From Data to Decisions: A Digital Twin Approach for Subsea Integrity Management

Scope/Goals: Subsea production systems are vital to offshore oil and gas extraction, especially as they expand to meet deep-water field demands. Their remote locations and intricate designs complicate maintenance and monitoring. This paper presents a web-based solution that integrates semantic search, generative artificial intelligence, and 3D visualization. Its main goal is to support the operational health management of subsea equipment, contributing to the reduction of unplanned downtime costs and promoting compliance with strict regulatory standards. Methods, Procedures, Process: The proposed system unifies scattered, often unstructured data-such as PDFs, logs, and databases-through a semantic search engine powered by generative AI. Users can retrieve both textual and graphical information via natural language queries, eliminating the need for specialized query languages. 3D point clouds from laser scans are automatically compared with original CAD models to visually highlight discrepancies. Novel computational geometry algorithms automatically detect integrity anomalies. A 3D digital twin module combined with interactive dashboards allow for intuitive and data-rich analysis and improved decision-making for subsea equipment management. Results, Observations, Conclusions: The developed system contributes significantly to asset management efficiency. Intelligent data retrieval markedly reduces the time engineers spend hunting through disparate files and databases, while automated geometry checks capture subtle anomalies such as corrosion, anode depletion, or dimensional shifts often missed by manual inspections. The integrated 3D digital twin further enhances situational awareness by comparing as-built scans to design models, revealing deviations in color-coded 3D environments for immediate assessment. As a result, operators can address potential failures earlier, preventing costly unplanned shutdowns and strengthening regulatory compliance. This holistic system also promotes cross-functional collaboration by offering multi-user sharing of analysis. Overall, the approach contributes to reduce operational risk, lower costs, and accelerate maintenance planning cycles, offering substantial benefits for the oversight and integrity of subsea production systems. Novel/Additive Information: In addition to data integration, the system provides intelligent search capabilities and geometry-based anomaly detection—combining textual knowledge with 3D scanning. Its ability to automatically identify integrity issues and correlate them with engineering data enhances current methodologies, setting a new standard for proactive subsea asset management and lifecycle analysis in the oil and gas industry.