1887

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.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201413033
2015-06-01
2024-04-20
Loading full text...

Full text loading...

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]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201413033
Loading
/content/papers/10.3997/2214-4609.201413033
Loading

Data & Media loading...

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