抄録
With the deepening development of oilfields towards complex reservoirs and heterogeneous fluid behavior, traditional reservoir, and fluid dynamic simulation methods face significant limitations regarding accuracy and efficiency. In recent years, the rapid development of big data technology has provided new technical pathways and innovative opportunities for oilfield development. This paper systematically explores the application potential of big data technology in reservoir and fluid dynamic simulation, with a focus on how big data analysis can achieve high-precision prediction of oil and gas flow behavior and intelligent optimization of injection and production schemes. First, the theoretical framework of reservoir and fluid dynamic simulation is introduced, highlighting the limitations of traditional methods in modeling complex reservoir structures and fluid properties. Next, applying big data technology, combined with machine learning and deep learning algorithms, is detailed, emphasizing the accurate modeling of reservoir characteristics and fluid flow patterns through the mining and analysis of massive multi-source data. Based on this, big data-driven optimization strategies for water injection and oil production are proposed, along with the effectiveness and insights from practical oilfield applications. Finally, the paper summarizes the challenges of big data technology in oilfield development, forecasts the future direction of intelligent oilfields, and emphasizes the importance of interdisciplinary integration in promoting injection-production optimization and intelligent decision-making. This paper aims to provide theoretical support and practical guidance for oilfield development, helping to improve development efficiency and economic benefits.
