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.

Recently the oil, gas and petrochemical sectors have been facing together safety, environmental and mechanical integrity regulations as well as challenges associated with the need for cost reduction to improve competitiveness. Therefore, continual inspection and corrosion control health assessments and investigations are key towards sustaining reliability and availability together with value creation through avoiding unplanned production loss and asset failures. The present paper discusses an inspection and corrosion control technical assessment performed on thirteen (13) subsea flowlines. These flowlines supply wet sour gas feed from two offshore fields, gather through two 36 in” trunk lines. In order to meet the health and integrity objective, the assessment covers a review on the susceptibility and control of three (3) damage mechanisms using available literature covering field and empirical data. In addition, a review and discussion on the available and required inspection methods to combat the susceptible damage mechanisms are performed. This review is extended to an exploration and evaluation of (6) inline inspection techniques and two (2) remotely operated vehicles (ROV) to complement damage mechanism inspection methods.

Risk-based inspection is a business process and improvement tool to enhance asset performance as well as asset life. This paper intends to discuss risk-based coating inspection parameters to enhance coating/lining life and prevent and or mitigate the corrosion threat to assets. This paper further discusses each key aspect of protective coating/lining inspection parameters and its intended purpose.