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

Dongyan Fan, Sicen Lai, Hai Sun (), Yuqing Yang, Can Yang, Nianyang Fan and Minhui Wang
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Dongyan Fan: State Key Laboratory of Deep Oil and Gas, 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

Energies, 2025, vol. 18, issue 4, 1-25

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.

Keywords: machine learning; steady-state capacity prediction; transient production forecasting; spatio-temporal analysis; physics-informed neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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