IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY (IJASMT )

E- ISSN 2489-009X
P- ISSN 2695-1908
VOL. 11 NO. 3 2025
DOI: 10.56201/ijasmt.vol.11.no3.2025.pg34.44

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Development of Operational Predictive Maintenance System in Oil and Gas Industry Case Study of Warri Refining and Petrochemical Company

Dr Oluwatoyin Mary Yerokun, Prof Dipo Theophilus Akomolafe, Umar Ibrahim Bello

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Abstract

The oil and gas industry is critical to global energy supply and national economic development, particularly in resource-rich countries like Nigeria. One of the major reasons for the collapse and total shutting down of all refineries in Nigeria is lack of maintenance culture. In the fast- paced digital world, it is imperative to employ emerging technologies in addressing such national issues like refinery maintenance, this study therefore focuses on developing an operational predictive maintenance (PdM) system for Warri Refining and Petrochemical Company (WRPC). The system utilized advanced data analytics and condition-based monitoring technologies to predict equipment failures before they occur. Predictive maintenance offers advantages over traditional reactive and preventive maintenance approaches by optimizing maintenance schedules, reducing downtime, and lowering operational costs. The study highlighted challenges such as aging infrastructure, resource constraints, and regulatory pressures WRPC faces and demonstrated how PdM system addressed the issues. Implementation results showed improved equipment reliability, enhanced safety, and optimized operational efficiency. Recommendations for future initiatives include technological upgrades, staff training, and collaborative efforts among industry stakeholders.

keywords:

Machine Learning, Predictive, Maintenance, Refining, Petrochemical.

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