فهرست نشریات علمی دسترسی آزاد ایران

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.