Costa Sousa, MárioMaurer, FrankSahaf, Zahra2018-09-132018-09-132018-08-31Sahaf, Z. (2018). Visual Analytics Framework for Exploring Uncertainty in Reservoir Models (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32902http://hdl.handle.net/1880/107726Uncertainty is related to poor knowledge of a phenomenon. In particular, 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 bruteforce 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. In this framework, a new block based approach is proposed using mutual information to calculate pair-wise distances between the 3D geological models. The calculated distances are then used within a clustering algorithm to group similar models. Cluster centers are the few representative models of the entire set of models that cover the uncertainty range. The whole framework is complimented by visual interactive tasks to be able to incorporate user's knowledge into the process and make the entire process more understandable. Finally, the framework is applied on many different case studies, and the results are evaluated by comparing with the existent brute force approach. In addition to that, the actual framework is evaluated in formal user study sessions with the domain experts in reservoir engineering and geoscience domain.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Visual AnalyticsMutual InformationEnsemblesClusteringMulti-dimensional ScalingGeological RealizationsComputer Sciencedoctoral thesis10.11575/PRISM/32902