IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5844-d636188.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5844/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/18/5844/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    2. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    3. Dahai Wang & Jun Peng & Qian Yu & Yuanyuan Chen & Hanghang Yu, 2019. "Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs," Sustainability, MDPI, vol. 11(7), pages 1-15, March.
    4. Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    2. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    3. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    4. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    5. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    6. Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2022. "l 1 -Regularization in Portfolio Selection with Machine Learning," Mathematics, MDPI, vol. 10(4), pages 1-15, February.
    7. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    8. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    9. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    10. Simon Blöthner & Mario Larch, 2022. "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, vol. 63(4), pages 1771-1807, October.
    11. Runge, Jason & Saloux, Etienne, 2023. "A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system," Energy, Elsevier, vol. 269(C).
    12. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    13. Lukasz Mach & Dariusz Zmarzly & Ireneusz Dabrowski & Pawel Fracz, 2020. "Comparison on Subannual Seasonality of Building Construction in European Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 241-257.
    14. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    15. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    16. Matt Burke & Matthew Agarwala & Patrycja Klusak & Kamiar Mohaddes, 2024. "Climate Policy and Sovereign Debt: The Impact of Transition Scenarios on Sovereign Creditworthiness," CAMA Working Papers 2024-73, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Guangjuan Fan & Ting Dong & Yuejun Zhao & Yalou Zhou & Wentong Zhao & Jie Wang & Yilong Wang, 2023. "Establishment and Application of a Pattern for Identifying Sedimentary Microfacies of a Single Horizontal Well: An Example from the Eastern Transition Block in the Daqing Oilfield, Songliao Basin, Chi," Energies, MDPI, vol. 16(20), pages 1-19, October.
    18. Coulibaly, Saliya & Bessin, Florent & Clerc, Marcel G. & Mussot, Arnaud, 2022. "Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    19. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    20. Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021. "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, vol. 48(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5844-:d:636188. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.