Advances in Hybrid Machine Learning and Physics-Based Models for Enhanced Reservoir Simulation

Reservoir simulation is critical in optimizing resource extraction and ensuring the efficient management of subsurface reservoirs. While rooted in robust physical principles, traditional modeling approaches often face limitations in handling complex reservoir dynamics, computational intensity, and data sparsity. Recent advances in hybrid methodologies combining machine learning (ML) and physics-based models offer transformative solutions to these challenges. This paper explores the evolution and integration of hybrid approaches in reservoir simulation, highlighting key developments such as physics-informed neural networks, surrogate modeling, and adaptive workflows. The benefits of these models, including enhanced predictive accuracy, computational efficiency, and adaptability, are examined alongside the challenges of implementation, such as computational demands and data quality constraints. Practical implications for the industry are discussed, particularly in improving decision-making and optimizing production strategies. The paper concludes with recommendations for further research and industry adoption, emphasizing the need for interdisciplinary collaboration, advancements in explainable artificial intelligence, and the development of standardized frameworks. These hybrid approaches mark a significant milestone in the evolution of reservoir simulation, offering innovative pathways for sustainable and efficient resource management.