Abstract
Carbonate reservoirs exhibit highly heterogeneous pore systems, complex fracture networks, and unique hydrodynamic characteristics, making hydrocarbon migration and accumulation mechanisms difficult to predict accurately. Traditional hydrocarbon migration models often struggle to address complex geological conditions, failing to capture migration pathways and reservoir distribution patterns precisely. In recent years, artificial intelligence (AI) technologies, particularly machine learning and deep learning models, have emerged as innovative solutions due to their strengths in big data analysis and complex system modeling. By integrating historical and real-time monitoring data, AI can construct data-driven hydrocarbon migration prediction models, effectively simulating hydrocarbon distribution and retention mechanisms under varying geological conditions. Additionally, AI dynamically optimizes hydrodynamic parameters, improving the efficiency and accuracy of development strategies. This paper systematically evaluates the current applications of AI in studying carbonate reservoir formation mechanisms and hydrocarbon migration simulation, with a focus on integrating AI models with hydrodynamic simulations to optimize migration behavior. Finally, AI-driven intelligent development strategies are proposed to support the efficient development and refined management of carbonate reservoirs.
