Particle tracking proxies for prediction of CO₂ plume migration within a model selection framework

Geologic 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 sto...

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Main Author: Bhowmik, Sayantan
Format: Others
Language:en
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/2152/24795
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spelling ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-247952015-09-20T17:23:23ZParticle tracking proxies for prediction of CO₂ plume migration within a model selection frameworkBhowmik, SayantanModel selectionHistory matchingParticle trackingRandom walkerCO₂ sequestrationGeologic 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.text2014-06-24T16:51:11Z2014-052014-06-12May 20142014-06-24T16:51:11ZThesisapplication/pdfhttp://hdl.handle.net/2152/24795en
collection NDLTD
language en
format Others
sources NDLTD
topic Model selection
History matching
Particle tracking
Random walker
CO₂ sequestration
spellingShingle Model selection
History matching
Particle tracking
Random walker
CO₂ sequestration
Bhowmik, Sayantan
Particle tracking proxies for prediction of CO₂ plume migration within a model selection framework
description Geologic 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. === text
author Bhowmik, Sayantan
author_facet Bhowmik, Sayantan
author_sort Bhowmik, Sayantan
title Particle tracking proxies for prediction of CO₂ plume migration within a model selection framework
title_short Particle tracking proxies for prediction of CO₂ plume migration within a model selection framework
title_full Particle tracking proxies for prediction of CO₂ plume migration within a model selection framework
title_fullStr Particle tracking proxies for prediction of CO₂ plume migration within a model selection framework
title_full_unstemmed Particle tracking proxies for prediction of CO₂ plume migration within a model selection framework
title_sort particle tracking proxies for prediction of co₂ plume migration within a model selection framework
publishDate 2014
url http://hdl.handle.net/2152/24795
work_keys_str_mv AT bhowmiksayantan particletrackingproxiesforpredictionofco2plumemigrationwithinamodelselectionframework
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