Risk Level Assessment and Impact Range Analysis of CCUS CO2 Pipeline Leakage Based on Machine Learning

In emergency decision-making for carbon capture, utilization, and storage (CCUS) CO2 pipeline leakage, risk levels and warning distances/impact ranges are often derived from different methodological systems—risk-matrix scoring versus mechanistic consequence modeling. Differences in threshold definitions and modeling assumptions make it difficult to align level assignment with distance boundaries for the same scenario, which in turn reduces the comparability and traceability of multi-scenario batch screening. To address this, this study proposes an integrated framework based on “threshold impact-distance calculation–risk-matrix mapping,” with physical consequence quantification as the main thread. A scenario library (N = 4320) covering phase state, leak aperture, operating conditions, and meteorological fields is constructed; impact distances corresponding to CO2 volume-fraction thresholds of 1%/4%/10% (R1%, R4%, R10%) are computed and then mapped to five RiskLevel classes under a unified rule set, enabling standardized synchronous outputs. The modeling tasks are formulated as RiskLevel classification and threshold-distance regression. Using a stratified 70%/30% train–test split, Extreme Gradient Boosting (XGBoost) is adopted as the primary model and compared with logistic regression (LR), support vector classification (SVC), ordinary least squares regression (OLS), and support vector regression (SVR). Results show that XGBoost achieves an accuracy of 0.806 and a macro-F1 of 0.825 for RiskLevel classification, with a recall of 0.631 for the high-risk classes (RiskLevel 4–5), and yields mean absolute errors (MAEs) of 95/62/41 m for R1%/R4%/R10% regression with coefficient of determination (R2) values of 0.795–0.814. Distributional analysis further indicates that threshold impact distances increase overall with higher RiskLevel, while dispersion becomes larger at higher levels. Accordingly, a parallel representation of “RiskLevel + multi-threshold rings” is recommended to support coordinated graded control and zoned warning delineation.