1887

Abstract

Summary

Reservoir characterization is one of the most important works for predicting future performances and making development plan. Ensemble Kalman filter (EnKF) uses recursive update whenever observed data are obtained. Ensemble smoother (ES) does not require multiple updates because it assimilates all available data at a time. ES has advantage on simulation time over EnKF, but it sometimes gives instable results on history matching. In this paper, we propose the concept of selective use of measurement data for ES. We have compared two cases. One case uses all data which are oil production and water cut from 20 to 900 days. The other case uses data selectively such as only oil production before water breakthrough and water cut after water breakthrough. For 2D channelized reservoirs, ES with all data case shows overshooting and filter divergence problems. However, ES with selective data case controls the two problems and characterizes main channel distribution properly. It keeps bimodal distribution of the channel fields better than EnKF. Furthermore, ES with selective data provides proper estimation of reservoir performances with uncertainty and enables us to make a rational decision. It requires low simulation cost which is only 2.2% of that of EnKF for 45 times updates.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.20141513
2014-06-16
2024-03-29
Loading full text...

Full text loading...

References

  1. Aanonsen, S.I., Nævdal, G. Oliver, D.S. Reynolds, A.C. and Vallès, B.
    [2009] The ensemble Kalman filter in reservoir engineering--a review. SPE Journal, 14(3), 393–412.
    [Google Scholar]
  2. Chen, Y. and Oilver, D.S.
    [2012] Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother. Mathematical Geosciences, 44(1), 1–26.
    [Google Scholar]
  3. [2013] History Matching Of The Norne Full Field Model Using An Iterative Ensemble Smoother. EAGE Annual Conference & Exhibition incorporating SPE Europec, London, United Kingdom, June 10–13 2013, SPE 164902.
    [Google Scholar]
  4. Emerick, A.A. and Reynolds, A.C.
    [2013a] Ensemble smoother with multiple data assimilation. Computers & Geosciences, 55, 3–15.
    [Google Scholar]
  5. [2013b] History-Matching Production and Seismic Data in a Real Field Case Using the Ensemble Smoother With Multiple Data Assimilation. SPE Reservoir Simulation Symposium, The Woodlands, Texas, February 18–20 2013, SPE 163675.
    [Google Scholar]
  6. Gervais, V., Le Ravalec, M. Heidari, L. and Schaaf, T.
    [2012] History-matching with ensemble-based methods: Application to an underground gas storage site. SPE Europec/EAGE Annual Conference, Copenhagen, Denmark, June 4–7 2012, SPE 154475.
    [Google Scholar]
  7. Jafarpour, B. and McLaughlin, D.B.
    [2009a] Reservoir Characterization With the Discrete Cosine Transform. SPE Journal, 14(1), 182–201.
    [Google Scholar]
  8. [2009b] Estimating channelized-reservoir permeabilities with the ensemble Kalman filter: The importance of ensemble design. SPE Journal, 14(2), 374–388.
    [Google Scholar]
  9. Jeong, H., Ki, S. and Choe, J.
    [2010] Reservoir characterization from insufficient static data using gradual deformation method with ensemble Kalman filter. Energy Sources, Part A, 32(10), 942–951.
    [Google Scholar]
  10. Jung, S. and Choe, J.
    [2012] Reservoir characterization using a streamline-assisted ensemble Kalman filter with covariance localization. Energy Exploration & Exploitation, 30(4), 645–660.
    [Google Scholar]
  11. Lee, K., Jeong, H., Jung, S. and Choe, J.
    [2013] Improvement of ensemble smoother with clustering covariance for channelized reservoirs. Energy Exploration and Exploitation, 31(5), 713–726.
    [Google Scholar]
  12. Nævdal, G. and Vefring, E.H.
    [2002] Near-well reservoir monitoring through ensemble Kalman filter. SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, April 13–17 2002, SPE 75235.
    [Google Scholar]
  13. Oliver, D.S. and Chen, Y.
    [2011] Recent progress on reservoir history matching: A review. Computational Geosciences, 15(1), 185–221.
    [Google Scholar]
  14. Park, K. and Choe, J.
    [2006] Use of ensemble Kalman filter to 3-dimenstional reservoir characterization during waterflooding. SPE Europe/EAGE Annual Conference and Exhibition, Vienna, Austria, June 12–15 2006, SPE 100178.
    [Google Scholar]
  15. Shin, Y., Jeong, H. and Choe, J.
    [2010] Reservoir characterization using an EnKF and a non-parametric approach for highly non-Gaussian permeability fields. Energy Sources, Part A, 32(16), 1,569–1,578.
    [Google Scholar]
  16. Skjervheim, J.-A., Evensen, G. Hove, J. and Vabø, J.G.
    [2011] An ensemble smoother for assisted history matching. SPE Reservoir Simulation Symposium, The Woodlands, Texas, February 21–23 2011, SPE 141929.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141513
Loading
/content/papers/10.3997/2214-4609.20141513
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