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.

Storage tanks are the primary means for storing large volumes of liquids and gaseous products. Metal
loss from internal and external corrosion can reduce the service life of a tank. It is important to maintain
the integrity of steel storage tanks for safety, economic, and environmental reasons.
Saudi Aramco implemented three different scenarios of cathodic protection (CP) during bottom plate
replacement for three typical tanks. The tanks’ bottom plates were replaced based on thickness
measurements and visual inspection that revealed severe corrosion from soil. The main reason for
such corrosion was due to the asphalt layer underneath the tank. It was considered during the
replacement of the bottom plate to provide a reliable external (CP) and leak detection system. The
purpose of this paper is to demonstrate the experience gained from executing CP upgrades, and to
provide the best option to ensure integrity, cost saving and fewer shutdown periods.

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.