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Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends

Author

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  • Hai Wang

    (Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

  • Shengnan Chen

    (Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

Abstract

In the past few decades, the machine learning (or data-driven) approach has been broadly adopted as an alternative to scientific discovery, resulting in many opportunities and challenges. In the oil and gas sector, subsurface reservoirs are heterogeneous porous media involving a large number of complex phenomena, making their characterization and dynamic prediction a real challenge. This study provides a comprehensive overview of recent research that has employed machine learning in three key areas: reservoir characterization, production forecasting, and well test interpretation. The results show that machine learning can automate and accelerate many reservoirs engineering tasks with acceptable level of accuracy, resulting in more efficient and cost-effective decisions. Although machine learning presents promising results at this stage, there are still several crucial challenges that need to be addressed, such as data quality and data scarcity, the lack of physics nature of machine learning algorithms, and joint modelling of multiple data sources/formats. The significance of this research is that it demonstrates the potential of machine learning to revolutionize the oil and gas sector by providing more accurate and efficient solutions for challenging problems.

Suggested Citation

  • Hai Wang & Shengnan Chen, 2023. "Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends," Energies, MDPI, vol. 16(3), pages 1-11, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1392-:d:1051695
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    References listed on IDEAS

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    1. Nilesh Dixit & Paul McColgan & Kimberly Kusler, 2020. "Machine Learning-Based Probabilistic Lithofacies Prediction from Conventional Well Logs: A Case from the Umiat Oil Field of Alaska," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. 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).
    3. Zekun Guo & Hongjun Wang & Xiangwen Kong & Li Shen & Yuepeng Jia, 2021. "Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation," Energies, MDPI, vol. 14(17), pages 1-17, September.
    4. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    5. Hongyang Chu & Xinwei Liao & Peng Dong & Zhiming Chen & Xiaoliang Zhao & Jiandong Zou, 2019. "An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)," Energies, MDPI, vol. 12(15), pages 1-27, July.
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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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