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.

Corrosion continues to be a threat to the petroleum industry. It risks people’s lives, assets integrity and the environment. These risks are mitigated by different means such as selection of appropriate materials, chemical treatment, cathodic protection, protective coatings, and process control. One of the most common corrosion control measures is the use of corrosion inhibitors. This is a cost-effective option that can be applied to upstream, mid-stream and downstream facilities. This has driven the research institutes and the chemical manufacturers to invest on developing corrosion inhibitor chemistries for field-specific applications. In spite of all the efforts being put, there are still many important aspects about corrosion inhibition treatment that need to be researched for a better understanding of the chemicals’ performance, monitoring, laboratory testing, and field application. This paper highlights knowledge gaps to invite focused research to help bridging the gaps between operators, research institutes and developing companies. These gaps are classified in four main areas: Field Monitoring, Facility Design, Laboratory Testing, and Simulation & Prediction.

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.