Intelligent Prediction of Lost Circulation and Gas Invasion in Cementing Operations Based on ACO-SWXGBoost

Lost circulation and gas invasion during cementing operations pose significant threats to safety and efficiency, making accurate prediction and timely early warning a critical concern in the industry. This paper proposes an intelligent early warning model based on ant colony optimization (ACO) and an enhanced version of XGBoost (SWXGBoost). The model extracts both raw features and slope-based variations of key parameters—flow rate, pressure, and density—to enhance representation, incorporates a sliding window and time decay mechanism to capture dynamic patterns, and leverages ACO to optimize hyperparameters for improved predictive performance. Experimental results based on 1800 field samples show that ACO-SWXGBoost achieves superior performance compared to mainstream baseline models, with a micro-F1 of 0.955, precision of 0.949, and recall of 0.961. On average, the model outperforms XGBoost, LightGBM, random forest, and decision tree by 5.55%, 7.28%, and 6.48% on the three respective metrics. Furthermore, SHAP analysis confirms a strong alignment between model predictions and field knowledge, highlighting the critical role of pressure, flow rate, and density in anomaly identification. The proposed approach is readily deployable within real-time monitoring systems, offering a reliable and interpretable solution for intelligent risk detection and early warning in cementing operations.