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dc.contributor.advisorSrinivasan, Sanjayen
dc.contributor.advisorBryant, Steven L.en
dc.creatorBhowmik, Sayantanen
dc.date.accessioned2014-06-24T16:51:11Zen
dc.date.issued2014-05en
dc.date.submittedMay 2014en
dc.identifier.urihttp://hdl.handle.net/2152/24795en
dc.descriptiontexten
dc.description.abstractGeologic sequestration of CO₂ in deep saline aquifers has been studied extensively over the past two decades as a viable method of reducing anthropological carbon emissions. The monitoring and prediction of the movement of injected CO₂ is important for assessing containment of the gas within the storage volume, and taking corrective measures if required. Given the uncertainty in geologic architecture of the storage aquifers, it is reasonable to depict our prior knowledge of the project area using a vast suite of aquifer models. Simulating such a large number of models using traditional numerical flow simulators to evaluate uncertainty is computationally expensive. A novel stochastic workflow for characterizing the plume migration, based on a model selection algorithm developed by Mantilla in 2011, has been implemented. The approach includes four main steps: (1) assessing the connectivity/dynamic characteristics of a large prior ensemble of models using proxies; (2) model clustering using the principle component analysis or multidimensional scaling coupled with the k-mean clustering approach; (3) model selection using the Bayes' rule on the reduced model space, and (4) model expansion using an ensemble pattern-based matching scheme. In this dissertation, two proxies have been developed based on particle tracking in order to assess the flow connectivity of models in the initial set. The proxies serve as fast approximations of finite-difference flow simulation models, and are meant to provide rapid estimations of connectivity of the aquifer models. Modifications have also been implemented within the model selection workflow to accommodate the particular problem of application to a carbon sequestration project. The applicability of the proxies is tested both on synthetic models and real field case studies. It is demonstrated that the first proxy captures areal migration to a reasonable extent, while failing to adequately capture vertical buoyancy-driven flow of CO₂. This limitation of the proxy is addressed in the second proxy, and its applicability is demonstrated not only in capturing horizontal migration but also in buoyancy-driven flow. Both proxies are tested both as standalone approximations of numerical simulation and within the larger model selection framework.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectModel selectionen
dc.subjectHistory matchingen
dc.subjectParticle trackingen
dc.subjectRandom walkeren
dc.subjectCO₂ sequestrationen
dc.titleParticle tracking proxies for prediction of CO₂ plume migration within a model selection frameworken
dc.typeThesisen
dc.date.updated2014-06-24T16:51:11Zen
dc.description.departmentPetroleum and Geosystems Engineeringen
thesis.degree.departmentPetroleum and Geosystems Engineeringen
thesis.degree.disciplinePetroleum Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


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