Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms

Handle URI:
http://hdl.handle.net/10754/255452
Title:
Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms
Authors:
Aman, Beshir M.
Abstract:
This work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.
Advisors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Committee Member:
Al-Naffouri, Tareq Y.; Sun, Shuyu ( 0000-0002-3078-864X )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Applied Mathematics and Computational Science
Issue Date:
Dec-2012
Type:
Thesis
Appears in Collections:
Applied Mathematics and Computational Science Program; Theses; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorHoteit, Ibrahimen
dc.contributor.authorAman, Beshir M.en
dc.date.accessioned2012-12-12T06:36:22Z-
dc.date.available2012-12-12T06:36:22Z-
dc.date.issued2012-12en
dc.identifier.urihttp://hdl.handle.net/10754/255452en
dc.description.abstractThis work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.en
dc.language.isoenen
dc.subjectHistory Matchingen
dc.subjectReservoir Modelingen
dc.subjectBox-Coxen
dc.subjectBayesian Estimationen
dc.subjectAnamorphosisen
dc.titleReservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transformsen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberAl-Naffouri, Tareq Y.en
dc.contributor.committeememberSun, Shuyuen
thesis.degree.disciplineApplied Mathematics and Computational Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id113174en
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