Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms

  • Beshir M. Aman

Student thesis: Master's Thesis


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.
Date of AwardDec 2012
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Sciences and Engineering
SupervisorIbrahim Hoteit (Supervisor)


  • History Matching
  • Reservoir Modeling
  • Box-Cox
  • Bayesian Estimation
  • Anamorphosis

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