Data-Driven Analysis of the Effectiveness of Water Control Measures in Offshore Horizontal Wells

Implementing effective water control measures in horizontal wells is essential for sustaining stable production in offshore oilfields. However, due to complex reservoir geological characteristics and diverse water control technologies, significant variability exists in the effectiveness of such measures, posing challenges for strategy selection. To address this gap, this study establishes a comprehensive and standardized multi-dimensional indicator system for describing treated wells, integrating geological, operational, and production parameters—an aspect seldom systematized in previous research. A major innovation of this work lies in developing a hybrid correlation evaluation framework that combines Pearson, Spearman, and canonical correlation analyses, enabling a more robust quantification of the relationships between influencing factors and water control effectiveness. This framework not only identifies the dominant indicators but also mitigates the limitations of single-method correlation analysis. Building upon these insights, the study proposes a machine learning-driven prediction system using Random Forest and Gradient Boosting algorithms, achieving classification accuracy exceeding 80% and effective regression prediction of measure duration. This represents a practical advancement over traditional empirical or single-feature decision approaches. The results reveal that overall field water cut percentage, 30-day pre-treatment water cut percentage, and daily liquid production are the key indicators governing treatment performance. Furthermore, water control measures in edge water reservoirs show significantly better performance than those in bottom water reservoirs. The developed prediction model provides a generalizable, data-driven decision-support tool, offering significant value for optimizing water control technologies in offshore horizontal wells.