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

doi:10.56201/ijasmt.vol.11.no3.2025.pg34.44

📄 Download Paper

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

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 LearningPredictiveMaintenanceRefiningPetrochemical.

References

Akinwale, O. I., & Ojo, J. A. (2020). Enhancing Predictive Maintenance Techniques Using IoT for Oil and Gas Infrastructure in Nigeria. Journal of Engineering Research and Reports, 5(1), 1–14. https://doi.org/10.9734/jerr/2020/v5i130278 Diptiben, Ghelani. (2024). 4. Harnessing machine learning for predictive maintenance in energy infrastructure: A review of challenges and solutions. International Journal of Science and Research Archive, doi: 10.30574/ijsra.2024.12.2.0525 Nguyen, H., Li, W., & Zhang, Y. (2020). The Role of IoT in Predictive Maintenance: A Comprehensive Review. Sensors, 20(11), 3154. https://doi.org/10.3390/s20113154 Ojo, J. A., & Ogundele, K. (2020). Challenges and Opportunities in the Nigerian Oil Refining Sector: The Case of Warri Refining and Petrochemical Company. Energy Reports, 6, 256-264. https://doi.org/10.1016/j.egyr.2020.04.003 Okonkwo, T. S., Iwuanyanwu, E. A., & Adebiyi, O. (2021). Technological Integration Challenges in Nigerian Refineries: A Predictive Maintenance Perspective. Journal of Petroleum and Gas Engineering, 12(4), 202-215. https://doi.org/10.1016/j.jpge.2021.05.007 Oil and gas industry challenges for the next decade: strategies to face them from the education of future petroleum engineers. Semantic Web Technologies and Applications in rtificial Intelligence of Things. IGI Global Scientific Publishing. https:/www.igi- global.com/chapter/…347416. Olujobi, O.J., Irumekhai, O.S., Olujobi, O.M,. Aina-Pelemo, A.D. & Olipede, D.E. Challenges Militating against Indigenous Oil Companies Operating in Nigeria’s Upstream Petroleum Industry: Strategies and Panaceas for their Sustainability. Journal of Sustainable Development Law and Policy (THE), Vol.15 No. https:/www.ajol.info/index.php/jsdlp/article/view/282743.,

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

Abstract

Keywords

References

More Articles from INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY