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dc.contributor.advisorDawson, Clinton N.
dc.creatorEstes, Samuel Clay
dc.date.accessioned2020-05-22T21:21:17Z
dc.date.available2020-05-22T21:21:17Z
dc.date.created2019-12
dc.date.issued2020-03-27
dc.date.submittedDecember 2019
dc.identifier.urihttps://hdl.handle.net/2152/81351
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/8359
dc.description.abstractReservoir simulation is critically important for optimally managing petroleum reservoirs. Often, many of the parameters of the model are unknown and cannot be measured directly. These parameters must then be inferred from production data at the wells. This is an inverse problem which can be formulated within a Bayesian framework to integrate prior knowledge with observational data. Markov Chain Monte Carlo (MCMC) methods are commonly used to solve Bayesian inverse problems by generating a set of samples which can be used to characterize the posterior distribution. In this work, we present a novel MCMC algorithm which uses a sequential transition kernel designed to exploit the redundancy which is often present in time series data from reservoirs. This method can be used to efficiently generate samples from the Bayesian posterior for time-dependent models. While this method is general and could be useful for many different models, we focus here on reservoir models with faults. A fault is a heterogeneity characterized by a sharp change in the permeability of the reservoir over a region with very small width relative to its length and the overall size of the reservoir domain [1]. It is often convenient to model faults as lower dimensional surfaces which act as barriers to the flow. The transmissibility multiplier is a parameter which characterizes the extent to which fluid can flow across a fault. We consider a Bayesian inverse problem in which we wish to infer fault transmissibilities from measurements of pressure at wells using a two-phase flow model. We demonstrate how the sequential MCMC algorithm presented here can be more efficient than a standard Metropolis-Hastings MCMC approach for this inverse problem. We use integrated autocorrelation times along with mean-squared jump distances to determine the performance of each method for the inverse problem
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectReservoir simulation
dc.subjectUncertainty quantification
dc.subjectMCMC
dc.subjectBayesian inverse problems
dc.titleUncertainty quantification in reservoirs with faults using a sequential approach
dc.typeThesis
dc.date.updated2020-05-22T21:21:18Z
dc.contributor.committeeMemberGhattas, Omar
dc.contributor.committeeMemberBui-Thanh, Tan
dc.contributor.committeeMemberBiros, George
dc.contributor.committeeMemberButler, Troy
dc.description.departmentComputational Science, Engineering, and Mathematics
thesis.degree.departmentComputational Science, Engineering, and Mathematics
thesis.degree.disciplineComputational Science, Engineering, and Mathematics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
dc.creator.orcid0000-0002-7973-3017
dc.type.materialtext


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