A digital twin integrated smart-liner for visualization monitoring of oil and gas pipeline infrastructure

This study presents an innovative smart-liner system that enhances the safety and efficiency of oil and gas transportation and storage systems by integrating polymer composite liners with distributed fiber optic sensors. The system provides continuous data collection from the adhesive curing process to mechanical deformations and damage over the infrastructure's lifespan, and accurately reflects the infrastructure's condition. Experimental tests conducted on a steel substrate validate the feasibility of the smart-liner, highlighting the effectiveness of developed data analysis and visualization techniques. These techniques enable a comprehensive structural health monitoring system to accurately locate, visualize, and quantify strain changes within the infrastructure. The smart-liner features the ability to collect continuous data across the entire protected surface, enabling the creation of a dynamic digital twin model that evolves alongside the infrastructure. The mechanical responses of the liner system were simultaneously simulated using the developed visualization techniques, resulting in highly accurate reconstructed digital twin model, with an R2 value exceeding 0.95. In addition, the smart-liner utilizes residual strain from the curing process to forecast potential defect zones; this ability provides critical insights that mitigate defects during design and manufacturing stages. Furthermore, a parametric study on sensor path spacing further refines sensor deployment strategies, enhancing the accuracy of shape reconstructions. Overall, the integration of DFOS and digital twin technologies marks a major advancement in infrastructure monitoring, promoting more reliable and resilient energy transportation and storage systems.