Reservoir Simulations: A Comparative Review of Machine Learning Approaches

Machine learning (ML) has rapidly emerged as a transformative technology in the oil and gas sector, particularly in enhancing the efficiency and accuracy of reservoir simulations, well-placement optimization, and underground gas storage. This paper provides a comparative review of state-of-the-art ML models used in these areas. The review systematically evaluates the performance, limitations, and future potential of various ML approaches in tackling critical challenges in reservoir engineering. By analyzing and comparing recent advances, the review highlights the role of ML in improving production forecasting, reservoir characterization, enhanced oil recovery, and optimizing well configurations. Moreover, it explores ML’s application in underground Carbon, Hydrogen, and natural gas storage. Furthermore, we identify critical research gaps and propose several future directions, such as integrating ML with traditional physics-based models. By offering insights into these state-of-the-art developments, this review aims to guide researchers and industry professionals in selecting and developing the most effective ML models for subsurface energy management.