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Numerical study of pipeline leak detection for gas-liquid stratified flow

journal contribution
posted on 13.05.2022, 11:08 authored by Mutiu Adesina Adegboye, Aditya Karnik, Wai Keung FungWai Keung Fung

Multiphase flows are of paramount importance in the oil and gas industry, considering that most petroleum industries produce and transport oil and gas simultaneously. However, systematic research on pipeline leakage conveying more than one phase at a time is lacking attention. In this work, a numerical method is proposed to investigate the effect of two-phase gas-liquid leak flow behaviour in a subsea natural gas pipeline. The results of the simulations have been validated against the latest experimental and numerical data reported in the literature, and a good agreement has been obtained. The effect of leak sizes, longitudinal leak locations, multiple leakages and axial leak positions on the pressure gradient, flow rate and volume fractions in the pipeline were systematically investigated. The results show that the flow field parameters provide pertinent indicators in pipeline leakage detection. In particular, the upstream pipeline pressure could serve as a critical indicator for detecting leakage even if the leak size is small. Whereas, the downstream flow rate is a dominant leakage indicator if the flow rate monitoring is chosen for leak detection. The results also reveal that when two leaks with different sizes co-occur in a single pipe, detecting the small leak becomes difficult if its size is below 25% of the large leak size. However, in the event of a double leak with equal sizes, the leak closer to the pipe upstream is easier to detect. 

History

Published in

Journal of Natural Gas Science and Engineering

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Citation

Mutiu Adesina Adegboye, Aditya Karnik, Wai-Keung Fung, Numerical study of pipeline leak detection for gas-liquid stratified flow, Journal of Natural Gas Science and Engineering, 2021, 104054, ISSN 1875-5100, https://doi.org/10.1016/j.jngse.2021.104054.

Electronic ISSN

1875-5100

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Wai Keung Fung

Copyright Holder

© The Publisher

Language

en