Seabed Digital Twins: Enhancing Offshore Megaproject Planning Through Real-Time Survey Intelligence

Abstract Offshore megaprojects operate in dynamic marine environments where seabed conditions can evolve rapidly, introducing uncertainty into engineering design, installation planning, and lifecycle asset management. Conventional offshore survey workflows rely on static datasets acquired at discrete project milestones, often resulting in repeated survey campaigns, conservative design assumptions, and increased project risk. This paper presents a seabed digital twin framework that integrates hydrographic, geotechnical, and metocean datasets into a continuously updated, decision-ready digital representation of seabed conditions. The framework combines repeat multibeam echo sounder (MBES) and sub-bottom profiler (SBP) surveys with real-time data streams from autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), autonomous surface vessels (ASVs), and IoT-enabled subsea sensors. Cloud-based data integration, quality control, and analytics enable near real-time seabed change detection, predictive modeling, and lifecycle decision support. Application of the framework across multiple offshore development case studies demonstrates measurable benefits, including up to 40% reduction in re-survey costs, approximately 25% improvement in pipeline and cable routing efficiency, earlier identification of seabed geohazards, and improved confidence in decommissioning planning through accurate seabed evolution modeling. The results confirm that seabed digital twins transform survey data from static deliverables into persistent digital assets, improving project economics, operational safety, and environmental stewardship. This approach establishes a scalable model for proactive seabed management across offshore megaproject lifecycles and supports broader digitalization and energy transition objectives.