# Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines
## 6 Fast Assessment of Failures and Inspection Planning  
In the technically demanding submarine environment, subsea pipelines are susceptible to various damage threats, which may lead to catastrophic failures of the disastrous economic and environmental variety. For instance, the waves and currents can lead to a scouring of the soil underneath the pipelines and free span problems where a segment of the pipeline becomes unsupported except at the two ends of the free span length. Free span lengths above the allowable design limit can result in fatigue damage through vortex-induced vibrations (VIV) [65; 85; 86; 87]. Corrosion and erosion can occur from chemical attacks and abrasion due to the internal fluids containing abrasive sand particles travelling at high velocities. Corrosoions, when combined with tensile stresses, can lead to stress cracking and leakage in subsea pipelines [46; 47; 48; 52; 65; 88].  
To detect and correct malfunctions of the physical assent, the digital twin applies ML, DL, and AI algorithms. It was revealed in a survey that asset monitoring and maintenance is the most anticipated application area for the digital twin [5]. The fitness-for-service (FFS) of the physical asset is continuously monitored by the digital twin to identify potential failures. The big-data analytics capabilities of the digital twin can monitor the asset and send warnings to the responsible parties.  
Digital twins provide high-fidelity accurate models and keep updating through the lifecycle of the pipelines with gathered data from sensors and inspections. Thus, they can reproduce the current state of the pipelines in the virtual space. Comparisons between digital twin simulated data and collected data can help determine the failure mode. One advantage of the state monitoring by the digital twin is that users can monitor the product state from any remote location through the unique identifier incorporated by the digital twin [31].  
A computer vision-based digital twin model for real-time corrosion inspection was proposed in [89]. The CNN algorithm was used for the automated corrosion identification and classification from the remotely operated vehicles (ROV) images and ILI data. Based on the corrosion assessment by the digital twin, predictive and prescriptive maintenance strategies are recommended.  
During the service life of subsea pipelines, failure mechanisms such as external/internal sheath damage, fatigue damage, or corrosion may arise. Therefore, high OPEX is consumed to confirm the fitness of the system [90].  
Figure 9: A predictive maintenance model based on sensor data analytics [4].  
Based on the reliability assessments [91; 92; 93], the risk-based inspection planning methodology has been used for the integrity management of subsea pipelines. The steps of the methodology include (1) data gathering, (2) development of risk criteria, (3) probability of failure (PoF) and consequence of failure (CoF), and (4) risk evaluation and enhanced inspection strategy [94; 95; 96; 97; 98; 99]. Bayesian network (BN) and genetic algorithm (GA) were used to develop a framework for the inspection decision-making for subsea pipelines [100]. Using digital twins, risk target data analysis and risk estimation by prognostic and ML techniques were performed [101; 102].