Experimental Approach to Monitoring the Degradation Status of Pipelines Transporting Hydrocarbons

Yahi, Sarah; Bensmaılı, A.; Haddad, Ahmed; Benmohamed, Manel

doi:10.33422/ejest.v4i2.605

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…SCC failures on the TransCanada (now TC Energy)-operated Keystone Pipeline occurred in North Dakota, USA, in 2007, which caused a substantial oil spill. In 2016, three North American pipeline incidents led to the leaking and spilling of more than 2000 metric tons of gas and oil [36,37]. More than 50% of failures on gas pipelines are due to SCC [38].…”

Section: Stress Corrosion Cracking (Scc) Failure Eventsmentioning

confidence: 99%

Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

Hussain,

Zhang,

Chaudhry

et al. 2024

Machines

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Pipeline integrity and safety depend on the detection and prediction of stress corrosion cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion-monitoring techniques are used. The observed data exhibit characteristics of nonlinearity, multidimensionality, and noise. Hence, data-driven modeling techniques have been widely utilized. To accomplish intelligent corrosion prediction and enhance corrosion control, machine learning (ML)-based approaches have been developed. Some published papers related to SCC have discussed ML techniques and their applications, but none of the works has shown the real ability of ML to detect or predict SCC in energy pipelines, though fewer researchers have tested their models to prove them under controlled environments in laboratories, which is completely different from real work environments in the field. Looking at the current research status, the authors believe that there is a need to explore the best technologies and modeling approaches and to identify clear gaps; a critical review is, therefore, required. The objective of this study is to assess the current status of machine learning’s applications in SCC detection, identify current research gaps, and indicate future directions from a scientific research and application point of view. This review will highlight the limitations and challenges of employing machine learning for SCC prediction and also discuss the importance of incorporating domain knowledge and expert inputs to enhance the accuracy and reliability of predictions. Finally, a framework is proposed to demonstrate the process of the application of ML to condition assessments of energy pipelines.

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Section: Stress Corrosion Cracking (Scc) Failure Eventsmentioning

confidence: 99%

Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

Hussain,

Zhang,

Chaudhry

et al. 2024

Machines

View full text Add to dashboard Cite

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Analysis, Assessment, and Mitigation of Stress Corrosion Cracking in Austenitic Stainless Steels in the Oil and Gas Sector: A Review

Vakili,

Koutník,

Kohout

et al. 2024

Surfaces

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This comprehensive review examines the phenomena of stress corrosion cracking (SCC) and chloride-induced stress corrosion cracking (Cl-SCC) in materials commonly used in the oil and gas industry, with a focus on austenitic stainless steels. The study reveals that SCC initiation can occur at temperatures as low as 20 °C, while Cl-SCC propagation rates significantly increase above 60 °C, reaching up to 0.1 mm/day in environments with high chloride concentrations. Experimental methods such as Slow Strain Rate Tests (SSRTs), Small Punch Tests (SPTs), and Constant-Load Tests (CLTs) were employed to quantify the impacts of temperature, chloride concentration, and pH on SCC susceptibility. The results highlight the critical role of these factors in determining the susceptibility of materials to SCC. The review emphasizes the importance of implementing various mitigation strategies to prevent SCC, including the use of corrosion-resistant alloys, protective coatings, cathodic protection, and corrosion inhibitors. Additionally, regular monitoring using advanced sensor technologies capable of detecting early signs of SCC is crucial for preventing the onset of SCC. The study concludes with practical recommendations for enhancing infrastructure resilience through meticulous material selection, comprehensive environmental monitoring, and proactive maintenance strategies, aimed at safeguarding operational integrity and ensuring environmental compliance. The review underscores the significance of considering the interplay between mechanical stresses and corrosive environments in the selection and application of materials in the oil and gas industry. Low pH levels and high temperatures facilitate the rapid progression of SCC, with experimental results indicating that stainless steel forms passive films with more defects under these conditions, reducing corrosion resistance. This interplay highlights the need for a comprehensive understanding of the complex interactions between materials, environments, and mechanical stresses to ensure the long-term integrity of critical infrastructure.

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Experimental Approach to Monitoring the Degradation Status of Pipelines Transporting Hydrocarbons

Cited by 2 publications

References 23 publications

Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

Analysis, Assessment, and Mitigation of Stress Corrosion Cracking in Austenitic Stainless Steels in the Oil and Gas Sector: A Review

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