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Fuzzy Chance-Constrained Goal Programming Model and Algorithm of Oilfield Measures

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5552))

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Abstract

Measures programming is an important way for improving the effect of measures of oil fields. Based on the theory of fuzzy chance-constrained goal programming, a fuzzy chance-constrained goal programming model is put forward for measures programming of Oilfield, which uses the increasing production and cost as objectives. The process of its hybrid intelligent algorithm including fuzzy simulation, genetic algorithm and neural network is provided, and the sensitivity analysis on uncertain factors to determine the influence on the optimal result can also be carried on. Finally, an example is given to illustrate the validity of the model and approach.

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Authors and Affiliations

  1. College of Economics and Management, China University of Petroleum(East China), Dongying, 257061, China

    Jiekun Song, Zaixu Zhang & Shuiqing Fan

  2. Kewen Institute, Xuzhou Normal University, Xuzhou, 221116, China

    Jiepeng Song

  3. Financial Assets Center of Dongxin Unit, Shengli Oilfield, Dongying, 257091, China

    Yu Zhang

Authors
  1. Jiekun Song
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  2. Jiepeng Song
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  3. Yu Zhang
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  4. Zaixu Zhang
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  5. Shuiqing Fan
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático,, CINVESTAV-IPN,, A.P. 14-740, Av.IPN 2508,, D.F., 07360,, México, México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering,, Stevens Institute of Technology,, NJ 07030,, Hoboken,, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering,, South Dakota School of Mines & Technology,, 501 E. St. Joseph Street,, SD 57701,, Rapid City,, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Song, J., Song, J., Zhang, Y., Zhang, Z., Fan, S. (2009). Fuzzy Chance-Constrained Goal Programming Model and Algorithm of Oilfield Measures. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-01510-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01509-0

  • Online ISBN: 978-3-642-01510-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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