ABSTRACT

Pipelines play a pivotal role in the oil and gas industry as they are currently the most widely used method for conveying hydrocarbon fluids, natural gas, and petroleum products around the world. These pipelines require continuous monitoring and surveillance to ensure sustained infrastructural stability and undistorted output. Although pipeline monitoring has advanced over the years, there remains a critical need for an effective monitoring system with real-time feedback and the ability to detect minor leakages that many sensors might fail to detect. Deep learning (DL), a subset of machine learning, can be integrated with unmanned aerial vehicles to collect and analyze sensor data to solve this problem. DL using specific algorithms with pre-trained models based on historical data can detect external leakages and cracks. They can also predict likely areas of damage that could be omitted in conventional damage monitoring. Recently, there has been an increasing popularity in the use of sensor-equipped drones for monitoring pipelines in the oil and gas industry because of the numerous advantages it offers. A prominent advantage is that integrating and analyzing the data set obtained from an unmanned aerial vehicle (UAV) with DL algorithms can give real-time feedback and predict possible areas on the pipeline which show the likelihood of deterioration over time. This paper proposes a framework that integrates UAVs with DL for the monitoring of pipelines in the oil and gas industry. It is expected that the application of this framework will ensure a more efficient and proactive pipeline monitoring procedure for mitigating hazards and preventing accidents associated with oil and gas leakage or spillage.

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Pipelines 2023
Pages: 181 - 191

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Published online: Aug 10, 2023

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Roy Lan, S.M.ASCE [email protected]
1School of Civil and Environmental Engineering and Construction Management, Univ. of Texas at San Antonio, San Antonio, TX. Email: [email protected]
Ibukun Awolusi, Ph.D., A.M.ASCE [email protected]
2School of Civil and Environmental Engineering and Construction Management, Univ. of Texas at San Antonio, San Antonio, TX. Email: [email protected]
Jiannan Cai, Ph.D., A.M.ASCE [email protected]
3School of Civil and Environmental Engineering and Construction Management, Univ. of Texas at San Antonio, San Antonio, TX. Email: [email protected]

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