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Abstract

Summary

Consistent integration of measured static and dynamic data and a proper description of the model uncertainty is essential for the predictability of a reservoir model. In a traditional base-case approach, the static data conditioning (geo-modelling) is often decoupled from the dynamic data conditioning (history matching) and the model uncertainty is completely ignored – which often lead to poor model predictability. In this paper we have applied an ensemble Kalman based history matching technique to consistently integrate both static and dynamic data, while capturing the modelling uncertainty on two gas condensate fields offshore Norway. We demonstrate the effectiveness and discuss practical aspects of the method when applying it on real field data.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201413033
2015-06-01
2025-03-20

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References

  1. Emerick, A.A. and Reynolds, A.C.
    [2013] Ensemble Smoother with Multiple Data Assimilation. Computers & Geosciences, 3–15.
     [Google Scholar]
  2. Evensen, G.
    [2009] Data Assimilation – The Ensemble Kalman Filter. Springer.
     [Google Scholar]
  3. Nævdal, G., Mannseth, T. and Vefring, E.H.
    [2002] Near-Well Reservoir Monitoring Through Ensemble Kalman Filter. SPE/DOE Improved Oil Recovery Symposium. Tulsa, Oklahoma: SPE.
     [Google Scholar]
  4. Oliver, D.S., Reynolds, A.C. and Liu, N.
    [2008] Inverse Theory for Petroleum Reservoir Characterization and History Matching. Cambridge University Press.
     [Google Scholar]
  5. Oliver, D. and Chen, Y.
    [2011] Recent progress on reservoir history matching: a review. Computational Geosciences, 185–211.
     [Google Scholar]
  6. Resoptima
    Resoptima [2015] http://www.resoptima.com/overview/resx/. Retrieved from Resoptima – ResX.
     [Google Scholar]
/content/papers/10.3997/2214-4609.201413033
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Improved History Matching and Uncertainty Quantification for Gas Condensate Fields Using an Ensemble Based Approach
Conference Proceedings 2015, 1 (2015); https://doi.org/10.3997/2214-4609.201413033
/content/papers/10.3997/2214-4609.201413033
/content/papers/10.3997/2214-4609.201413033
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