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A Review of AI Applications in Unconventional Oil and Gas Exploration and Development

Author

Listed:
  • Feiyu Chen

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065007, China)

  • Linghui Sun

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065007, China
    State Key Laboratory of Enhanced Oil & Gas Recovery, Research Institute of Petroleum Exploration & Development, Beijing 100083, China)

  • Boyu Jiang

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065007, China)

  • Xu Huo

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065007, China)

  • Xiuxiu Pan

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065007, China)

  • Chun Feng

    (State Key Laboratory of Enhanced Oil & Gas Recovery, Research Institute of Petroleum Exploration & Development, Beijing 100083, China)

  • Zhirong Zhang

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065007, China)

Abstract

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development.

Suggested Citation

  • Feiyu Chen & Linghui Sun & Boyu Jiang & Xu Huo & Xiuxiu Pan & Chun Feng & Zhirong Zhang, 2025. "A Review of AI Applications in Unconventional Oil and Gas Exploration and Development," Energies, MDPI, vol. 18(2), pages 1-30, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:391-:d:1569415
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    References listed on IDEAS

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