Digital Twins for Condition Monitoring in Offshore Facilities: Opportunities and Gaps

Harsh and extreme marine and environmental conditions have a great impact on offshore energy systems, oil and gas platforms, and wind farms. Extreme waves, corrosion from seawater/microorganisms, and the remote nature of these facilities demand continuous monitoring to prevent costly total shutdowns or accidents caused by corrosion and wave-induced vibrations. Condition monitoring of offshore facilities involves using multi-source data fusion from sensors to enable real-time data analytics, coupled with AI models to obtain facility insights. Digital twin provides integrated real-time analysis, alarms, and an AI-based approach that represents a paradigm shift in condition monitoring for offshore facilities by offering a virtual replica that can simulate real-time degradation.

We reviewed traditional methods and compared them to recent advances that fused physics-based models and AI into a digital twin. We review technologies such as blockchain, CNN-based algorithms, physics-augmented AI, physics-informed neural networks (PINNs), graph neural networks (GNNs), federated learning, and their suitability in creating offshore facilities digital twins. We investigated digital twin architectures, implementation and integration strategies, challenges, and provided a forward-looking roadmap.

Our review reveals that traditional approaches often struggle with multi-fault complexity and data corruption from harsh environments, leading to higher false positives/negatives in fault detection and limited predictive accuracy. In contrast, digital twins integrating physics-informed models achieve superior predictive performance, due to their robust handling of noisy/multi-source data. The digital twins approach reported lower root-mean-square errors and better generalisation in corrosion-fatigue and structural fatigue scenarios, leading to improved forecasting accuracy for unplanned downtime, maintenance costs, and the useful lives of facilities and equipment. Our review also highlighted a research-to-practice gap in the scalability of the proposed solution, data privacy, and cross-operator data-sharing capabilities.