Electric Resistance Welded (ERW) pipes X60M / X65M API 5L PSL2, with resistance to ductile fracture propagation as per API 5L PSL2 Annex G [1] are achieved not only by setting the proper welding parameters and the steel cleanliness, but also by a combination of metallurgical processes affecting the final weld line and HAZ microstructure. The steel chemistry is the starting point to minimize the presence of inclusions, central segregation and the toughness impairment due to harmful elements, S, P, etc. on the pipe body, with a given casting and rolling technology. During the welding process, the right parameters combination is needed to avoid cold weld, penetrators, and other weld imperfections. At the last stage, the Seam Heat Treatment (SHT) has to be adjusted in a way that the steel response to the thermal cycles leads to the compliance of mechanical requirements at the weld line and Heat Affected Zone (HAZ). This heat treatment is performed through electromagnetic induction using several coils, which allows it to have a rapid and localized heating of the HAZ into the austenitic region, and that is followed by air cooling. The objective is to refine the structure and to eliminate brittle constituents around the weld line. As the SHT strongly affects the weld performance, the optimum processing conditions such as austenitization temperature and cooling rate may not be the same for all steel chemistry, and has to be carefully selected. The capability to model the thermal cycle after the ERW process and the understanding of the metallurgical behavior of different steel chemistries and dimensional configuration becomes the main target of any ERW pipe manufacturer aiming supply reliable Line Pipes as per API 5L PSL2 Annex G. In this work, a numerical thermal model of the SHT is presented along with validation and simulation results. A summary of metallurgical thermal cycle simulations by means of a Gleeble® 3500, applied on different steels is also included.

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.

Steel pipeline is the optimum choice for transporting oil and gas due to its excellent strength,
material properties and cost. Some pipeline services require special attention to avoid
corrosion or erosion, e.g., corrosive water injection systems. To address this, internal liners
have been introduced, including cement-lined to protect the pipelines from such conditions.
Even though cement-liners enhance the reliability of the pipelines, there are still challenges
related to inspection. The inspection of cement-lined pipelines is difficult with in-line
inspection tools (ILI) due to surface roughness of the cement, which impacts the movement.
Also, the cement lining is too thick for the sensors of the ILI tool to measure the steel
thickness through the liner.
Cement-lined pipelines are frequently used for water injection system facilities where
common inspection techniques cannot be used due to inherent limitations. As safety,
reliability and continuity are important at Saudi Aramco operations, the team spare no effort
to ensure the integrity of these pipelines utilizing different inspection techniques. In 2017,
electromagnetic acoustic transmission (EMAT) inspection technology was utilized for the first
time on cement-lined pipelines at the water injection facilities. This paper describes the
capability and successful deployment of EMAT inspection technology.