Abstract
As oilfield development enters a complex stage, water injection, as a key method for enhancing oil recovery, is often significantly influenced by the complex geological characteristics of reservoirs, making precise prediction and optimization challenging. This study proposes a reservoir geological response prediction model based on big data analysis to address this issue. A machine learning-based prediction model is developed to simulate and evaluate reservoir dynamics under different water injection strategies by systematically collecting and integrating extensive geological, production, and water injection-related data. The model development process involves key steps such as data preprocessing, feature engineering, algorithm selection, and parameter optimization. Results show that the model demonstrates high accuracy in predicting reservoir dynamic behavior and effectively guides the optimization of water injection strategies, thereby significantly improving oilfield development efficiency and economic benefits. Furthermore, this study explores challenges encountered during model development, including data quality, algorithm selection, and computational complexity, while also highlighting the broad application prospects of big data technology in oilfield management. This study provides theoretical support and practical guidance for formulating scientific water injection strategies, contributing to extending oilfield development cycles and enhancing resource utilization efficiency.
