Modern Measurement and Control Applications in Petroleum Production Systems

  • Sneha Yadav

Abstract

Efficient petroleum production requires advanced measurement and control applications to optimize extraction, enhance operational safety, and reduce environmental impact. As oil and gas reservoirs become more complex, the need for real-time monitoring, automated decision-making, and intelligent control systems has increased. The integration of digital technologies such as smart sensors, distributed control systems (DCS), supervisory control and data acquisition (SCADA), and the Internet of Things (IoT) has significantly improved production efficiency, reservoir management, and asset reliability.


Modern advancements in artificial intelligence (AI), machine learning (ML), and edge computing have further revolutionized petroleum production by enabling predictive analytics, automated process optimization, and remote operations. These technologies facilitate real-time data acquisition, early fault detection, and adaptive control strategies, leading to reduced downtime and lower operational costs. Furthermore, the use of fiber-optic sensing, multiphase flow meters, and digital twin technology has enhanced the industry's ability to monitor well conditions, optimize production rates, and predict equipment failures with high accuracy.


Despite these advancements, challenges such as data security risks, integration complexity, and high implementation costs remain significant barriers to widespread adoption. Ensuring cybersecurity, seamless integration with legacy infrastructure, and scalability of smart systems are critical factors in the successful deployment of modern measurement and control applications.


This review provides a comprehensive analysis of the latest measurement and control technologies in petroleum production, their key benefits, the challenges associated with implementation, and potential future trends. The discussion highlights how the industry can leverage digital transformation, AI-driven automation, and sustainability-focused innovations to enhance productivity, improve safety, and minimize environmental impact, ensuring long-term energy security and operational efficiency.

References

1. Wanasinghe TR, Wroblewski L, Petersen BK, Gosine RG, James LA, De Silva O, Mann GK, Warrian PJ. Digital twin for the oil and gas industry: Overview, research trends, opportunities, and challenges. IEEE access.
2020 Jun 1;8:104175-97.
2. Wanasinghe TR, Gosine RG, James LA, Mann GK, De Silva O, Warrian PJ. The internet of things in the oil
and gas industry: a systematic review. IEEE Internet of Things Journal. 2020 May 19;7(9):8654-73.
3. Sircar A, Yadav K, Rayavarapu K, Bist N, Oza H. Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research. 2021 Dec 1;6(4):379-91.
4. Thawait LK, Singh MK. A review on real time implementation of soft computing techniques in thermal power
plant. Network: Computation in Neural Systems. 2025Jan 2;36(1):1-37.
5. Ashry I, Mao Y, Wang B, Hveding F, Bukhamsin AY, Ng TK, Ooi BS. A review of distributed fiber–optic sensing
in the oil and gas industry. Journal of Lightwave Technology. 2022 Mar 1;40(5):1407-31.
6. Tariq Z, Aljawad MS, Hasan A, Murtaza M, Mohammed E, El-Husseiny A, Alarifi SA, Mahmoud M, Abdulraheem A. A systematic review of data science and machine learning applications to the oil and gas industry. Journal
of Petroleum Exploration and Production Technology. 2021 Dec 1:1-36.
7. Abbas A. AI for predictive maintenance in industrial systems. International Journal of Advanced Engineering
Technologies and Innovations. 2024;1(1):31-51.
8. Roy A, Sain C, Kumar RR, Chanda S, Balas VE, Mekhilef S. Intelligent Computation and Analytics on Sustainable
Energy and Environment. 2024.
9. Liu Z, Babaei M, Song CC, Zhang C. Pumps for Digital Twin Simulations. Digital Twin Computing for Urban
Intelligence. 2024:119.
10. Falcone G. Key multiphase flow metering techniques. Developments in Petroleum Science. 2009 Jan 1;54:47-
190.
11. Hussain M, Alamri A, Zhang T, Jamil I. Application of artificial intelligence in the oil and gas industry. InEngi-
neering applications of artificial intelligence 2024 Feb 20 (pp. 341-373). Cham: Springer Nature Switzerland.
12. Koroteev D, Tekic Z. Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the
future. Energy and AI. 2021 Mar 1;3:100041.
13. Wu Y, Fu G, Han M, Jia Q, Lyu Q, Wang Y, Wu Z. Comparison of the theoretical elements and application characteristics of STAMP, FRAM, and 24Model: A major hazardous chemical explosion accident. Journal of
Loss Prevention in the Process Industries. 2022 Dec 1;80:104880.
Published
2025-05-03
How to Cite
YADAV, Sneha. Modern Measurement and Control Applications in Petroleum Production Systems. Journal of Advanced Research in Petroleum Technology & Management, [S.l.], v. 12, n. 1&2, p. 13-19, may 2025. ISSN 2455-9180. Available at: <http://www.thejournalshouse.com/index.php/petroleum-tech-mngmt-adr-journal/article/view/1445>. Date accessed: 04 may 2025.