Research on Optimization Technology of Injection Well Allocation Scheme Based on Big Data

With the deepening of oilfield development, the ASP flooding technology plays an increasingly significant role in enhancing oil recovery. However, traditional dynamic analysis methods struggle to accurately identify the key factors affecting development effectiveness. This study takes Block S as the research object and relies on big data technology to construct an optimization model for injection well allocation schemes. Through data integration and governance, sensitive factor analysis, deep learning modeling, and scheme optimization techniques, an intelligent allocation scheme software platform has been developed. The research results show that static sensitive factors (effective thickness, permeability, etc.) and dynamic sensitive factors (injection pressure, mother liquid volume, etc.) have a significant impact on production. The implementation efficiency of the optimized allocation scheme reaches $80 \%$, and the system response time is shortened to less than 8 seconds. The research findings provide efficient data - driven decision making support for oilfield development.