual interactivity are the key strengths of our approach.
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Sahaf, Z., Mota, R.C., Hamdi, H., Sousa, M.C., Maurer, F. (2019). An ROI Visual-Analytical Approach for Exploring Uncertainty in Reservoir Models. In: Bechmann, D., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2018. Communications in Computer and Information Science, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-26756-8_6
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