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

被引:26
|
作者
Khalaf, Ali Hussein [1 ,2 ]
Xiao, Ying [1 ,2 ,3 ]
Xu, Ning [4 ]
Wu, Bohong [5 ]
Li, Huan [6 ]
Lin, Bing [1 ,2 ]
Nie, Zhen [5 ]
Tang, Junlei [1 ,2 ,7 ]
机构
[1] Southwest Petr Univ, Sch Chem & Chem Engn, Chengdu 610500, Peoples R China
[2] Southwest Petr Univ, Inst Carbon Neutral, Chengdu 610500, Peoples R China
[3] Shenzhen Gas Corp Ltd, Shenzhen 518040, Peoples R China
[4] CNPC R&D DIFC Co Ltd, Dubai Int Financial Ctr, Unit OT 17-41-42,Cent Pk Tower, Dubai, U Arab Emirates
[5] CNPC, Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China
[6] Xinjiang Oilfield Co, Oil Prod Plant Xinjiang Oilfield Co 2, Xinjiang 834008, Peoples R China
[7] Tianfu Yongxing Lab, Chengdu 610217, Peoples R China
关键词
Corrosion in oil and gas industry; Artificial intelligence; Corrosion monitoring; Predictive modeling; Data analysis; CARBON-STEEL; PITTING CORROSION; ACOUSTIC-EMISSION; MAGNESIUM ALLOY; NEURAL-NETWORK; BEHAVIOR; MECHANISM; CHLORIDE; MODELS;
D O I
10.1016/j.engfailanal.2023.107735
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
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.
引用
收藏
页数:25
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