抄録
With the increasing complexity of oilfield development and the urgent need to enhance resource extraction efficiency, the application of big data technology in oilfield development and management has gained growing attention. This paper focuses on the big data-driven optimization of combined water flooding and hydraulic fracturing, aiming to explore their synergistic effects and propose data-based optimization strategies to improve recovery efficiency. First, the fundamental principles of water flooding and hydraulic fracturing and their critical roles in enhancing recovery are outlined, highlighting the core value of big data in reservoir characterization, dynamic parameter optimization, and real-time monitoring. Subsequently, an optimization model driven by big data is developed, enabling real-time data acquisition and feedback to dynamically adjust water flooding and fracturing parameters, thereby achieving efficient synergy between the two technologies and significantly improving production efficiency and oilfield development outcomes. Finally, the study summarizes the technical challenges of big data-driven joint optimization. It discusses future development directions, including algorithm improvements, real-time monitoring system upgrades, and multi-source data integration. This paper provides new technological support and scientific insights for efficient oilfield development, with significant theoretical and practical value.
