Advancements in Predictive Maintenance in the Oil and Gas Industry: A Review of AI and Data Science Applications

Abstract

With an emphasis on the use and effects of Data Science and Artificial Intelligence (AI), this research gives a thorough overview
of the developments in predictive maintenance within the gas and oil sector. The major goal was to assess the impact of
AI and data science on the shift from manual to automated maintenance procedures. A comprehensive literature review was
conducted as part of the process, with databases such Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink used.
A primary goal of the search was to locate articles published in the oil and gas industry between 2010 and 2022 that discussed
artificial intelligence (AI), data science (DS), and predictive maintenance (PM). The results show that predictive maintenance
procedures are much improved by AI and data science. Reduced operating expenses and downtime have resulted from improved
equipment failure prediction capabilities and streamlined maintenance scheduling made possible by artificial intelligence algorithms
and data analytics. Among the difficulties highlighted by the research is the need for high-quality, real-time data as well
as the complexity of data management. Building stronger AI models that can adjust to the ever-changing industrial landscape is
where the future of breakthroughs lies. Policymakers should establish frameworks to promote the ethical use of AI, and industry
stakeholders should put money into workforce training for AI-based systems, according to the report. Creating sustainable
maintenance procedures and investigating how AI interacts with other new technologies are two areas that might need further
investigation in the future. Research shows that oil and gas companies’ maintenance plans of the future will be heavily influenced
by the ever-changing landscape of artificial intelligence.