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Residual stresses are self-equilibrating stresses that exist in materials and structures at the absence of instantaneous application of external loadings. In industrial manufacturing and fabrication processes, such stresses can be prominent and may lead to premature failures if uncontrolled. Such failures can be manifested in many forms including stress corrosion cracking, fatigue cracking or brittle facture. This paper is devoted to providing a comprehensive review on residual stress in the manufacturing and fabrication domain with a greater emphasis on welding based residual stresses. Three residual stress evolution mechanisms will be evaluated covering deformation driven stresses during manufacturing, thermally driven during welding and surface modifications such as grinding, carburizing and plating. In welding processes, the residual stresses in the cooling cycle are characterized using Gleeble testing illustrating the stress profiles as a function of temperature. The effect of residual stresses in welded structures will be discussed covering fatigue performance, brittle fracture and effect on Stress Corrosion Cracking resistance. To ensure residual stresses are effectively measured and quantified, a total of nine (9) destructive, semi-destructive and non-destructive residual stress measurement techniques are evaluated. A comparison and evaluation of four (4) common residual stress mitigation techniques are also discussed covering Ultrasonic Impact Treatment, High Frequency Mechanical Impact, shot peening and Post-weld Heat Treatment. The review discussion extends to four (4) factors towards impacting the residual stress magnitude and distribution covering material properties, welding process and clamping and preheating during welding.
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.
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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.