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Significant Advancements in UAV Technology for Reliable Oil and Gas Pipeline Monitoring

by Ibrahim Akinjobi Aromoye1, Hai Hiung Lo1, Patrick Sebastian1, Ghulam E Mustafa Abro2,*, Shehu Lukman Ayinla1,3

1 Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia
2 Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia
3 Department of Computer Engineering, University of Ilorin, Ilorin, 240003, Nigeria

* Corresponding Author: Ghulam E Mustafa Abro. Email: email

Computer Modeling in Engineering & Sciences 2025, 142(2), 1155-1197. https://doi.org/10.32604/cmes.2025.058598

Abstract

Unmanned aerial vehicles (UAVs) technology is rapidly advancing, offering innovative solutions for various industries, including the critical task of oil and gas pipeline surveillance. However, the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring, which is crucial for maintaining the integrity of pipeline infrastructure. This review paper evaluates methods for extending UAV flight endurance, focusing on their potential application in pipeline inspection. Through an extensive literature review, this study identifies the latest advancements in UAV technology, evaluates their effectiveness, and highlights the existing gaps in achieving prolonged flight operations. Advanced techniques, including artificial intelligence (AI), machine learning (ML), and deep learning (DL), are reviewed for their roles in pipeline monitoring. Notably, DL algorithms like You Only Look Once (YOLO) are explored for autonomous flight in UAV-based inspections, real-time defect detection, such as cracks, corrosion, and leaks, enhancing reliability and accuracy. A vital aspect of this research is the proposed deployment of a hybrid drone design combining lighter-than-air (LTA) and heavier-than-air (HTA) principles, achieving a balance of endurance and maneuverability. LTA vehicles utilize buoyancy to reduce energy consumption, thereby extending flight durations. The paper details the methodology for designing LTA vehicles, presenting an analysis of design parameters that align with the requirements for effective pipeline surveillance. The ongoing work is currently at Technology Readiness Level (TRL) 4, where key components have been validated in laboratory conditions, with fabrication and flight testing planned for the next phase. Initial design analysis indicates that LTA configurations could offer significant advantages in flight endurance compared to traditional UAV designs. These findings lay the groundwork for future fabrication and testing phases, which will be critical in validating and assessing the proposed approach’s real-world applicability. By outlining the technical complexities and proposing specialized techniques tailored for pipeline monitoring, this paper provides a foundational framework for advancing UAV capabilities in the oil and gas sector. Researchers and industry practitioners can use this roadmap to further develop UAV-enabled surveillance solutions, aiming to improve the reliability, efficiency, and safety of pipeline monitoring.

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Significant Advancements in UAV Technology for Reliable Oil and Gas Pipeline Monitoring

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Cite This Article

APA Style
Aromoye, I.A., Lo, H.H., Sebastian, P., Abro, G.E.M., Ayinla, S.L. (2025). Significant advancements in UAV technology for reliable oil and gas pipeline monitoring. Computer Modeling in Engineering & Sciences, 142(2), 1155–1197. https://doi.org/10.32604/cmes.2025.058598
Vancouver Style
Aromoye IA, Lo HH, Sebastian P, Abro GEM, Ayinla SL. Significant advancements in UAV technology for reliable oil and gas pipeline monitoring. Comput Model Eng Sci. 2025;142(2):1155–1197. https://doi.org/10.32604/cmes.2025.058598
IEEE Style
I. A. Aromoye, H. H. Lo, P. Sebastian, G. E. M. Abro, and S. L. Ayinla, “Significant Advancements in UAV Technology for Reliable Oil and Gas Pipeline Monitoring,” Comput. Model. Eng. Sci., vol. 142, no. 2, pp. 1155–1197, 2025. https://doi.org/10.32604/cmes.2025.058598



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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