Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008, Article ID 263108, 13 pages
Research Article

Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis

1Department of Computer Science, Memorial University of Newfoundlan, St. John's, NL, Canada A1B 3X5
2Chevron Energy Technology Company, San Ramon, CA 94583, USA

Received 15 March 2007; Accepted 9 August 2007

Academic Editor: Leonardo Vanneschi

Copyright © 2008 Tina Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Alfaro-Cid, K. Sharman, and A. Esparcia-Alcázar, “A genetic programming approach for bankruptcy prediction using a highly unbalanced database,” in Proceedings of the 1st European Workshop on Evolutionary Computation in Finance and Economics, M. Giacobini, A. Brabazon, and S. Cagnoni, Eds., pp. 169––178, Springer, Valencia, Spain, April 2007.
  2. W. Banzhaf, P. Nordin, R. Keller, and F. Francone, Genetic Programming: An Introduction, Morgan Kaufmann, San Francisco, Calif, USA, 1998.
  3. R. C. Bissell, Y. Sharma, and J. E. Killough, “History matching using the method of gradients: two case studies,” in Proceedings of the SPE Annual Technical Conference and Exhibition, pp. 275–282, New Orleans, La, USA, September 1994.
  4. A. Castellini, J. Landa, and J. Kikani, “Practical methods for uncertainty assessment of flow predictions for reservoirs with significant history—a field case study,” in Proceedings of the 9th European Conference on the Mathematics of Oil Recovery (ECMOR-IX '04), Cannes, France, August-September 2004, A-33.
  5. C. V. Deutsch, Geostatistical Reservoir Modeling, Oxford University Press, New York, NY, USA, 2002.
  6. A. L. Eidi, L. Holden, E. Reiso, and S. I. Aanonsen, “Automatic history matching by use of response surfaces and experimental design,” in Proceedings of the 4th European Conference on the Mathematics of Oil Recovery (ECMOR-IV '94), Roros, Norway, June 1994.
  7. K. Fang, “Uniform design: application of number theory in test design,” ACTA Mathematicae Applicatae Sinica, vol. 3, no. 4, pp. 363–372, 1980.
  8. J. M. Hammersley, “Monte Carlo methods for solving multivariable problems,” Annals of the New York Academy of Science, vol. 86, no. 3, pp. 844–874, 1960. View at Publisher · View at Google Scholar
  9. N. Japkowicz and S. Stephen, “The class imbalance problem: a systematic study,” Intelligent Data Analysis, vol. 6, no. 5, pp. 429–449, 2002.
  10. J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, Mass, USA, 1992.
  11. J. L. Landa and B. Güyagüler, “A methodology for history matching and the assessment of uncertainties associated with flow prediction,” in Proceedings of SPE Annual Technical Conference and Exhibition, pp. 3663–3676, Denver, Colo, USA, October 2003.
  12. J. L. Landa, R. K. Kalia, A. Nakano, K. Nomura, and P. Vashishta, “History match and associated forecast uncertainty analysis—practical approaches using cluster computing,” in Proceedings of the International Petroleum Technology Conference (IPTC '05), pp. 1319–1328, Doha, Qatar, November 2005.
  13. K. Narayanan, C. D. White, L. W. Lake, and B. J. Willis, “Response surface methods for upscaling heterogeneous geologic models,” in Proceedings of the 15th SPE Symposium on Reservoir Simulation, pp. 333–334, Houston, Tex, USA, February 1999.
  14. Discipulus 3.0, RML Technologies, Littleton, Colo, USA, 1998.
  15. X.-H. Wen, T. Yu, and S. Lee, “Coupling sequential-self calibration and genetic algorithms to integrate dynamic production data in geostatistical modeling,” in Proceedings of the 7th Geostatistics Congress, pp. 691–702, Banff, Alberta, Canada, September-October 2004.
  16. B. Yeten, A. Castellini, B. Güyagüler, and W. H. Chen, “A comparison study on experimental design and response surface methodologies,” in Proceedings of the SPE Reservoir Simulation Symposium, pp. 465–479, Houston, Tex, USA, January-February 2005.
  17. T. Yu, X.-H. Wen, and S. Lee, “A hybrid of sequential-self calibration and genetic algorithms inverse technique for geo-statistical reservoir modeling,” in Proceedings of the IEEE World Congress on Computational Intelligence, G. Yen, Ed., Vancouver, BC, Canada, July 2006.
  18. T. Yu, D. Wilkinson, and A. Castellini, “Applying genetic programming to reservoir history matching problem,” in Genetic Programming Theory and Practice IV, R. Riolo, Ed., pp. 187–201, Springer, New York, NY, USA, 2006. View at Publisher · View at Google Scholar
  19. T. Yu, D. Wilkinson, and D. Xie, “A hybrid GP-fuzzy approach for reservoir characterization,” in Genetic Programming Theory and Practice, R. L. Riolo and B. Worzel, Eds., pp. 271–290, Kluwer Academic Publishers, Boston, Mass, USA, 2003.