Visual Analytics Framework for Exploring Uncertainty in Reservoir Models

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dc.contributor.advisorCosta Sousa, Mário
dc.contributor.advisorMaurer, Frank
dc.contributor.authorSahaf, Zahra
dc.contributor.committeememberWillett, Wesley
dc.contributor.committeememberAlim, Usman Raza
dc.contributor.committeememberEl-Sheimy, Naser
dc.contributor.committeememberMackay, Eric James
dc.date2018-11
dc.date.accessioned2018-09-13T14:45:23Z
dc.date.available2018-09-13T14:45:23Z
dc.date.issued2018-08-31
dc.description.abstractUncertainty 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.en_US
dc.identifier.citationSahaf, 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/32902en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/32902
dc.identifier.urihttp://hdl.handle.net/1880/107726
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity 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.
dc.subjectVisual Analytics
dc.subjectMutual Information
dc.subjectEnsembles
dc.subjectClustering
dc.subjectMulti-dimensional Scaling
dc.subjectGeological Realizations
dc.subject.classificationComputer Scienceen_US
dc.titleVisual Analytics Framework for Exploring Uncertainty in Reservoir Models
dc.typedoctoral thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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