Proactive Water Management Using Machine Learning: From Monitoring to Field-Wide Optimization

Executive Summary The increasing challenge of managing water production in horizontal wells requires a shift from traditional reactive workflows to intelligent, data-driven strategies. This manuscript presents an integrated machine learning framework designed to proactively predict water breakthrough and recommend optimal intervention strategies, thereby enhancing dry oil production and reducing operational inefficiencies. Using a synthetic reservoir model, we develop a predictive system comprising three key components: a probabilistic water breakthrough detection engine, a decision tree-based rule engine, and a reinforcement learning agent trained on operational complexity. Our first model predicts the probability of water breakthrough within a 30-day window using ensemble tree methods trained on dynamic and static well data. This early warning signal feeds directly into two downstream models that recommend suitable water management interventions; ICV optimization, ICD adjustment, zonal shutoff or sidetrack based on reservoir characteristics, completion configuration, and remaining oil potential. A decision tree offers deterministic recommendations based on physical thresholds, while the reinforcement learner formulates optimal actions by balancing complexity-adjusted rewards. The model performance was validated both statistically and spatially. Feature analysis using SHAP values and correlation heatmaps confirms the physical plausibility of selected predictors such as vertical distance to water, ESP load decline, ICD imbalance, and ICV configurations. Results show that the ML framework delays water breakthrough by an average of 45 days, increases successful intervention rate by 12%, and reduces unnecessary interventions by 25% compared to conventional practices. These outcomes are validated against synthetic reservoir simulation runs and historical patterns of breakthrough and intervention success. This study demonstrates the power of coupling physics with interpretable AI to enable proactive water control strategies. The modular structure of the proposed framework, its emphasis on domain knowledge, and its clear mapping to operational outcomes position it as a field-deployable solution with measurable impact on production sustainability and operational efficiency.