AI-Driven HSE management systems for risk mitigation in the oil and gas industry
1 Independent Researcher; Lagos Nigeria.
2 Department of Mechanical Engineering, Nnamdi Azikiwe University, Awka Nigeria.
3 Independent Researcher, United Kingdom.
4 Independent Researcher, Houston Texas, USA.
Review
Comprehensive Research and Reviews in Engineering and Technology, 2024, 02(01), 001–022.
Article DOI: 10.57219/crret.2024.2.1.0059
Publication history:
Received on 11 June 2024; revised on 25 July 2024; accepted on 27 July 2024
Abstract:
The oil and gas industry faces numerous health, safety, and environmental (HSE) risks due to the complexity of its operations. Traditional HSE management systems often rely on manual processes and reactive approaches, which can lead to inefficiencies and delayed responses to potential hazards. This paper proposes the integration of Artificial Intelligence (AI) into HSE management systems to enhance real-time safety monitoring and predictive risk management. By leveraging AI-driven technologies such as machine learning, computer vision, and predictive analytics, companies can proactively identify and mitigate risks, significantly reducing accidents, equipment failures, and environmental incidents. AI-enabled systems can process vast amounts of data from various sensors, drones, and other IoT devices in real-time, enabling continuous monitoring of hazardous conditions. Furthermore, predictive models can analyze historical data and operational patterns to foresee potential risks before they materialize, providing actionable insights to decision-makers. This approach allows for more dynamic, data-driven safety protocols, optimizing risk management strategies and improving compliance with regulatory standards. The paper will also explore the role of AI in automating routine safety checks, enhancing worker safety through real-time alerts, and minimizing human error. It will highlight case studies where AI-driven HSE systems have been successfully implemented, leading to substantial improvements in safety performance and operational efficiency. Additionally, the challenges and limitations of integrating AI into existing HSE frameworks, such as data security, workforce training, and technology costs, will be discussed. Ultimately, this paper demonstrates that AI-driven HSE management systems offer a transformative solution to risk mitigation in the oil and gas industry. By adopting AI technologies, companies can enhance safety, reduce operational risks, and create more resilient, efficient operations in an industry known for its hazardous environments.
Keywords:
AI-driven HSE; Risk mitigation; Oil and gas industry; Predictive risk management
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0