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Abstract

Summary

History Matching is a key step in all reservoir engineering study. This inverse problem is known to provide not unique answers. To find out the optimal global solution, a global optimizer is required as gradient methods fails in finding the global solution often being trapped in local minimum. But using a global optimizer, thousands of reservoir simulation runs are required which is unpractical. It is here that comes in our innovation approach: replace the reservoir simulator by a proxy. This proxy is build using an Artificial Neural Network. It is the most efficient approach we found due to the no linear behaviour of the output again the parameters. Of course, several ANN are possible (number of layer and number of neurons per layer…). A methodology to find out the most predictive ANN is proposed. Coming back to the global optimizer, the paper will emphasize the advantages of using the covariance matrix adaptation evolution strategy (CMA-ES) through practical cases.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201602403
2016-12-05
2024-04-18

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References

  1. Bruyelle, J. and GuérillotD.
    [2014] Neural Networks and their Derivatives for History Matching and Reservoir Optimization Problems. Computational Geosciences. Volume 18, Issue 3, pp 549–561. Springer International Publishing.
     [Google Scholar]
  2. Bruyelle, J. and Lange, A.
    [2009] Automated Characterization of Fracture Conductivities from Well Tests Inversion. SPE 121172, SPE EUROPEC/EAGE Annual Conference and Exhibition held in Amsterdam, The Netherlands, 8–11 June 2009.
     [Google Scholar]
  3. GuérillotD., LazaarS. and PianeloL.
    [1998] Inverse Methods in Geoscience Modelling: a Review for Prospective. 6th European Conference on the Mathematics of Oil Recovery, Peebles, Sept. 8–11, Proc., Paper C-33, p. 8.
     [Google Scholar]
  4. Hansen, N. and Ostermeier, A.
    [2001]. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, 9(2), 159–195.
     [Google Scholar]
  5. Hansen, N. and Kern, S.
    [2004] Evaluating the CMA Evolution Strategy on Multimodal Test Functions. Proc., Parallel Problem Solving from Nature (PPSN VIII), Berlin: Springer, 282–291.
     [Google Scholar]
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History Matching Methodology Using an Optimal Neural Network Proxy and a Global Optimization Method
Conference Proceedings 2016, 1 (2016); https://doi.org/10.3997/2214-4609.201602403
/content/papers/10.3997/2214-4609.201602403
/content/papers/10.3997/2214-4609.201602403
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