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    Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms

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    Beshir_Thesis.pdf
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    Description:
    Thesis
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    Type
    Thesis
    Authors
    Aman, Beshir M.
    Advisors
    Hoteit, Ibrahim cc
    Committee members
    Al-Naffouri, Tareq Y. cc
    Sun, Shuyu cc
    Program
    Applied Mathematics and Computational Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2012-12
    Permanent link to this record
    http://hdl.handle.net/10754/255452
    
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    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.
    Citation
    Aman, B. M. (2012). Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms. KAUST Research Repository. https://doi.org/10.25781/KAUST-853VJ
    DOI
    10.25781/KAUST-853VJ
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-853VJ
    Scopus Count
    Collections
    Applied Mathematics and Computational Science Program; MS Theses; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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