Machine Learning in Reservoir Engineering: A Review of Techniques and Applications

Chegini, Mahdi; Saffarzadeh Hosseini, Sadegh

doi:10.22050/ijogst.2025.497684.1727

Machine Learning in Reservoir Engineering: A Review of Techniques and Applications

Document Type : Review paper

Authors

Mahdi Chegini ¹

Sadegh Saffarzadeh Hosseini ²

¹ 1 M.S. Student, Department of Petroleum Engineering, Ahwaz Faculty of Petroleum, Petroleum University of Technology, Ahwaz, Iran

² 2 Assistant Professor, Department of Petroleum Engineering, Abadan Faculty of Petroleum, Petroleum University of Technology, Abadan, Iran

10.22050/ijogst.2025.497684.1727

Abstract

Machine learning is emerging as a transformative force in reservoir engineering, addressing long-standing challenges in hydrocarbon exploration and production with unprecedented speed and accuracy. This review examines the core machine learning paradigms—supervised learning, unsupervised learning, and reinforcement learning—applied across eight critical domains: reservoir simulation, pore pressure prediction, history matching, reservoir characterization, acidizing operations, hydraulic fracturing, waterflooding, and CO₂-enhanced oil recovery. By leveraging algorithms such as artificial neural networks, support vector machines, particle swarm optimization, and long short-term memory networks, machine learning achieves up to three orders of magnitude improvement in computational efficiency and predictive accuracy exceeding 90%, with the potential to approach 100% under optimal conditions. These performance levels substantially surpass those of traditional methods, which typically plateau at approximately 75% accuracy. Machine learning models integrate heterogeneous, multi-scale data to optimize real-time operations, enhance decision-making, and significantly reduce operational costs. Furthermore, machine learning contributes to sustainability through energy-efficient computational frameworks and enhanced carbon sequestration capabilities. Although challenges such as data scarcity and limited model interpretability persist, emerging approaches—including transfer learning and physics-informed models—are creating new opportunities. Overall, this review underscores the capacity of machine learning to redefine reservoir engineering toward a more intelligent and sustainable energy future.

Highlights

· Machine learning significantly accelerates reservoir simulation, achieving speedups of approximately two to three orders of magnitude while maintaining accuracy levels of 90–100%, compared with approximately 75% for traditional simulation methods.

· Data scarcity in machine learning applications is increasingly mitigated through transfer learning approaches and physics-informed modeling frameworks.

· Machine learning techniques enhance CO₂-enhanced oil recovery and reservoir management by improving energy efficiency and enabling carbon sequestration, thereby supporting sustainability objectives.

· Artificial neural networks, particle swarm optimization, and recurrent neural networks have fundamentally transformed history matching processes and time-series modeling of reservoir behavior.

Keywords

Machine learning (ML), Reinforcement learning (RL), Reservoir engineering (RE), Supervised learning (SL), Unsupervised learning (UL)

Subjects

Petroleum Engineering – Reservoir

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