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

• Published in: Engineering failure analysis Vol. 155; p. 107735
• Main Authors: Hussein Khalaf, Ali, Xiao, Ying, Xu, Ning, Wu, Bohong, Li, Huan, Lin, Bing, Nie, Zhen, Tang, Junlei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2024
• Subjects: Artificial intelligence; Corrosion in oil and gas industry; Corrosion monitoring; Data analysis; Predictive modeling; Data analysis; Predictive modeling; Corrosion in oil and gas industry; Artificial intelligence; Corrosion monitoring
• ISSN: 1350-6307; 1873-1961
• DOI: 10.1016/j.engfailanal.2023.107735

•Emphasizes the importance of addressing oil wellbore corrosion, and it’s impact on the oil and gas industry.•Explores the promising potential of artificial intelligence (AI) in revolutionizing corrosion monitoring techniques.•Acknowledgment the challenges in implementing AI-driven solutions for wellbore corrosion monitoring. 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 approaches for monitoring oil and gas industry corrosion. First, begins by examining and highlighting 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 potential 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.