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Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks

Author

Listed:
  • Chenglong Chen

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Yikun Liu

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Decai Lin

    (School of Energy Science and Engineering, University of Science and Technology of China, Hefei 230000, China
    Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510000, China)

  • Guohui Qu

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Jiqiang Zhi

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Shuang Liang

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Fengjiao Wang

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Dukui Zheng

    (School of Petroleum Engineering, Yangtze University, Wuhan 430000, China)

  • Anqi Shen

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
    Petroleum Engineering Department, University of Houston, Houston, TX 77004, USA)

  • Lifeng Bo

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Shiwei Zhu

    (College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China
    Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

Abstract

Accurately predicting oilfield development indicators (such as oil production, liquid production, current formation pressure, water cut, oil production rate, recovery rate, cost, profit, etc.) is to realize the rational and scientific development of oilfields, which is an important basis to ensure the stable production of the oilfield. Due to existing oilfield development index prediction methods being difficult to accurately reflect the complex nonlinear problem in the oil field development process, using the artificial neural network, which can predict the oilfield development index with the function of infinitely close to any non-linear function, will be the most ideal prediction method at present. This article summarizes four commonly used artificial neural networks: the BP neural network, the radial basis neural network, the generalized regression neural network, and the wavelet neural network, and mainly introduces their network structure, function types, calculation process and prediction results. Four kinds of artificial neural networks are optimized through various intelligent algorithms, and the principle and essence of optimization are analyzed. Furthermore, the advantages and disadvantages of the four artificial neural networks are summarized and compared. Finally, based on the application of artificial neural networks in other fields and on existing problems, a future development direction is proposed which can serve as a reference and guide for the research on accurate prediction of oilfield development indicators.

Suggested Citation

  • Chenglong Chen & Yikun Liu & Decai Lin & Guohui Qu & Jiqiang Zhi & Shuang Liang & Fengjiao Wang & Dukui Zheng & Anqi Shen & Lifeng Bo & Shiwei Zhu, 2021. "Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks," Energies, MDPI, vol. 14(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5844-:d:636188
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    References listed on IDEAS

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