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A Hybrid Oil Production Prediction Model Based on Artificial Intelligence Technology

Author

Listed:
  • Xiangming Kong

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

  • Yuetian Liu

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

  • Liang Xue

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

  • Guanlin Li

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

  • Dongdong Zhu

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

Abstract

Oil production prediction plays a significant role in designing programs for hydrocarbon reservoir development, adjusting production operations and making decisions. The prediction accuracy of oil production based on single methods is limited since more and more unconventional reservoirs are being exploited. Artificial intelligence technology and data decomposition are widely implemented in multi-step forecasting strategies. In this study, a hybrid prediction model was proposed based on two-stage decomposition, sample entropy reconstruction and long short-term memory neural network (LSTM) forecasts. The original oil production data were decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); then these IMFs with different sample entropy (SE) values were reconstructed based on subsequence reconstruction rules that determine the appropriate reconstruction numbers and modes. Following that, the highest-frequency reconstructed IMF was preferred to be decomposed again by variational mode decomposition (VMD), and subsequences of the secondary decomposition and the remaining reconstructed IMFs were fed into the corresponding LSTM predictors based on a hybrid architecture for forecasting. Finally, the prediction values of each subseries were integrated to achieve the result. The proposed model makes predictions for the well production rate of the JinLong volcanic reservoir, and comparative experiments show that it has higher forecasting accuracy than other methods, making it recognized as a potential approach for evaluating reservoirs and guiding oilfield management.

Suggested Citation

  • Xiangming Kong & Yuetian Liu & Liang Xue & Guanlin Li & Dongdong Zhu, 2023. "A Hybrid Oil Production Prediction Model Based on Artificial Intelligence Technology," Energies, MDPI, vol. 16(3), pages 1-16, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1027-:d:1038651
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    References listed on IDEAS

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    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    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).
    3. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    4. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
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    Cited by:

    1. Beichen Zhao & Binshan Ju & Chaoxiang Wang, 2023. "Initial-Productivity Prediction Method of Oil Wells for Low-Permeability Reservoirs Based on PSO-ELM Algorithm," Energies, MDPI, vol. 16(11), pages 1-17, June.

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