AI-Driven HSE Management Systems for Real-Time Risk Mitigation in Oil and Gas Operations

References

[1] Akinsemoyin, A., Awolusi, I., Chakraborty, D., Al-Bayati, A. J., & Akanmu, A. (2023). Unmanned aerial systems and deep learning for safety and health activity monitoring on construction sites. Sensors, 23(15), 6690. https://doi.org/10.3390/s23156690

[2] Al-Mekhlafi, A. B. A., Kanwal, N., Alhajj, M. N., Isha, A. S. N., & Baarimah, A. O. J. (2025). Trends in safety culture research: A Scopus analysis. Safety, 11(2), 33. https://doi.org/10.3390/safety11020033

[3] Azmi, P. A. R., Yusoff, M., & Sallehud-Din, M. T. M. (2024). A review of predictive analytics models in the oil and gas industries. Sensors, 24(12), 4013. https://doi.org/10.3390/s24124013

[4] Bi, Y., Liu, J., & Zhang, X. (2024). An intelligent monitoring approach for urban natural gas pipeline leak using semi-supervised learning generative adversarial networks. Journal of Hazardous Materials, 480, Article 136198. https://doi.org/10.1016/j.jhazmat.2024.136198

[5] Chen, Z., Gu, Z., Qin, L., Zhou, C., Feng, X., Song, T., Wu, K., & Wang, X. (2025). Classification prediction of natural gas pipeline leakage faults based on deep learning: Employing a lightweight CNN with attention mechanisms. Processes, 13(11), 3454. https://doi.org/10.3390/pr13113454

[6] Chen, Z., Liu, Y., Gu, Z., et al. (2024). An improved convolutional neural network for pipe leakage identification based on acoustic emission. Journal of Marine Science and Engineering, 12(10), 1720. https://doi.org/10.3390/jmse12101720

[7] Formisano, F., Dellutri, M., Massera, E., Del Giudice, A., Barretta, L., & Di Francia, G. (2024). Wearable sensor node for safety improvement in workplaces: Technology assessment in a simulated environment. Sensors, 24(15), 4993. https://doi.org/10.3390/s24154993

[8] Gowekar, G. S. (2024). Artificial intelligence for predictive maintenance in oil and gas operations. World Journal of Advanced Research and Reviews, 23(3), 1228–1233. https://doi.org/10.30574/wjarr.2024.23.3.2721

[9] Jamil, Z., Nordin, S., & Isha, A. S. N. (2023). Enhancing safety communication theory in high-risk work environments: A comprehensive framework for effective communication. ASEAN Journal of Applied Linguistics, 2, 49–66. https://doi.org/10.5281/zenodo.8276021

[10] Jamil, Z., Nordin, S., Miraj, M., Alqahtani, M., Shaik, R. A., Akhter, S., & Isha, A. S. N. (2025). Sustainable safety practices and hazard management in the oil and gas industry: an HSE perspective. Frontiers in Public Health, 13, Article 1611106. https://doi.org/10.3389/fpubh.2025.1611106

[11] Lin, B., Zhu, H., Jin, Y., et al. (2024). Modeling approach and case studies of digital twin in drilling and production of oil and gas fields. Petroleum Science Bulletin, 9(2), 282–296. https://doi.org/10.3969/j.issn.2096-1693.2024.02.020

[12] Lu, H., Xi, D., & Qin, G. (2023). Environmental risk of oil pipeline accidents. Science of The Total Environment, 874, Article 162386. https://doi.org/10.1016/j.scitotenv.2023.162386

[13] Meza, E. B. M., Souza, D. G. B., Copetti, A., Sobral, A. P. B., Silva, G. V., Tammela, I., & Cardoso, R. (2024). Tools, technologies and frameworks for digital twins in the oil and gas industry: An in-depth analysis. Sensors, 24(19), 6457. https://doi.org/10.3390/s24196457

[14] Ohalete, N. N. C., Aderibigbe, A. O., Ani, E. C., Ohenhen, P. E., & Akinoso, A. (2023). Advancements in predictive maintenance in the oil and gas industry: A review of AI and data science applications. World Journal of Advanced Research and Reviews, 20(3), 167–181. https://doi.org/10.30574/wjarr.2023.20.3.2432

[15] Ren, S., Shen, F., Zhang, X., & Fu, Z. (2025). Application and development of digital twinning in oil and gas exploitation and production: A focus on China and global perspectives. Energy Exploration & Exploitation. https://doi.org/10.1080/27525783.2025.2533862

[16] Ucar, A., Karakose, M., & Kırımça, N. (2024). Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends. Applied Sciences, 14(2), 898. https://doi.org/10.3390/app14020898

[17] Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., & Liu, Z. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47–60. https://doi.org/10.1038/s41586-023-06221-2

[18] Wang, L., Liu, J., & Miao, X. (2024). Real-time detection of urban gas pipeline leakage based on machine learning of IoT time-series data. Measurement, 239, Article 115481. https://doi.org/10.1016/j.measurement.2024.115481

[19] Xie, J., Zhang, Y., He, Z., Liu, P., Qin, Y., & Wang, Z. (2023). Automated leakage detection method of pipeline networks under complicated backgrounds by combining infrared thermography and Faster R-CNN technique. Process Safety and Environmental Protection, 174, 39–52. https://doi.org/10.1016/j.psep.2023.04.014

[20] Zhao, H., Chen, L., & Li, X. (2025). A review on gas pipeline leak detection: Acoustic-based, OGI-based, and multimodal fusion methods. Information, 16(9), 731. https://doi.org/10.3390/info16090731

@article{1718845,
      author = {Ismail Al Zadjali},
      title = {AI-Driven HSE Management Systems for Real-Time Risk Mitigation in Oil and Gas Operations},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {1311-1322},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718845.pdf},
      abstract = {The oil and gas (O&G) industry is a highly dangerous operation in the world, which requires strong Health, Safety and Environment (HSE) management systems to provide timely risk mitigation. The advent of Artificial Intelligence (AI), which includes Internet of Things (IoT), unmanned aerial systems (UAS), digital twin architectures and machine learning and deep learning, has the potential to unlock a new way of approaching HSE management that moves away from reactive to proactive and predictive. This study aims to conduct a systematic literature review (SLR) of the AI-related applications in the field of HSE within O&G operations based on 20 peer-reviewed publications from 2023 to 2025. This review includes predictive maintenance, pipeline leak detection, worker physiological monitoring, drone surveillance and digital twin risk simulation. Thematic synthesis methodology was used to merge and synthesize technology enablers, performance outcomes, implementation barriers and future technology research priorities. The outcomes show that integrating AI can increase pipeline monitoring accuracy to over 90%, cut down on unplanned equipment failures by up to 34% and drastically cut down on the emergency response time. The challenges are related to data governance issues, regulatory weaknesses, cyber security risks and lack of skills in the workforce. The article wraps up with a forward-looking AI-HSE integration framework, in line with international safety standards, and the United Nations Sustainable Development Goals (SDGs), and provides action-oriented guidance to O&G operators, safety practitioners and policy makers.},
      keywords = {Artificial Intelligence, HSE Management, Oil And Gas, Real-Time Risk Mitigation, Predictive Maintenance, Pipeline Leak Detection, Digital Twins, Wearable Sensors, Safety Culture.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718845}
  }