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A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network

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  • Fuquan Song

    (School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China
    School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Heying Ding

    (School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Yongzheng Wang

    (School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Shiming Zhang

    (Exploration and Development Scientific Research Institute of Shengli Oil Field Branch of Sinopec, Dongying 257015, China)

  • Jinbiao Yu

    (Exploration and Development Scientific Research Institute of Shengli Oil Field Branch of Sinopec, Dongying 257015, China)

Abstract

Tight reservoirs have poor physical properties: low permeability and strong heterogeneity, which makes it difficult to predict productivity. Accurate prediction of oil well production plays a very important role in the exploration and development of oil and gas reservoirs, and improving the accuracy of production prediction has always been a key issue in reservoir characterization. With the development of artificial intelligence, high-performance algorithms make reliable production prediction possible from the perspective of data. Due to the high cost and large error of traditional seepage theory formulas in predicting oil well production, this paper establishes a horizontal well productivity prediction model based on a hybrid neural network method (CNN-LSTM), which solves the limitations of traditional methods and produces accurate predictions of horizontal wells’ daily oil production. In order to prove the effectiveness of the model, compared with the prediction results of BPNN, RBF, RNN and LSTM, it is concluded that the error results of the CNN-LSTM prediction model are 67%, 60%, 51.3% and 28% less than those of the four models, respectively, and the determination coefficient exceeds 0.95. The results show that the prediction model based on a hybrid neural network can accurately reflect the dynamic change law of production, which marks this study as a preliminary attempt of the application of this neural network method in petroleum engineering, and also provides a new method for the application of artificial intelligence in oil and gas field development.

Suggested Citation

  • Fuquan Song & Heying Ding & Yongzheng Wang & Shiming Zhang & Jinbiao Yu, 2023. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network," Energies, MDPI, vol. 16(6), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2904-:d:1103595
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    References listed on IDEAS

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    1. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(C).
    2. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
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    Cited by:

    1. Aoxue Zhang & Yanlong Zhao & Xuanxuan Li & Xu Fan & Xiaoqing Ren & Qingxia Li & Leishu Yue, 2024. "Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production," Energies, MDPI, vol. 17(21), pages 1-15, October.
    2. Dongyan Fan & Sicen Lai & Hai Sun & Yuqing Yang & Can Yang & Nianyang Fan & Minhui Wang, 2025. "Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir," Energies, MDPI, vol. 18(4), pages 1-25, February.

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