1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery
  5. Conference paper

Abstract

Summary

Ensemble-based history matching methods are among the state-of-the-art approaches to reservoir characterization. In practice, however, they often suffer from ensemble collapse, a phenomenon that deteriorates history matching performance. To prevent ensemble collapse, it is customary to equip an ensemble history matching algorithm with a certain localization scheme.

In a previous study (SPE Journal, SPE-185936-PA), we propose an adaptive localization scheme that exploits the correlations between model variables and simulated observations for localization. Correlation-based adaptive localization not only overcomes some longstanding issues arising in conventional distance-based localization, but also is more convenient to implement and use in real field case studies (SPE conference paper, SPE-191305-MS).

The aforementioned correlation-based localization is subject to two problems. One is that, it requires to run a relatively large ensemble in order to achieve decent performance in an automatic manner, which becomes computationally expensive in large-scale problems. As a result, certain empirical tuning factors are introduced in the previous work to reduce the computational costs. The other problem is that, the way used to compute the tapering coefficients in the previous work may induce dis-continuities, and neglect the information of certain still-influential observations for model updates.

The main objective of this work is to improve the efficiency and accuracy of correlation-based adaptive localization proposed in the previous work, making it run in an automatic manner but without incurring substantial extra computational costs. To this end, we introduce two enhancements to address the aforementioned problems. We apply the resulting automatic and adaptive correlation-based localization with the two enhancements to a 2D and a 3D case studies, and show that it leads to better history matching performance than that is achieved in the previous work.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201802278
2018-09-03
2024-04-19

  • From This Site

    /content/papers/10.3997/2214-4609.201802278
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aanonsen, S., Nævdal, G., Oliver, D., Reynolds, A. and Vallès, B.
    [2009] The Ensemble Kalman Filter in Reservoir Engineering: a Review.SPE Journal, 14, 393–412. SPE-117274-PA.
     [Google Scholar]
  2. Anderson, J.L.
    [2012] Localization and sampling error correction in ensemble Kalman filter data assimilation.Monthly Weather Review, 140(7), 2359–2371.
     [Google Scholar]
  3. [2016] Reducing correlation sampling error in ensemble Kalman filter data assimilation.Monthly Weather Review, 144(3), 913–925.
     [Google Scholar]
  4. Anderson, J.L. and Anderson, S.L.
    [1999] A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts.Mon. Wea. Rev, 127, 2741–2758.
     [Google Scholar]
  5. Arroyo, E., Devegowda, D., Datta-Gupta, A. and Choe, J.
    [2008] Streamline-assisted ensemble Kalman filter for rapid and continuous reservoir model updating.SPE Reservoir Evaluation & Engineering, 11, 1046–1060. SPE-104255-PA.
     [Google Scholar]
  6. Chen, Y. and Oliver, D.S.
    [2010] Cross-covariances and localization for EnKF in multiphase flow data assimilation.Computational Geosciences, 14, 579–601.
     [Google Scholar]
  7. [2017] Localization and regularization for iterative ensemble smoothers.Computational Geosciences, 21, 13–30.
     [Google Scholar]
  8. Donoho, D.L. and Johnstone, I.M.
    [1995] Adapting to unknown smoothness via wavelet shrinkage.Journal of the American Statistical Association, 90, 1200–1224.
     [Google Scholar]
  9. Donoho, D.L. and Johnstone, J.M.
    [1994] Ideal spatial adaptation by wavelet shrinkage.Biometrika, 81, 425–455.
     [Google Scholar]
  10. Emerick, A. and Reynolds, A.
    [2011] Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications.Computational Geosciences, 15, 251–269.
     [Google Scholar]
  11. Evensen, G.
    [2009] Data Assimilation: The Ensemble Kalman Filter.Springer Science & Business Media.
     [Google Scholar]
  12. Gaspari, G. and Cohn, S.E.
    [1999] Construction of correlation functions in two and three dimensions.Quart. J. Roy. Meteor. Soc., 125, 723 – 757.
     [Google Scholar]
  13. Hamill, T.M., Whitaker, J.S., Anderson, J.L. and Snyder, C.
    [2009] Comments on "Sigma-Point Kalman Filter Data Assimilation Methods for Strongly Nonlinear Systems".J. Atmos. Sci., 66, 3498–3500.
     [Google Scholar]
  14. Hamill, T.M., Whitaker, J.S. and Snyder, C.
    [2001] Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter.Mon. Wea. Rev., 129, 2776–2790.
     [Google Scholar]
  15. Houtekamer, P.L. and Mitchell, H.L.
    [1998] Data assimilation using an ensemble Kalman filter technique.Mon. Wea. Rev., 126, 796–811.
     [Google Scholar]
  16. Lorentzen, R., Luo, X., Bhakta, T. and Valestrand, R.
    [2018] History matching Norne reservoir and petroelastic models using seismic impedance with correlated noise.Under review.
     [Google Scholar]
  17. Luo, X. and Bhakta, T.
    [2017] Estimating observation error covariance matrix of seismic data from a perspective of image denoising.Computational Geosciences, 21, 205–222.
     [Google Scholar]
  18. Luo, X., Bhakta, T., Jakobsen, M. and Nævdal, G.
    [2016] An ensemble 4D seismic history matching framework with wavelet multiresolution analysis – A 3D benchmark case study.15th European Conference on the Mathematics of Oil Recovery (ECMOR), Amsterdam, Netherlands, 29 August - 01 September.
     [Google Scholar]
  19. Luo, X., Bhakta, T., Jakobsen, M. and Naevdal, G.
    [2017] An ensemble 4D-seismic history-matching framework with sparse representation based on wavelet multiresolution analysis.SPE Journal, 22, 985 – 1010. SPE-180025-PA.
     [Google Scholar]
  20. Luo, X., Bhakta, T. and Nædal, G.
    [2018a] Correlation-based adaptive localization with applications to ensemble-based 4D seismic history matching.SPE Journal, 23, 396 – 427. SPE-185936-PA.
     [Google Scholar]
  21. Luo, X. and Hoteit, I.
    [2011] Robust ensemble filtering and its relation to covariance inflation in the ensemble Kalman filter.Mon. Wea. Rev., 139, 3938–3953.
     [Google Scholar]
  22. [2013] Covariance inflation in the ensemble Kalman filter: a residual nudging perspective and some implications.Mon. Wea. Rev., 141, 3360–3368.
     [Google Scholar]
  23. [2014a] Efficient particle filtering through residual nudging.Quart. J. Roy. Meteor. Soc., 140, 557–572.
     [Google Scholar]
  24. [2014b] Ensemble Kalman filtering with residual nudging: an extension to the state estimation problems with nonlinear observations.Mon. Wea. Rev., 142, 3696–3712.
     [Google Scholar]
  25. Luo, X., Lorentzen, R., Valestrand, R. and Evensen, G.
    [2018b] Correlation-based adaptive localization for ensemble-based history matching: Applied to the Norne field case study.SPE Norway One Day Seminar. SPE-191305-MS.
     [Google Scholar]
  26. Luo, X., Stordal, A., Lorentzen, R. and Nævdal, G.
    [2015] Iterative ensemble smoother as an approximate solution to a regularized minimum-average-cost problem: theory and applications.SPE Journal, 20, 962–982. SPE-176023-PA.
     [Google Scholar]
  27. Mallat, S.
    [2008] A wavelet tour of signal processing.Academic press.
     [Google Scholar]
  28. Raniolo, S., Dovera, L., Cominelli, A., Callegaro, C. and Masserano, F.
    [2013] History match and polymer injection optimization in a mature field using the ensemble Kalman filter. In: IOR 2013-17th European Symposium on Improved Oil Recovery.
     [Google Scholar]
  29. Zhang, Y. and Oliver, D.S.
    [2010] Improving the ensemble estimate of the Kalman gain by bootstrap sampling.Mathematical Geosciences, 42, 327–345.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201802278
Loading
Towards Automatic And Adaptive Localization For Ensemble-Based History Matching
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802278
/content/papers/10.3997/2214-4609.201802278
/content/papers/10.3997/2214-4609.201802278
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error