The paper considers best practice to realise the optimum combination of strength, toughness,
corrosion resistance and radiographic integrity in UNS S32760 pipe girth welds made using the
GTAW process.
Aspects of fit up, tacking, root gap are considered. The effect of weld heat input and heat input
control through the thickness of the joint, welding technique, inter pass temperature control and
the use of different combinations of shielding and backing gasses on corrosion resistance of
joints is presented. Current specification, procedure and welder qualification requirements are
discussed, as is the need for supplementary testing, in particular quantitative microstructural
evaluation.

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.

During the construction of a 56km long 16 in. carbon steel sour gas pipeline, repetitive surface
preparation failures were detected during visual inspection of pipeline girth weld internal surface prior to
coating application. Such failures represented 67% of the total pipeline girth welds and were manifested
by excessive sharp-edges at the root pass. To identify the failure causes, an investigation was
performed through reviewing the pipeline, fabrication and coating application specifications and
procedures, quality control records and performing an extensive visual inspection through an advanced
video robotic crawler on all pipeline girth welds made. Upon investigation analysis, the failures were
caused by sharp-edges in the root pass which were attributed to improper practices during
manufacturing, field fabrication and pre-coating quality control. The failure analysis indicated that the
mechanized Gas Metal Arc Welding process, with the parameters used, was not suitable for internal
girth weld coating application. In addition, a more stringent requirement should be applied to the
acceptable pipe-end diameter tolerance and pre-coating quality control to ensure absence of similar
premature surface preparation failures. The pre-coating quality control can be improved through
utilization of robotic laser contour mapping crawler for precise detection and sizing of unsatisfactory
surface weldment defects, including sharp edges.