A sulfur recovery unit (SRU) train in a gas processing facility went under an emergency shutdown due to the failure of a reaction furnace waste heat boiler (WHB) tube. The failed tube had been in service for approximately 18 years. The failed tube was subjected to a number of metallurgical laboratory examinations in order to determine the damage mechanism and root cause(s) of the failure. Examinations included visual inspection, scale analysis, chemical analysis, metallographic examination and mechanical testing. The examination revealed internal corrosion thinning in the tube which led to rupture since the tube could no longer withstand the pressure. Metallographic examination revealed spheroidized microstructure indicating that the tube experienced high metal temperature. This is suggesting that something was impeding heat transfer between the tube and water. Scale analysis results from a sample collected from the tube internal surface indicated the presence of iron sulfide corrosion products. Based on the aforementioned findings, it was concluded that the corrosion thinning was caused by sulfidation. Sulfidation is one of the potential damage mechanisms in WHB tubes and is caused by reactive sulfur species as a result of the thermal decomposition of sulfur compounds at high temperatures (above 500oF). Failure contributing factors as well as corrective actions to prevent recurrence of such failure are discussed in this paper.

This paper describes a bridge coating operation and maintenance manual that was developed for the City of Vancouver which operates and maintains an inventory of 33 bridges with coated steel elements.

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.