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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Artificial intelligence for predictive maintenance in oil and gas operations

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  • Artificial intelligence for predictive maintenance in oil and gas operations

Ganesh Shankar Gowekar 1, 2, *

1 Executive MBA Oil and Gas Management, University of Petroleum and Energy Studies, Uttarakhand, India.
2 Technology Lifecycle Management, SLB, Houston (TX), United States.
 
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1228-1233
Article DOI: 10.30574/wjarr.2024.23.3.2721
DOI url: https://doi.org/10.30574/wjarr.2024.23.3.2721
 
Received 27 July 2024; revised on 08 September 2024; accepted on 10 September 2024
 
Oil and Gas companies are maximizing capabilities using AI in the area of predictive maintenance to create data-driven insights leading towards higher operational efficiencies and safety. AI-driven predictive maintenance means analyzing data collected from various sensors and equipment to predict when a machinery fault might occur. And this approach helps cut back on unanticipated downtimes and drops maintenance costs to increase the life cycle of key assets.
AI driven systems with predictive analytics capabilities analyze equipment behavior for patterns, anomalies using AI techniques like machine learning algorithms, deep learning etc. Those methods enable detecting issues in early stages, so intervention can be done before the failure. The accuracy of learning models is enhanced by AI collaborating with IoT devices to provide real-time data and continuous monitoring.
 
Predictive maintenance; Artificial Intelligence; machine learning; Oil and gas; IoT; Equipment reliability
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-2721.pdf

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Ganesh Shankar Gowekar. Artificial intelligence for predictive maintenance in oil and gas operations. World Journal of Advanced Research and Reviews, 2024, 23(3), 1228-1233. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2721

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