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Theoretical research on reservoir closed-loop production management

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Abstract

Closed-loop production management combines the process of history matching and production optimization together to periodically updates the reservoir model and determine the optimal control strategy for production development to realize the goal of decreasing the knowledge of model uncertainty as well as maximize the economic benefits for the expected reservoir life. The adjoint-gradient-based methods seem to be the most efficient algorithms for closed-loop management. Due to complicated calculation and limited availability of adjoint-gradient in commercial reservoir simulators, the application of this method is still prohibited for real fields. In this paper, a simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed for reservoir closed-loop production management with the combination of a parameterization way for history matching and a covariance matrix to smooth well controls for production optimization. By using a set of unconditional realizations, the proposed parameterization method can transform the minimization of the objective function in history matching from a higher dimension to a lower dimension, which is quite useful for large scale history matching problem. Then the SPSA algorithm minimizes the objective function iteratively to get an optimal estimate reservoir model. Based on a prior covariance matrix for production optimization, the SPSA algorithm generates a smooth stochastic search direction which is always uphill and has a certain time correlation for well controls. The example application shows that the SPSA algorithm for closed-loop production management can decrease the geological uncertainty and provide a reasonable estimate reservoir model without the calculation of the adjoint-gradient. Meanwhile, the well controls optimized by the alternative SPSA algorithm are fairly smooth and significantly improve the effect of waterflooding with a higher NPV and a better sweep efficiency than the reactive control strategy.

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References

  1. Chen Y, Oliver D, Zhang D. Efficient ensemble-based closed-loop production optimization. SPE J, 2009, 14(4): 634–645

    Google Scholar 

  2. Brouwer D, Naevdal G, Jansen J, et al. Improved reservoir management through optimal control and continuous model updating. SPE 90149-MS, 2004

  3. Chen C, Wang Y, Li G, et al. Closed-loop reservoir Management on the Brugge test case. Comput Geosci, 2010, 14: 691–703

    Article  MATH  Google Scholar 

  4. Jansen J, Douma S, Brouwer D, et al. Closed-loop reservoir management. SPE 119098, 2009

  5. Peters E, Arts R, Brouwer G, et al. Results of the Brugge benchmark study for flooding optimization and history matching. SPE 119094, 2009

  6. Sarma P, Durlofsky L, Aziz K. Efficient closed-loop production optimization under uncertainty. SPE-94241, 2005

  7. Wang C, Li G, Reynolds A C. Production optimization in closed-loop reservoir management. 2007 SPE Annual Technical Conference and Exhibition, SPE 109805, 2007

  8. Zhang F, Reynolds A. Optimization algorithms for automatic history matching of production data. Proceedings of 8th European Conference on the Mathematics of Oil Recovery, 2002

  9. Gao G, Reynolds A. An improved implementation of the LBFGS algorithm for automatic history matching. SPE J, 2006, 11(1): 5–17

    Google Scholar 

  10. Tavakoli R, Reynolds A C. History matching with parameterization based on the SVD of a dimensionless sensitivity matrix. SPE J, 2010, 15(2): 495–508

    Google Scholar 

  11. Gao G, Li G, Reynolds A. A stochastic algorithm for automatic history matching. SPE J, 2007, 12(2): 196–208

    Google Scholar 

  12. Li G, Reynolds A. Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm. Comput Geosci, 2010, 15: 378–390

    Google Scholar 

  13. Gao G, Zafari M, Reynolds A. Quantifying uncertainty for the PUNQ-S3 problem in a Bayesian setting with RML and EnKF. SPE-93324, 2005

  14. Gu Y, Oliver D. The ensemble Kalman filter for continuous updating of reservoir simulation models. J Energy Res Technol, 2006, 128(1): 79–87

    Article  Google Scholar 

  15. Gu Y, Oliver D. An iterative ensemble Kalman filter for multiphase fluid flow data assimilation. SPE J, 2007, 12(4): 438–446

    Google Scholar 

  16. Liu N, Oliver D. Critical evaluation of the ensemble Kalman filter on history matching of geologic facies. SPE Reserv Eval Eng, 2005, 8(4): 470–477

    Google Scholar 

  17. Zafari M, Li G, Reynolds A. Iterative forms of the ensemble kalman filter. Proceedings of the 10th European Conference on the Mathematical Oil Recovery Amsterdam, 2006

  18. Zafari M, Reynolds A. Assessing the uncertainty in reservoir description and performance predictions with the ensemble Kalman filter. Proceedings of the 2005 SPE Annual Technical Conference and Exhibition. SPE 95750, 2005

  19. Naevdal G, Johnsen L, Aanonsen S. Reservoir monitoring and continuous model updating using ensemble Kalman filter. 2003 SPE Annual Technical Conference and Exhibition. SPE 84372, 2003

  20. Naevdal G, Mannseth T, Vefring E H, et al. Near-well reservoir monitoring through ensemble Kalman filter. SPE 75235, 2002

  21. Zhao Y, Reynolds A, Li G. Generating facies maps by assimilating production data and seismic data with the ensemble Kalman filter. SPE 113990, 2008

  22. Brouwer D, Jansen J. Dynamic optimization of waterfooding with smart wells using optimal control theory. SPE J, 2004, 9(4): 391–402

    Google Scholar 

  23. Sarma P, Durlofsky L, Aziz K. Implementation of adjoint solution for optimal control of smart wells. SPE 92864, 2005

  24. Wang C, Li G, Reynolds A. Optimal well placement for production optimization, SPE J, 2009, 14(3): 506–523

    Google Scholar 

  25. Leeuwenburgh O, Egberts P, Abbink O A. Ensemble methods for reservoir life-cycle optimization and well placement. SPE 136916, 2010

  26. Asadollahi M, Navedal G, Markovinovic R, et al. A work flow for efficient initialization of local search iterative methods for waterflooding optimization. Proceedings of the 2009 International Petroleum Technical Conference, 2009. IPTC13994

  27. Oliver D, Reynolds A, Liu N. Inverse Theory for Petroleum Reservoir Characterization and History Matching. New York: Cambridge University Press, 2008

    Book  Google Scholar 

  28. Spall J. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans Automat Control, 1992, 37(3): 332–341

    Article  MathSciNet  MATH  Google Scholar 

  29. Spall J. Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans Aerosp Electron Syst, 1998, 34(3): 817–823

    Article  Google Scholar 

  30. Nocedal J, Wright S J. Numerical Optimization. New York: Springer, 1999

    Book  MATH  Google Scholar 

  31. Christalkos G. Random Field Models in Earth Sciences. San Diego, CA: Academic Press, 1992

    Google Scholar 

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Authors and Affiliations

  1. Petroleum Engineering College, Yangtze University, Jingzhou, 434023, China

    Hui Zhao

  2. Petroleum Engineering College, China University of Petroleum, Dongying, 257061, China

    Yang Li, Jun Yao & Kai Zhang

  3. Exploration and Production Research Institute, China Petroleum and Chemical Corporation, Beijing, 100029, China

    Yang Li

Authors
  1. Hui Zhao
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  2. Yang Li
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  3. Jun Yao
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  4. Kai Zhang
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Correspondence to Hui Zhao.

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Zhao, H., Li, Y., Yao, J. et al. Theoretical research on reservoir closed-loop production management. Sci. China Technol. Sci. 54, 2815–2824 (2011). https://doi.org/10.1007/s11431-011-4465-2

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  • DOI: https://doi.org/10.1007/s11431-011-4465-2

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