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

Abstract

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.

Semi-Quantitative Categorization Method for the Corrosion Behavior of Metals Based on Immersion Test

“…One study presented an electrochemical noise analysis method based on Adaboos and identified three categories for the corrosion behavior of metals: passivation (0), uniform corrosion (1), and pitting corrosion (2) [5]. In recent years, there has been a growing interest in employing machine learning techniques to classify corrosion based on alternative electrochemical measurements, including the Tafel extrapolation method [9][10][11][12][13][14][15].…”

Addressing Hydrogen Sulfide Corrosion in Oil and Gas Industries: A Sustainable Perspective

“…Enhanced data visualization tools also assist in presenting intricate corrosion data in a more user-friendly format, thereby expediting decision-making processes. This analysis aids in recognizing current corrosion trends and forecasting future risks, empowering proactive steps to mitigate substantial damage or downtime due to corrosion-related issues [113,114].…”

Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

“…In addition, the high stress and strain in the damaged areas of a pipeline affect the fatigue strength and cause low-pressure failure [9,10]. Fluctuating pressures that can cause fatigue or the effects of corrosion can also cause long-term problems [10][11][12]. There are several typical flaw shapes in pipelines, i.e., through-wall flaws, surface flaws, embedded flaws, edge cracks, and corner flaws [13].…”