Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis

Yang, Jaehyun; Mostaghimi, Hamid; Hugo, Ronald J.; Park, Simon S.

doi:10.1016/j.measurement.2021.110368

Cited by 25 publications

(5 citation statements)

References 28 publications

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“…In recent years, some leak detection methods [37][38][39][40] have been developed and involved the use of machine learning (ML), which is a subset of artificial intelligence (AI). ML-AI modelling has the capability of simultaneously detecting the leak size and location, but the modelling effectiveness requires creating a link between the actual data in the fields where quick response and accurate detection are required.…”

Section: Machine Learning and Data Acquisitionsmentioning

confidence: 99%

Roadmap for Recommended Guidelines of Leak Detection of Subsea Pipelines

Reda,

Mahmoud,

Shahin

et al. 2024

JMSE

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The leak of hydrocarbon-carrying pipelines represents a serious incident, and if it is in a gas line, the economic exposure would be significant due to the high cost of lost or deferred hydrocarbon production. In addition, the leakage of hydrocarbon could pose risks to human life, have an impact on the environment, and could cause an image loss for the operating company. Pipelines are designed to operate at full capacity under steady-state flow conditions. Normal operations may involve day-to-day transients such as the operations of pumps, valves, and changes in production/delivery rates. The basic leak detection problem is to distinguish between the normal operational transients and the occurrence of non-typical process conditions that would indicate a leak. To date, the industry has concentrated on a single-phase flow, primarily of oil, gas, and ethylene. The application of a leak-monitoring system to a particular pipeline system depends on environmental issues, regulatory imperatives, loss prevention of the operating company, and safety policy rather than pipe size and configuration. This paper provides a review of the recommended guidance for leak detection of subsea pipelines in the context of pipeline integrity management. The paper also presents a review of the capability and application of various leak detection techniques that can be used to offer a roadmap to potential users of the leak detection systems.

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Section: Machine Learning and Data Acquisitionsmentioning

confidence: 99%

Roadmap for Recommended Guidelines of Leak Detection of Subsea Pipelines

Reda,

Mahmoud,

Shahin

et al. 2024

JMSE

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“…Artificial intelligence is equally playing an increasingly prominent role in leakage detection. For example, studies carried out in [23] proposed a pipeline monitoring system that combines pressure sensors and accelerometers together with machine learning algorithms to detect and localize pipeline leakages with very little error margin.…”

Section: Background To the Studymentioning

confidence: 99%

A LoRaWAN-based IoT System for Leakage Detection in Pipelines

Agbolade,

Olanrewaju,

Oyetunji

et al. 2023

EJENG

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Leakages in a pipeline are an important problem due to the potential economic and environmental hazard they present. In this study, we proposed a LoRaWAN-based approach for detecting and localizing leakages in pipelines. Our study includes an experimental setup that simulates a pipeline network with pressure and flow rate sensors attached. The flow rate and pressure data were transmitted through LoRaWAN to a receiver, which in turn uploads the data to a cloud server using a cellular network. The receiver compares the flow rate reading from all the monitoring nodes attached to the pipeline network. If flow rate reading from successive nodes presents a percentage variation of more than 1.5%, a leak is confirmed to have taken place. The flow rate readings can also be used to localize the leak. The resolution of the leak detection is dependent on the number of monitoring nodes on the pipeline network. In our study, the pressure readings were found to be insufficient to provide reliable evidence of leakages. In our specific situation, due to the relatively short length of the experimental pipeline network, a pressure drop of up to 38.2% was recorded between successive nodes with an overall pressure loss of 62%, making pressure data unsuitable for leak detection in the short pipeline network.

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“…While these approaches have contributed to improved coverage and data collection efficiency, the true breakthrough in pipeline monitoring has been achieved through the incorporation of Artificial Intelligence (AI) [12], [13]. AI, especially through the lens of Computer Vision (CV), has revolutionized the analysis and interpretation of the vast amounts of data gathered by surveillance technologies [14], [15].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Platform for Managing Third-Party Risks in Oil Pipelines

Casas,

Ramos,

Romero

et al. 2024

IEEE Access

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Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis

Cited by 25 publications

References 28 publications

Roadmap for Recommended Guidelines of Leak Detection of Subsea Pipelines

Roadmap for Recommended Guidelines of Leak Detection of Subsea Pipelines

A LoRaWAN-based IoT System for Leakage Detection in Pipelines

An End-to-End Platform for Managing Third-Party Risks in Oil Pipelines

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