ABSTRACT
The oil and gas industry, particularly in refining and petrochemical operations, faces complex safety challenges due to the high-risk nature of its processes. Traditional safety protocols, while effective, often rely on reactive measures, addressing risks after incidents occur. This paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in predictive safety analytics, shifting from reactive to proactive risk management. By analyzing extensive datasets, AI-driven models can identify potential safety hazards, predict equipment failures, and anticipate operational risks before they materialize. This study highlights several AI and ML applications, including anomaly detection, predictive maintenance, and risk assessment models specifically tailored to refinery and petrochemical operations. Key findings demonstrate that predictive analytics significantly enhance safety outcomes by enabling early intervention and minimizing unplanned downtimes. The paper concludes that integrating AI and ML into safety management frameworks can reduce accident rates, optimize maintenance scheduling, and improve regulatory compliance. This shift toward predictive analytics marks a critical advancement in operational safety, providing the oil and gas industry with a robust, data-driven approach to risk mitigation.
References
- [1] Abououf, M., Mizouni, R., Singh, S., Otrok, H. and Damiani, E., 2022. Self-supervised online and lightweight anomaly and event detection for IoT devices. IEEE Internet of Things Journal, 9(24), pp.25285-25299.DOI: 10.1109/JIOT.2022.3196049.
- [2] Assis, D.L.C.D., 2019. Análise temporal preditiva de acidentes no trabalho: umaabordagem do modelo SARIMA.
- [3] Bashkin, V.N. and Galiulin, R.V., 2015. Ecological management of (geo) ecological risks in gas industry. The Open Ecology Journal, 8(1).DOI: 10.2174/1874213001508010073.
- [4] Chang, Y.C., Lee, S.Y., Liu, P.L. and Chang, C.C., 2017, December. Injury prediction based on safety climate questionnaire score using artificial neural networks. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1241-1244). IEEE.DOI: 10.1109/IEEM.2017.8290091.
- [5] Chen, S., Zhang, Z., Wang, H., Zhang, M., Zhang, H., Xu, A., Ma, Y. and Zheng, G., 2018, July. System Safety Analysis Method Based on Real-Time Online Risk Monitoring Technology. In International Conference on Nuclear Engineering (Vol. 51449, p. V002T14A024). American Society of Mechanical Engineers.DOI: 10.1115/ICONE26-82563.
- [6] Danis, M., 2019, September. Saving Lives with Statistics-An Introduction to Data Science in Workplace Safety. In SPE Offshore Europe Conference and Exhibition (p. D021S009R001). SPE.DOI: 10.2118/195737-MS.
- [7] Darvishi, H., Ciuonzo, D. and Rossi, P.S., 2023. Deep Recurrent Graph Convolutional Architecture for Sensor Fault Detection, Isolation and Accommodation in Digital Twins. IEEE Sensors Journal.DOI: 10.1109/JSEN.2023.3326096.
- [8] Das, T., ShuklaT, R.M. and Sengupta, S., 2021, June. Imposters among us: A supervised learning approach to anomaly detection in iot sensor data. In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT) (pp. 818-823). IEEE.DOI: 10.1109/WF-IoT51360.2021.9595280.
- [9] Esmaeili, B. and Hallowell, M., 2013. Integration of safety risk data with highway construction schedules. Construction Management and Economics, 31(6), pp.528-541.DOI: 10.1080/01446193.2012.739288.
- Fataliyev, T.K. and Mehdiyev, S.A., 2018. Analysis and new approaches to the solution of problems of operation of oil and gas complex as cyber-physical system. International Journal of Information Technology and Computer Science, 10(11), pp.67-76.DOI: 10.5815/IJITCS.2018.11.07.
- Gupta, S., Nikolaou, M., Saputelli, L. and Bravo, C., 2016, September. ESP health monitoring KPI: a real-time predictive analytics application. In SPE Intelligent Energy International Conference and Exhibition (pp. SPE-181009). SPE.DOI: 10.2118/181009-MS.
Download all article in PDF
