Machine Learning in Reservoir Engineering: Techniques and Applications

Machine learning (ML) is emerging as a transformative force in reservoir engineering (RE), addressing long-standing challenges in hydrocarbon exploration and production with unprecedented speed and accuracy. This study explores the core ML paradigms—supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL)—applied across eight essential domains: reservoir simulation, pore pressure prediction, history matching, reservoir characterization, acidizing operations, hydraulic fracturing, waterflooding, and CO₂-enhanced oil recovery (EOR). By employing algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), particle swarm optimization (PSO), and long short-term memory (LSTM) networks, ML achieves up to ۱,۰۰۰-fold improvements in computational efficiency and predictive accuracy rates exceeding ۹۰%, with potential to reach ۱۰۰% under optimal conditions—far surpassing traditional approaches, which often plateau at around ۷۵%. These models integrate diverse, multi-scale data to optimize real-time operations, enhance decision-making, and significantly reduce costs. Moreover, ML contributes to sustainability through energy-efficient computations and carbon sequestration capabilities. While key limitations such as data scarcity and limited interpretability remain, promising innovations—including transfer learning and physics-informed models—are opening new pathways. This paper highlights ML’s unique ability to redefine reservoir engineering for a more intelligent and sustainable energy future.

کلیدواژه ها:

Machine Learning(ML) ، Reservoir Engineering(RE) ، Supervised Learning(SL) ، Unsupervised Learning(UL) ، Reinforcement Learning(RL)