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.

Hydrocracking and other refinery hydroprocessing units have a common goal to convert organic sulfur compounds to hydrogen sulfide (H2S) that can be removed, thereby producing low-sulfur refinery products. Corrosion and equipment degradation risks range from high-temperature hydrogen attacks (HTHA) to ammonium bisulfide and ammonium chloride corrosion in the downstream heat recovery and fluid separation equipment.
This paper provides an overview of corrosion management principles that can be applied to reduce operating risks in new and existing units, focusing equipment susceptible to ammonium bisulfide (NH4HS) and ammonium chloride (NH4Cl) corrosion. Best practices for materials selection, as well as designing for corrosion management through adequate provision of corrosion management related instrumentation and sampling points are covered.

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.