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.

Composite repairs have been applied to pipelines and piping systems for structural reinforcement after external corrosion. Such repairs may consist of glass or carbon fibers embedded in a matrix of epoxy. Typically, these repairs are hand applied using either wet lay-up systems or prefabricated rolls of composite sleeve. In some applications, pipeline continued corrosion growth under composite repairs were reported using Inline Inspection (ILI) which raises a concern about the integrity of the metallic piping under composite repairs. When continued corrosion is detected by ILI, a difficulty is typically faced due to the inability to measure pipeline remaining thickness under such repairs. To resolve this challenge, this paper will discuss multiple inspection and corrosion monitoring techniques for metal loss under composite repairs. To measure the pipeline wall thickness due to internal corrosion, one or more of the three (3) Non-Destructive Testing (NDT) technologies namely; Dynamic Response Spectroscopy (DRS), Multi-skip Ultrasonic (MS-UT) and digital radiography were evaluated and found capable. To monitor for external corrosion, a scheduled visual inspection of the composite repair would be the first inspection step. If the composite repair appears to be intact then the visual inspection would suffice and the repair should be acceptable to its design life. If the original defect is external corrosion and a scheduled visual inspection of the composite repair shows damage to the composite repair then inspection to assess the integrity of the substrate must be used before permanently fixing the composite repair. For this scenario, digital radiography or MS-UT are recommended to assess the condition of the substrate

Severe corrosion and cracking problems in Amine units and associated plants have been documented extensively. Mitigating these issues has had a significant economic impact in the oil and gas refining industries where this equipment is used to capture acid gases, such as CO2 and H2S. Currently, expensive austenitic stainless steels and other corrosion resistant alloys are being utilised to control these corrosion issues with varying degrees of success.
In this paper, the use of advanced cold applied liquid coatings, as an alternative solution to expensive alloys, are examined. In particular, the performance of a specific coating is considered (under a rigorous testing regime) in environments containing the amines MDEA and DGA with combinations of sour gases. The permeation properties of the coating were also examined by conducting a six month cold wall test using demineralised water at 150oC. It is found that this coating offers excellent resistance even when the testing environment respectively contains 38.8% H2S at a temperature and pressure of 150oC and 1000psi. Finally, some case studies are discussed which provide verification to the experimental results obtained.