抄録
Microbial enhanced oil recovery (MEOR) utilizes microbial metabolic products to improve reservoir properties and enhance oil recovery, making it a key direction in tertiary oil recovery technologies. However, due to the complexity of reservoir environments and the dynamic mechanisms of microbial activity, traditional methods face significant limitations in optimizing injection strategies and improving recovery efficiency. This study integrates big data and artificial intelligence technologies to investigate numerical simulation and intelligent optimization methods for MEOR systematically. Firstly, the principles and technical challenges of MEOR are analyzed, highlighting the critical role of big data in reservoir data integration and the formulation of precise injection strategies. Secondly, dynamic numerical models of the microbial reservoir system are developed using machine learning and deep learning techniques to simulate the multi-layered impacts of microbial metabolism, injection parameters, and environmental conditions on oil recovery efficiency. Furthermore, optimization algorithms such as genetic algorithms and deep reinforcement learning are explored for their application in optimizing injection parameters, enabling intelligent and optimal decision-making support for injection strategies. Case studies in real oilfields demonstrate the significant advantages of big data-driven numerical simulations and intelligent optimization in improving MEOR efficiency. Finally, the future directions of MEOR are discussed, including data-driven multi-scale modeling, real-time optimization under complex reservoir conditions, and intelligent control. This study provides a novel practical pathway to advance MEOR technologies.
