Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets for which the oil and gas industry is not immune. Over the last few decades, both downstream and upstream industry segments have recognized the magnitude of CUI and challenges faced by the industry in its ability to handle CUI risk-based assessment, predictive detection and inspection of CUI. It is a concern that is hidden, invisible to inspectors and prompted mainly by moisture ingress between the insulation and the metallic pipe surface. The industry faces significant issues in the inspection of insulated assets, not only of pipes, but also tanks and vessels in terms of detection accuracy and precision. Currently, there is no reliable NDT detection tool that can predict the CUI spots in a safe and fast manner. In this study, a cyber physical-based approach is being presented to identify susceptible locations of CUI through a collection of infrared data overtime. The experimental results and data analysis demonstrates the feasibility of utilizing machine-learning techniques coupled with thermography to predict areas of concern. This is through a simplified clustering and classification model utilizing the Convolutional Neural Networks (CNN). This is a unique and innovative inspection technique in tackling complex challenges within the oil and gas industry, utilizing trending technologies such as big data analytics and artificial intelligence.
The use of corrosion sensors for remote monitoring of infrastructure assets has become more frequent in recent years. Corrosion sensors utilizing the electrical resistance (ER) method have been developed, in which the resistance of a test sample is used to infer thickness change and hence corrosion rate. ER corrosion sensors have been deployed on various structures including marine wharves, bridges and coal processing facilities. On some marine wharf structures and some marine coastal bridges the performance of jacketed petrolatum-based tape wrapping systems on steel piles has been assessed including on steel piles suffering from accelerated low water corrosion (ALWC) and microbiologically influenced corrosion (MIC). The paper provides some relevant discussion of the corrosion and its mechanisms prevalent to marine wharf and bridge steel piles in Eastern Australia, Southern Australia and Western Australia as well as the pile wrapping/jacketing systems installed and being performance monitored in-situ. Details are provided of the corrosion sensors. An assessment of the results obtained to-date for up to 3 years of in-situ exposure has been made.