Emerging AI technologies for corrosion monitoring in oil and gas industry: A comprehensive review

Ali Hussein Khalaf,Ying Xiao, Ning Xu,Bohong Wu, Huan Li,Bing Lin,Zhen Nie,Junlei Tang

ENGINEERING FAILURE ANALYSIS(2024)

引用 0|浏览4
暂无评分
摘要
Corrosion presents a daunting challenge to the oil and gas industry, resulting in substantial maintenance expenses and productivity losses. Conventional corrosion monitoring techniques often fall short in providing accurate and effective solutions. However, the advent of artificial intelligence (AI) in recent years has brought forth promising opportunities to revolutionize the corrosion monitoring process. In this comprehensive review, we explore various AI-driven ap-proaches for monitoring oil and gas industry corrosion. First, begins by examining and high-lighting corrosion and its detrimental effects on the industry. Second, delves into the factors influencing corrosion, offering insights into the complexity of this corrosion phenomenon. Third, explores the application of AI in developing corrosion prediction models, which offer the po-tential to proactively identify and mitigate corrosion-related issues. Fourth, sheds light on the applications of AI in data analysis, prediction modeling, and monitoring strategies, offering insight into the potential benefits of these technologies for real-time and proactive corrosion detection. Finally, addresses the challenges inherent in implementing AI-driven solutions for oil and gas industry corrosion monitoring. Issues such as data acquisition, data quality, algorithm selection, and model validation are discussed, along with the importance of human expertise integration in decision-making processes.
更多
查看译文
关键词
Corrosion in oil and gas industry,Artificial intelligence,Corrosion monitoring,Predictive modeling,Data analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要