Leveraging Data Analytics and AI to Optimize Operational Efficiency in the Oil and Gas Industry

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© 2024 by IJCTT Journal
Volume-72 Issue-5
Year of Publication : 2024
Authors : Amrish Solanki
DOI :  10.14445/22312803/IJCTT-V72I5P109

How to Cite?

Amrish Solanki , "Leveraging Data Analytics and AI to Optimize Operational Efficiency in the Oil and Gas Industry," International Journal of Computer Trends and Technology, vol. 72, no. 5, pp. 72-81, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I5P109

Abstract
The fusion of Artificial Intelligence (AI) and Data Analytics has become a crucial approach for enhancing efficiency in the oil and gas sector. This study investigates the diverse advantages of integrating Artificial Intelligence (AI) and Data Analytics technology to improve productivity, safety, and profitability in oil and gas operations. Companies can utilize sophisticated algorithms and machine learning approaches to derive practical and valuable information from extensive data gathered during the manufacturing, exploration, and distribution phases. These observations enable decision-makers to allocate resources efficiently, simplify operations, and proactively detect potential dangers or anomalies. Furthermore, the utilization of AI-powered predictive maintenance solutions aids in reducing downtime, enhancing the dependability of assets, and elevating safety standards as a whole. This study consolidates significant discoveries from current research and case studies, emphasizing the revolutionary influence of AI and data analytics on operational efficiency and financial outcomes in the oil and gas industry. This research highlights the importance for industry stakeholders to adopt technological innovation as a driving force for long-term growth and competitiveness in a rapidly changing market environment.

Keywords
Oil and Gas Industry, Artificial Intelligence, Data Analytics, Efficiency, Safety, Profitability, Optimization, Future Prospects.

Reference

[1] Natalia Khan, Wei Deng Solvang, and Hao Yu, “Industrial Internet of Things (IIoT) and Other Industry 4.0 Technologies in Spare Parts Warehousing in the Oil and Gas Industry: A Systematic Literature Review,” Logistics, vol. 8, no. 1, pp. 1-21, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Alex Khang et al., AI-Aided IoT Technologies and Applications for Smart Business and Production, CRC Press, pp. 1-325, 2023.
[Google Scholar] [Publisher Link]
[3] Huan X. Nguyen et al., “Digital Twin for 5G and Beyond,” IEEE Communications Magazine, vol. 59, no. 2, pp. 10-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Praveen Kumar Ghodke et al., “Artificial Intelligence in the Digital Chemical Industry, Its Application and Sustainability,” Recent Trends and Best Practices in Industry 4.0, pp. 1-29, 2023.
[Google Scholar] [Publisher Link]
[5] Muhammad Saleem Sumbal, Eric Tsui, and Eric W.K. See-to, “Interrelationship between Big Data and Knowledge Management: An Exploratory Study in the Oil and Gas Sector,” Journal of Knowledge Management, vol. 21, no. 1, pp. 180-196, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] E.A. Dozie et al., “Revolutionizing Petrochemical Production: Unleashing the Full Potential of Industry 4.0 to Drive Efficiency,” Harness Reserve and Propel Innovation, 2023.
[Google Scholar] [Publisher Link]
[7] Muhammad Hussain et al., “Application of Artificial Intelligence in the Oil and Gas Industry,” Engineering Applications of Artificial Intelligence, pp. 341-373, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Chuka Anthony Arinze et al., “Integrating Artificial Intelligence into Engineering Processes for Improved Efficiency and Safety in Oil and Gas Operations,” Open Access Research Journal of Engineering and Technology, vol. 6, no. 1, pp. 39–51, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Abdulhamid Musa, “Revolutionizing Oil and Gas Industries with Artificial Intelligence Technology,” International Journal of Computer Sciences and Engineering, vol. 11, no. 5, pp. 20-30, 2023.
[Google Scholar] [Publisher Link]
[10] M.O. Al Jawhari et al., “Integration of a Production Optimization System with Intelligent Well Surveillance for an Effective Reservoir Management in Abu Dhabi Field,” Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Nzubechukwu Chukwudum Ohalete et al., “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, vol. 20, no. 3, pp. 167-181, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Vrutang Shah et al., “Big Data Analytics in Oil and Gas Industry,” Emerging Technologies for Sustainable and Smart Energy, pp. 37- 55, 2022.
[Google Scholar] [Publisher Link]
[13] Anirbid Sircar et al., “Application of Machine Learning and Artificial Intelligence in Oil and Gas Industry,” Petroleum Research, vol. 6, no. 4, pp. 379-391, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Armstrong Lee Agbaji, “An Empirical Analysis of Artificial Intelligence, Big Data and Analytics Applications in Exploration and Production Operations,” International Petroleum Technology Conference, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Dmitry Koroteev, and Zeljko Tekic, “Artificial Intelligence in Oil and Gas Upstream: Trends, Challenges, and Scenarios for the Future,” Energy and AI, vol. 3, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Zeeshan Tariq et al., “A Systematic Review of Data Science and Machine Learning Applications to the Oil and Gas Industry,” Journal of Petroleum Exploration and Production Technology, vol. 11, pp. 4339-4374, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Tanveer Ahmad et al., “Artificial Intelligence in Sustainable Energy Industry: Status Quo, Challenges and Opportunities,” Journal of Cleaner Production, vol. 289, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fereshteh Sattari et al., “Application of Bayesian Network and Artificial Intelligence to Reduce Accident/Incident Rates in Oil & Gas Companies,” Safety Science, vol. 133, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Michael Trevathan, “The Evolution, Not Revolution, of Digital Integration in Oil and Gas,” Doctoral Dissertation, Massachusetts Institute of Technology, pp. 1-159, 2020.
[Google Scholar] [Publisher Link]
[20] Harsh Patel et al., “Transforming Petroleum Downstream Sector through Big Data: A Holistic Review,” Journal of Petroleum Exploration and Production Technology, vol. 10, pp. 2601-2611, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Thumeera R. Wanasinghe et al., “Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges,” IEEE Access, vol. 8, pp. 104175-104197, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Hongfang Lu et al., “Oil and Gas 4.0 Era: A Systematic Review and Outlook,” Computers in Industry, vol. 111, pp. 68-90, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Khadijah M. Hanga, and Yevgeniya Kovalchuk, “Machine Learning and Multi-Agent Systems in Oil and Gas Industry Applications: A Survey,” Computer Science Review, vol. 34, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Karthik Balaji et al., “Status of Data-Driven Methods and Their Applications in Oil and Gas Industry,” SPE Europec Featured at EAGE Conference and Exhibition, Copenhagen, Denmark, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] P. Chanana, T.M. Soni, and U. Bhakne, “Emerging Technologies and Workflows in Digital Oil Field,” Offshore Technology Conference Asia, Kuala Lumpur, Malaysia, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Opeyemi Bello et al., “Application of Artificial Intelligence Methods in Drilling System Design and Operations: A Review of the State of the Art,” Journal of Artificial Intelligence and Soft Computing Research, vol. 5, no. 2, pp. 121-139, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Keith R. Holdaway, Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data-Driven Models, John Wiley & Sons, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Yasin Hajizadeh, “Machine Learning in Oil And Gas; A SWOT Analysis Approach,” Journal of Petroleum Science and Engineering, vol. 176, pp. 661-663, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Opeyemi Bello et al., “Application of Artificial Intelligence Techniques in Drilling System Design and Operations: A State-Of-The-Art Review and Future Research Pathways,” SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 2016.
[CrossRef] [Google Scholar] [Publisher Link]