History matching in reservoir simulation

dc.contributor.committeeChairGorell, Sheldon
dc.contributor.committeeMemberSheng, James J.
dc.contributor.committeeMemberEttehadtavakkol, Amin
dc.creatorJiang, Junzhe
dc.date.accessioned2018-11-09T16:17:02Z
dc.date.available2018-11-09T16:17:02Z
dc.date.created2018-05
dc.date.issued2018-05-07
dc.date.submittedMay 2018
dc.date.updated2018-11-09T16:17:02Z
dc.description.abstractHistory matching is a widely used reservoir simulation workflow. Its goal is to create models which reasonably match historical field injection and production data so future predictions can be made. Many methods have been developed in the past to try to solve this problem. One set of methods that have been those that involve ensemble data assimilation. An example is the Ensemble Kalman Filter (EnKF), which has been widely implemented. A key issue with ensemble methods is that spurious correlation can severely degrade the reliability of the estimation. In this paper, first, we introduce the history matching problem and review the existing theory and automatically method for history matching. Then, a new theory to relate Bayesian theory with least square theory is proposed. The spurious correlation problem in EnKF is analysis under the new theory. At last a new method to eliminate the spurious correlation in EnKF is introduced and used to verify the theoritical analysis.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2346/82095
dc.subjectReservoir simulation
dc.subjectHistory matching
dc.titleHistory matching in reservoir simulation
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentPetroleum Engineering
thesis.degree.disciplinePetroleum Engineering
thesis.degree.grantorTexas Tech University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Petroleum Engineering

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