The Main Control Factors and Productivity Evaluation Method of Stimulated Well Production Based on an Interpretable Machine Learning Model

Low-permeability waterflooding reservoirs face numerous challenges, including low productivity per well, inadequate formation pressure maintenance, poor waterflood response, and low water injection utilization efficiency. Illustrated by Bai 153 Block in the Changqing Oilfield, the primary concern has shifted in recent years from fracture water breakthrough to formation blockages. Currently, low-yield wells (≤0.5 t) constitute a significant proportion (27.5%), with a recovery factor of only 0.41%. The effectiveness of stimulation treatments is influenced by reservoir properties, treatment types, process parameters, and production performance. Selecting candidate wells requires collecting and analyzing data such as individual well block characteristics. Evaluating treatment effectiveness involves substantial effort and complexity. Early fracturing treatments exhibited significant variations in effectiveness, and the primary controlling factors influencing fracturing success remained unclear. This paper proposes a big data analysis-based method for evaluating stimulation effectiveness in low-permeability waterflooding reservoirs. Utilizing preprocessed geological, construction, and production data from the target block, an integrated application of the Random Forest algorithm and Recursive Feature Elimination ranks the importance of factors affecting treatments and identifies the block’s main controlling factors. Using these factors as target parameters, a multivariate quantitative evaluation model for fracturing effectiveness is established. This model employs the Pearson correlation coefficient method, Recursive Feature Elimination, and the Random Forest algorithm. Results from the quantitative model indicate that the primary main controlling factors that significantly affect post-fracturing oil increment are production parameters, geological parameters such as vertical thickness, fracture pressure, and oil saturation; engineering parameters such as sand ratio, blowout volume, and fracturing method; and production parameters such as pre-measure cumulative fluid production, production months, and pre-measure cumulative oil production, which are most closely related to post-fracturing oil increment. These parameters show the strongest correlation with incremental oil production. The constructed quantitative model demonstrates a linear correlation rate exceeding 85% between predicted fracturing stimulation and actual well test production, verifying its validity. This approach provides a novel method and theoretical foundation for the post-evaluation of oil increment effectiveness from stimulation treatments in low-permeability waterflooding reservoirs.