1 Department of Renewable Energy Engineering, Henriott Watt University, Edinburgh, United Kingdom.
2 Department of Petroleum Engineering, University of Benin, Edo State, Nigeria.
3 Department of Banking and Finance, Ekiti State University, Ado Ekiti, Ekiti State, Nigeria.
4 Department of Chemical Engineering, Lagos State University of Science and Technology, Ikorodu North, Lagos State, Nigeria,
5 Department of Computer Science Adekunle Ajasin University, Akungba-Akoko, Ondo state, Nigeria.
6 Department of Biomedical Technology, Federal University of Technology, Akure, Ondo State, Nigeria,
7 Department of Electrical and Electronics Engineering, University of Ibadan, Ibadan, Oyo State, Nigeria,
World Journal of Advanced Research and Reviews, 2025, 25(01), 2212-2218
Article DOI: 10.30574/wjarr.2025.25.1.0295
Received on 19 December 2024; revised on 27 January 2025; accepted on 30 January 2025
The oil and gas industry operates under very extreme conditions, posing a huge challenge when it comes to equipment reliability. Predictive maintenance; which is now possible through machine learning and data analytics, has transformed the way one looks at equipment management by making real-time failure prediction possible, reducing unplanned downtime, and optimizing maintenance schedules. The review of the technological advances in predictive maintenance methodology focuses on supervised and unsupervised machine learning, deep learning models, and integration with IoT-big data analytics. The paper also summarizes a number of case studies from some of the leading IOCs such as Shell, BP, ExxonMobil, Chevron, and Total Energies. While emphasizing respective KPIs between traditional and predictive maintenance methods; advantages, challenges, and future opportunities in the use of predictive maintenance systems were analysed. This review will be very helpful to both academics and field professionals with research and professional interests in pursuing operational efficiency and sustainability for the oil and gas industry.
Predictive Maintenance; Machine Learning; Oilfield Equipment; IoT Integration; Operational Efficiency
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Ekunke Onyeka Virginia, Okiemute Richards Obada, Bioluwatife Oluwaferanmi Oke, Israel Oluwaseun Jimson, Austine Oluwole Iwalokun, Michael Oluwatosin Akinbolusere and Awe Boluwatife Pius. Leveraging machine learning and data analytics for equipment reliability in oil and gas using predictive maintenance. World Journal of Advanced Research and Reviews, 2025, 25(01), 2212-2218. Article DOI: https://doi.org/10.30574/wjarr.2025.25.1.0295.
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