Data analytics as a catalyst for operational optimization: A comprehensive review of techniques in the oil and gas sector
1 Independent Researcher, Georgia, USA.
2 Shell Nigeria Exploration and Production Company Lagos. Nigeria.
3 TotalEnergies Limited, Nigeria (c/o Benmaris Limited).
4 Independent Researcher, Houston Texas, USA.
5 Independent Researcher; Lagos Nigeria.
Review
International Journal of Frontline Research in Multidisciplinary Studies, 2022, 01(02), 070–084.
Article DOI: 10.56355/ijfrms.2022.1.2.0032
Publication history:
Received on 10 May 2022; revised on 14 November 2022; accepted on 16 November 2022
Abstract:
Data analytics has emerged as a critical enabler for operational optimization in the oil and gas sector, driving efficiency and profitability through data-driven insights. This comprehensive review examines the various techniques employed in the industry, focusing on the role of predictive analytics, machine learning, and artificial intelligence (AI) in enhancing exploration, production, and distribution processes. The integration of real-time data analytics with traditional engineering methodologies allows for more accurate reservoir simulations, improved drilling precision, and predictive maintenance of critical infrastructure. Furthermore, the adoption of big data and cloud computing enables faster data processing and more scalable solutions for large and complex datasets, enhancing decision-making capabilities. The review also highlights the significance of prescriptive analytics, which aids in scenario planning and optimizing supply chain logistics, minimizing operational downtime, and improving overall asset management. Techniques such as seismic data interpretation, remote sensing analytics, and IoT-enabled sensors are discussed in the context of their application to real-time monitoring and risk mitigation in oil and gas operations. These technologies contribute to reducing environmental impact by optimizing resource allocation and minimizing operational inefficiencies, supporting the industry's transition to more sustainable practices. Moreover, the review identifies key challenges in the adoption of data analytics, such as data silos, cybersecurity risks, and the need for advanced technical literacy among the workforce. Solutions to these challenges, including the implementation of integrated data platforms and enhanced cybersecurity protocols, are explored. Finally, the review underscores the future potential of analytics-driven technologies in driving digital transformation and operational excellence in the oil and gas sector.
Keywords:
Data Analytics; Operational Optimization; Oil and Gas; Predictive Maintenance; Machine Learning; Artificial Intelligence; Prescriptive Analytics; Seismic Data; Iot; Real-Time Monitoring; Big Data; Cloud Computing; Risk Mitigation; Sustainability; Digital Transformation
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0