抄録
As oilfield development enters a more complex and refined stage, traditional static water injection strategies struggle to meet the demands of precise reservoir management under dynamic conditions. The emergence of big data technology provides new theoretical and practical support for optimizing oilfield water injection strategies. This paper systematically explores the application of big data in dynamic water injection management, clarifying the core concepts and advantages of dynamic injection strategies. By integrating real-time monitoring, massive data acquisition, and machine learning algorithms, oilfield development can achieve intelligent adjustment and precise optimization of water injection plans based on reservoir dynamics. This study focuses on the big data-driven optimization process for water injection, encompassing model construction, parameter optimization, and decision support. It examines its significant impact on improving injection efficiency and economic benefits. Additionally, through case studies, this paper verifies the practical effectiveness of big data technology in water injection management and demonstrates its feasibility and value through economic analysis. Finally, the paper summarizes key challenges in current technological development, such as data quality, model complexity, and interdisciplinary integration, and envisions the broad prospects of artificial intelligence and big data in driving intelligent and sustainable oilfield development. This paper aims to provide theoretical guidance and technical support for optimizing oilfield water injection strategies based on big data, fostering the intelligent evolution of oilfield development.
