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Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir

Author

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  • Dongyan Fan

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum, Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China)

  • Sicen Lai

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China)

  • Hai Sun

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China)

  • Yuqing Yang

    (Institute of Geology, Tenth Oil Production Plant, Daqing Oilfield Co., Ltd., Daqing 163000, China)

  • Can Yang

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China)

  • Nianyang Fan

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China)

  • Minhui Wang

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China)

Abstract

Accurate oil and gas production forecasting is essential for optimizing field development and operational efficiency. Steady-state capacity prediction models based on machine learning techniques, such as Linear Regression, Support Vector Machines, Random Forest, and Extreme Gradient Boosting, effectively address complex nonlinear relationships through feature selection, hyperparameter tuning, and hybrid integration, achieving high accuracy and reliability. These models maintain relative errors within acceptable limits, offering robust support for reservoir management. Recent advancements in spatiotemporal modeling, Physics-Informed Neural Networks (PINNs), and agent-based modeling have further enhanced transient production forecasting. Spatiotemporal models capture temporal dependencies and spatial correlations, while PINN integrates physical laws into neural networks, improving interpretability and robustness, particularly for sparse or noisy data. Agent-based modeling complements these techniques by combining measured data with numerical simulations to deliver real-time, high-precision predictions of complex reservoir dynamics. Despite challenges in computational scalability, data sensitivity, and generalization across diverse reservoirs, future developments, including multi-source data integration, lightweight architectures, and real-time predictive capabilities, can further improve production forecasting, addressing the complexities of oil and gas production while supporting sustainable resource management and global energy security.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:842-:d:1588724
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