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

Samenvatting

With an emphasis on the use and effects of Data Science and Artificial Intelligence (AI), this research gives a thorough over
view 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 algo
rithms 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 indus
try 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 influ
enced by the ever-changing landscape of artificial intelligence.