Enhanced predictive maintenance for subsea pipelines using multi-modal data

Subsea pipeline failures pose a critical challenge for the oil and gas industry, causing billions in annual losses alongside significant environmental and safety risks. Traditional integrity management relies on scheduled inspections or reactive repairs, often too late due to a lack of real-time data. This paper introduces a digital twin framework using artificial intelligence to shift pipeline integrity management from reactive to predictive. The proposed system integrates data from pressure, temperature, flow, acoustic, and vibration sensors. Using an Isolation Forest machine learning algorithm, it detects anomalies and analyses sensor correlations over time. It produces a health score (0–1) and predicts Remaining Useful Life, enabling condition-based interventions tied to actual equipment states rather than fixed schedules. Results show high anomaly detection specificity. F1-score performance on the synthetic dataset indicates sensitivity requires further calibration, particularly for early onset degradation. A real-time dashboard visualises pipeline health and triggers automated alerts when degradation trends are identified. Analysis of 365 days of simulated data confirms the framework detects both gradual wear and sudden failures more effectively than traditional methods. This work contributes practical methodologies for applied machine learning in offshore infrastructure management, demonstrating how multi-modal data analytics and digital twin strategies can support condition-based integrity management. The framework has the potential to maximise equipment availability while minimising operational costs and integrity-related risks. However, field validation against real operational data is necessary before deployment to confirm the methodology’s effectiveness in practice.