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Fuzzy Information and Engineering pp 628–636Cite as

An Optimization Model for Stimulation of Oilfield at the Stage of High Water Content

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Part of the book series: Advances in Soft Computing ((AINSC,volume 40))

Abstract

This paper presents a method to predict pressure distribution and residual oil distribution for water flooding oilfield. To meet the demands of the integral design of oilfield development plan and the high-efficiency exploitation of oilfield, this paper proposes an optimization method for single stimulation measure and integral adjustment of a tract based on analysis of oilfield performance, economic evaluation and professional experience. This integral optimization method is helpful to decision making and optimize the adjustment project. Enforcement of the plan will help not only complete the mission of production, but can bring the maximum incomes under present conditions. So, this study is significant to improve the efficiency and effectiveness of field work.

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References

  1. Jia, Z., et al.: Design and Analysis of Oilfield Development, pp. 18–27. Press of Harbin Industrial University (1994)

    Google Scholar 

  2. Wang, X., et al.: Method of Integral Adjustment for High Water Cut Oilfield Development, pp. 59–66. Petroleum Industry Press, Beijing (1997)

    Google Scholar 

  3. Liu, Q., et al.: Optimization of Stimulation Measures. Technology of Petroleum Drilling and Production (1992)

    Google Scholar 

  4. Zadeh, L.A.: Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems 1, 3–28 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  5. Dubois, D., Prade, H.: Possibility Theory. Plenum, New York (1988)

    MATH  Google Scholar 

  6. Klir, G.H.: On Fuzzy-Fet Interpretation of Possibility Theory. Fuzzy Sets and Systems 108, 263–373 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  7. Esogbue, A.O.: On the Computational Complexity of Some Fuzzy Dynamic Programs. Journal of Computers and Mathematics with Applications 37(11-12), 47–51 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  8. Bellman, R.E., Zadeh, L.A.: Decision Making in a Fuzzy Environment. Management Science 17(4), 141–164 (1970)

    Article  MathSciNet  Google Scholar 

  9. Kacprzyk, J.: Multistage Fuzzy Control: A Model-Based Approach to Control and Decision-Making. Willey, Chichester (1997)

    MATH  Google Scholar 

  10. Esogbue, A.O.: New Frontiers of Research in High Impact Dynamic Programming. In: Proceedings of the 9th Bellman Continuum International Workshop on Uncertain Systems and Soft Computing, Beijing, China, July 24-27 (2002)

    Google Scholar 

  11. Inuiguchi, M., Ichihashi, H., Kume, Y.: Modality Constrained Programming Problems: A Unified Approach to Fuzzy Mathematical Programming Problems in the Setting of Prossibility Theory. Inform. Sciences 67, 93–126 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  12. Lai, Y.-J., Hwang, C.-L.: Fuzzy Mathematical Programming: Methods and Applications. Springer, Heidelberg (1992)

    MATH  Google Scholar 

  13. Liu, B., Liu, Y.-K.: Expected Value of Fuzzy Variable and Fuzzy Expected Value Models. IEEE Trans. Fuzzy Syst. 10(4) (2002)

    Google Scholar 

  14. Luhandjula, M.K.: Fuzziness and Randomness in an Optimization Framework. Fuzzy Sets and Systems 77, 291–297 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  15. Lwakernaak, H.: Fuzzy Random Variables I. Definition and Theorems. Inform. Sciences 15, 1–29 (1978)

    Article  Google Scholar 

  16. Liu, B.: Theory and Practice of Uncertain Programming. Physica-Verlag, Heidelberg (2002)

    MATH  Google Scholar 

  17. Liu, B.: Random Fuzzy Dependent-Chance Programming and Its Hybrid Intelligent Algorithm. Inform. Sciences 141(3-4), 259–271 (2002)

    Article  MATH  Google Scholar 

  18. Liu, Y.-K., Liu, B.: Expected Value Operator of Random Fuzzy Variable and Random Fuzzy Expected Value Models. Technical Report (2001)

    Google Scholar 

  19. Kaufman, A., Gupta, M.M.: Fuzzy Mathematical Models in Engineering and Management Science, 2nd edn. North-Holland, Amsterdam (1991)

    Google Scholar 

  20. Liu, B., Esogbue, A.O.: Fuzzy Criterion Set and Fuzzy Criterion Dynamic Programming. Journal of Mathmatical Analysis and Applications 199(1), 293–311 (1996)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Key Laboratory of Enhanced Oil Recovery in Ministry of Education, Daqing Petroleum Institute, Daqing, Heilongjiang Province, 163318, P.R.C.

    Song Kaoping, Yang Erlong & Liu Meijia

  2. Editorial Department of Oil Drilling & Production Technology, Research Institute of Oil Production Technology, Huabei Oil Field Corporation, Renqiu City, Hebei Province, 062552, P.R.C.

    Jing nuan

Authors
  1. Song Kaoping
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  2. Yang Erlong
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  3. Jing nuan
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  4. Liu Meijia
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Editor information

Bing-Yuan Cao

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© 2007 Springer-Verlag Berlin Heidelberg

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Kaoping, S., Erlong, Y., nuan, J., Meijia, L. (2007). An Optimization Model for Stimulation of Oilfield at the Stage of High Water Content. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_68

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  • DOI: https://doi.org/10.1007/978-3-540-71441-5_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71440-8

  • Online ISBN: 978-3-540-71441-5

  • eBook Packages: EngineeringEngineering (R0)

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