Big data analysis of waterflood performance in mature conventional oilfields in Eastern China

Most conventional oilfields in Eastern China with waterflood operations have reached ultra-high water cut in recent decade. The high water injection demand and produced water treatment cost pose significant environmental threats. Therefore, optimizing waterflood performance is key to improving production efficiency. This study performs data analysis on waterflood operations of all the oilfields operated by Sinopec across Eastern China. The production mechanisms and most effective operations for different reservoir types at diverse production stages are identified using data-driven methods. Random Forest models (RFMs) are constructed and integrated with Shapley Additive exPlanations (SHAP) analysis to quantify the weights and patterns of key geological and engineering features. A comparison of the estimated ultimate recovery factors for different blocks shows that geological factors play dominant roles in medium-to-high permeability reservoirs while development parameters are more critical for low-permeability reservoirs. The analysis of temporal data regarding field development and production history is conducted to select oil production-increasing operations in blocks. The results show that the most influential field operations vary for the diverse production stages, and well patterns should be carefully designed to improve production efficiency and reduce ineffective water circulation.