A State-Aware Multi-Evidence Residual Scoring Method for Early Overflow Warning Using Real-Time Mud-Logging Data

Overflow early warning during drilling requires timely interpretation of noisy, incomplete, and operation-dependent mud-logging streams. Single-threshold alarms are easy to deploy but are sensitive to pump changes and surface-tank operations, whereas generic anomaly scores may respond to operational disturbances that are not well-control events. This study develops a state-aware multi-evidence residual scoring method that combines flow imbalance, pit-volume variation, mud-density response, pressure response, gas response, and normal-response residuals into a bounded event risk score. Missing evidence is handled by weight renormalization, and operating-state confidence and surface-disturbance suppression are introduced to reduce false warnings during non-hazardous operations. Five field replay cases were analyzed, including three confirmed overflow events and two pseudo-overflow disturbances. The proposed method detected all three confirmed overflow events, reduced the pseudo-overflow false alarm rate from 1.000 for the rule-based, residual-only, and Isolation Forest baselines to 0.500, and yielded an average warning time difference of −8.44 min relative to the field-interpreted event time. Additional sensitivity analyses showed that the selected threshold–duration setting lay in a non-isolated high-recall region, but also that excessive score smoothing or simultaneous loss of density, gas, and pressure evidence reduced detection reliability. The results support a reproducible event-level workflow for overflow warning from real-time mud-logging data, with the current evidence bounded by the small number of replay cases and the absence of independent multi-well validation.