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Parameter estimation of subsurface flow models using iterative regularized ensemble Kalman filter

  • A. H. ELSheikh
  • C. C. Pain
  • F. Fang
  • J. L. M. A. Gomes
  • I. M. Navon
Original Paper

Abstract

A new parameter estimation algorithm based on ensemble Kalman filter (EnKF) is developed. The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles. The inverse problem is formulated as a sequential data integration problem. Gaussian process regression is used to integrate the prior knowledge (static data). The search space is further parameterized using Karhunen–Loève expansion to build a set of basis functions that spans the search space. Optimal weights of the reduced basis functions are estimated by an iterative regularized EnKF algorithm. The filter is converted to an optimization algorithm by using a pseudo time-stepping technique such that the model output matches the time dependent data. The EnKF Kalman gain matrix is regularized using truncated SVD to filter out noisy correlations. Numerical results show that the proposed algorithm is a promising approach for parameter estimation of subsurface flow models.

Keywords

ensemble Kalman filter inverse problems regularization Gaussian process regression Karhunen–Loève expansion 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their insightful and constructive comments which helped enhance this manuscript. Dr. A. H. Elsheikh and Dr. Jefferson L. M. A. Gomes carried this work as part of activities of the Qatar Carbonates and Carbon Storage Research Centre (QCCSRC). They gratefully acknowledge the funding of QCCSRC provided jointly by Qatar Petroleum, Shell, and the Qatar Science and Technology Park. Professor I. M. Navon acknowledges the support of NSF/CMG grant ATM-0931198.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • A. H. ELSheikh
    • 1
  • C. C. Pain
    • 1
  • F. Fang
    • 1
  • J. L. M. A. Gomes
    • 1
  • I. M. Navon
    • 2
  1. 1.Department of Earth Science and EngineeringImperial College LondonLondonUK
  2. 2.Department of Scientific ComputingFlorida State UniversityFLUSA

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