# Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines
## 4 Update of the Digital Twin with Information Acquired in Inspections  
Notable offshore production regions include the Gulf of Mexico (GoM), the North Sea, Brazil, West Africa, the Persian Gulf, Atlantic Canada, the Gulf of Thailand, the East and the South China Sea, the Caspian Sea, and the Southern and Western Australia [65]. The GoM has the greatest number of offshore pipelines installed, followed by the North Sea. From 1952 to 2017, more than 72,000 km of pipelines have been installed in the GoM, and about 42,000 km of them are still active. The North Sea has the second-largest pipeline network with approximately 45,000 km of pipeline installed since 1966 [66]. However, the issues and obstacles make the inspection and monitoring of pipelines a challenging task. Problems may occur throughout the life of the pipe, and the environment of service is full of potential dangers [65]. In the use of digital twin technology in offshore structures, the twin model must be updated during the service life with the data of corrosion and other structural degradation gathered during service life inspections and could be updatable according to the accidental damages. A pipe segment is often coated during the manufacturing process to protect it against corrosion or abrasion. Additional layers of coating can be added, depending on the requirements. Some subsea pipelines have outer concrete coatings for protection against corrosion and impacts and weight stabilization [67; 68]. PIP designs may also be implemented with additional layers for protection and thermal insulation [69; 70; 71]. In addition to the inspection of the primary metallic body, the coating layers may also require inspections for damage and debonding from the pipelines. Depending on the task and the technology used in the inspection, part of the pipelines may need to be stripped of coatings to be fully inspected. Based on an investigation by The US Department of Transportation, the major failures of subsea pipelines were categorized into five categories: mechanical, operational, corrosion, natural hazards, and third party. All of these possible failures necessitate the routine inspection of the pipeline and the use of permanent monitoring technologies. Nowadays, one of the challenges for subsea pipeline inspection is the extreme water depths. Many well proved inspection technologies cannot be delivered to the pipeline without costly equipment and procedures. In the cases of multi-layered pipelines, the cost of stripping away the coatings for routine inspections in deepwater is impractical. In-line inspection (ILI) may be used to inspect the pipeline and the inner from the inside. Pigging is one of the ILI techniques in which devices referred to as \"pigs\" or scrapers are inserted into pipelines to perform inspection activities. Pigging can be conducted on a variety of pipeline sizes without having to stop the flow of material through the line. Given the increasing capacity of satellite links, the potential of cloud computing and deep learning (DL) algorithms, the digital twin models with millimetre precision move the subsea asset management into a new era, enabling engineers to incorporate less margin into their remediation advice which translates into more targeted maintenance. A solution to processing high-resolution data collected from pipeline inspections was presented by the geo-data specialist company Fugro using in-house Remote Observation, AutomaticModelling, Economic Simulations (Roames) technology [71]. The resultant product is a web-based service that enables pipeline inspection data to be uploaded to the secure cloud environment in near real-time, processed using ML, verified by experts onshore and visualized in an intuitive 4D web viewer. This approach can greatly reduce the cost of infrastructure management practices, lower the risk exposure, and contribute to the extended life of the pipeline. The project was executed which employed a Kongsberg Hugin autonomous underwater vehicle (AUV) operated at an average of 3 m altitude and 4.5 kt [72].