A Neural Stochastic Optimization Framework for Oil Parameter Estimation

  • Rafael E. Banchs
  • Hector Klie
  • Adolfo Rodriguez
  • Sunil G. Thomas
  • Mary F. Wheeler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

The main objective of the present work is to propose and evaluate a neural stochastic optimization framework for reservoir parameter estimation, for which a history matching procedure is implemented by combining three independent sources of spatial and temporal information: production data, time-lapse seismic and sensor information. In order to efficiently perform large-scale parameter estimation, a coupled multilevel, stochastic and learning search methodology is proposed. At a given resolution level, the parameter space is globally explored and sampled by the simultaneous perturbation stochastic approximation (SPSA) algorithm. The estimation and sampling performed by SPSA is further enhanced by a neural learning engine that evaluates the objective function sensitiveness with respect to parameter estimates in the vicinity of the most promising optimal solutions.

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References

  1. Lumley, D.: Time-lapse seismic reservoir monitoring. Geophysics 66, 50–53 (2001)CrossRefGoogle Scholar
  2. Versteeg, R., Ankeny, M., Harbour, J., Heath, G., Kostelnik, K., Matson, E., Moor, K., Richardson, A.: A structured approach to the use of near-surface geophysics in long-term monitoring. Expert Systems with Applications 23, 700–703 (2004)Google Scholar
  3. van der Baan, M., Jutten, C.: Neural networks in geophysical applications. Geophysics 65, 1032–1047 (2000)CrossRefGoogle Scholar
  4. Nikravesh, M.: Soft computing-based computational intelligent for reservoir characterization. Expert Systems with Applications 26, 19–38 (2004)CrossRefGoogle Scholar
  5. Bangerth, W., Klie, H., Parashar, M., Mantosian, V., Wheeler, M.F.: An autonomic reservoir framework for the stochastic optimization of well placement. Cluster Computing 8, 255–269 (2005)CrossRefGoogle Scholar
  6. Parashar, M., Klie, H., Catalyurek, U., Kurc, T., Bangerth, W., Matossian, V., Saltz, J., Wheeler, M.F.: Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. Future Generation of Computer Systems 21, 19–26 (2005)CrossRefGoogle Scholar
  7. Spall, J.C.: Introduction to stochastic search and optimization: Estimation, simulation and control. John Wiley & Sons, Inc., New Jersey (2003)MATHCrossRefGoogle Scholar
  8. Keane, A., Nair, P.: Computational Approaches for Aerospace Design: The Pursuit of Excellence. Wiley, England (2005)CrossRefGoogle Scholar
  9. Parashar, M., Wheeler, J.A., Pope, G., Wang, K., Wang, P.: A new generation EOS compositional reservoir simulator. Part II: Framework and multiprocessing. In: Fourteenth SPE Symposium on Reservoir Simulation, Dalas, Texas, pp. 31–38 (1997)Google Scholar
  10. Wang, P., Yotov, I., Wheeler, M.F., Arbogast, T., Dawson, C.N., Parashar, M., Sepehrnoori, K.: A new generation EOS compositional reservoir simulator. Part I: Formulation and Discretization. In: Fourteenth SPE Symposium on Reservoir Simulation, Society of Petroleum Engineers, Dalas, Texas pp. 55–64 (1997)Google Scholar
  11. Bourbie, T., Coussy, O., Zinszner, B.: Acoustics of Porous Media. Institut fran¸cais du p´etrole publications, Editions TECHNIP (1987)Google Scholar
  12. Nishi, K.: A three dimensional robust seismic ray tracer for volcanic regions. Earth Planets Space 53, 101–109 (2001)Google Scholar
  13. Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, New York (1994)MATHGoogle Scholar
  14. Christie, M., Blunt, M.: Tenth SPE Comparative Solution Project: A Comparison of Upscaling Techniques. SPE Reservoir Engineering 12, 308–317 (2001)Google Scholar
  15. Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rafael E. Banchs
    • 1
  • Hector Klie
    • 2
  • Adolfo Rodriguez
    • 2
  • Sunil G. Thomas
    • 2
  • Mary F. Wheeler
    • 2
  1. 1.GPS, TSCPolytechnic University of CataloniaBarcelonaSpain
  2. 2.CSM, ICESThe University of Texas at AustinUSA

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