Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions

Hicham Ferroudji1; Wahib A. Al-Ammari; Abinash Barooah; Ibrahim  Hassan; Rashid Hassan; Ahmad K. Sleiti; Sina Rezaei Gomari; Mohammad Azizur Rahman

doi:10.11159/ffhmt25.181

Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions

Hicham Ferroudji1, Wahib A. Al-Ammari, Abinash Barooah, Ibrahim Hassan , Rashid Hassan, Ahmad K. Sleiti, Sina Rezaei Gomari, Mohammad Azizur Rahman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Multiphase flows are crucial to the oil and gas industry since most petroleum companies produce and transport both gas and
oil simultaneously. Pipeline leaks are frequently caused by corrosion, aging, and metal deterioration. After an incident, the energy sector
not only loses money but also raises environmental and safety concerns. Therefore, developing a successful tool for instantaneous leakage
identification in pipelines becomes crucial. In the current work, a leaky pipeline carrying multiphase flow is numerically simulated using
Ansys-Fluent under plug flow conditions. The obtained numerical results were validated against experimental data collected from an
experimental setup. After that, Probability Density Function (PDF), Wavelet Transform (WT), and Empirical Mode Decomposition
(EMD) methods were applied to the obtained time series signals. On the other hand, the analysis is complemented by the application of
several machine learning models like Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). For
instance, it is observed that the Empirical Mode Decomposition exhibits better performance in leakage identification.

Original language	English
Title of host publication	Proceedings of the 12 th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT 2025)
Publisher	INTERNATIONAL ASET INC
Number of pages	10
ISBN (Print)	9781990800580
DOIs	https://doi.org/10.11159/ffhmt25.181
Publication status	Published - 15 Jul 2025
Event	12th International Conference on Fluid Flow, Heat and Mass Transfer - Imperial College London Conference Center, London, United Kingdom Duration: 15 Jul 2025 → 17 Jul 2025 https://avestia.com/FFHMT2025_Proceedings/index.html

Publication series

Name	International Conference on Fluid Flow, Heat and Mass Transfer
Publisher	Avestia Publishing
ISSN (Electronic)	2369-3029

Conference

Conference	12th International Conference on Fluid Flow, Heat and Mass Transfer
Abbreviated title	FFHMT 2025
Country/Territory	United Kingdom
City	London
Period	15/07/25 → 17/07/25
Internet address	https://avestia.com/FFHMT2025_Proceedings/index.html

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10.11159/ffhmt25.181Licence: Unspecified

Cite this

Ferroudji1, H., Al-Ammari, W. A., Barooah, A., Hassan , I., Hassan, R., Sleiti, A. K., Rezaei Gomari, S., & Rahman, M. A. (2025). Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions. In Proceedings of the 12 th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT 2025) Article 181 (International Conference on Fluid Flow, Heat and Mass Transfer). INTERNATIONAL ASET INC. https://doi.org/10.11159/ffhmt25.181

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title = "Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions",

abstract = "Multiphase flows are crucial to the oil and gas industry since most petroleum companies produce and transport both gas and oil simultaneously. Pipeline leaks are frequently caused by corrosion, aging, and metal deterioration. After an incident, the energy sector not only loses money but also raises environmental and safety concerns. Therefore, developing a successful tool for instantaneous leakage identification in pipelines becomes crucial. In the current work, a leaky pipeline carrying multiphase flow is numerically simulated using Ansys-Fluent under plug flow conditions. The obtained numerical results were validated against experimental data collected from an experimental setup. After that, Probability Density Function (PDF), Wavelet Transform (WT), and Empirical Mode Decomposition (EMD) methods were applied to the obtained time series signals. On the other hand, the analysis is complemented by the application of several machine learning models like Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). For instance, it is observed that the Empirical Mode Decomposition exhibits better performance in leakage identification.",

author = "Hicham Ferroudji1 and Al-Ammari, \{Wahib A.\} and Abinash Barooah and Ibrahim Hassan and Rashid Hassan and Sleiti, \{Ahmad K.\} and \{Rezaei Gomari\}, Sina and Rahman, \{Mohammad Azizur\}",

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booktitle = "Proceedings of the 12 th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT 2025)",

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Ferroudji1, H, Al-Ammari, WA, Barooah, A, Hassan , I, Hassan, R, Sleiti, AK, Rezaei Gomari, S & Rahman, MA 2025, Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions. in Proceedings of the 12 th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT 2025)., 181, International Conference on Fluid Flow, Heat and Mass Transfer, INTERNATIONAL ASET INC, 12th International Conference on Fluid Flow, Heat and Mass Transfer, London, United Kingdom, 15/07/25. https://doi.org/10.11159/ffhmt25.181

Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions. / Ferroudji1, Hicham; Al-Ammari, Wahib A.; Barooah, Abinash et al.
Proceedings of the 12 th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT 2025). INTERNATIONAL ASET INC, 2025. 181 (International Conference on Fluid Flow, Heat and Mass Transfer).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions

AU - Ferroudji1, Hicham

AU - Al-Ammari, Wahib A.

AU - Barooah, Abinash

AU - Hassan , Ibrahim

AU - Hassan, Rashid

AU - Sleiti, Ahmad K.

AU - Rezaei Gomari, Sina

AU - Rahman, Mohammad Azizur

PY - 2025/7/15

Y1 - 2025/7/15

N2 - Multiphase flows are crucial to the oil and gas industry since most petroleum companies produce and transport both gas and oil simultaneously. Pipeline leaks are frequently caused by corrosion, aging, and metal deterioration. After an incident, the energy sector not only loses money but also raises environmental and safety concerns. Therefore, developing a successful tool for instantaneous leakage identification in pipelines becomes crucial. In the current work, a leaky pipeline carrying multiphase flow is numerically simulated using Ansys-Fluent under plug flow conditions. The obtained numerical results were validated against experimental data collected from an experimental setup. After that, Probability Density Function (PDF), Wavelet Transform (WT), and Empirical Mode Decomposition (EMD) methods were applied to the obtained time series signals. On the other hand, the analysis is complemented by the application of several machine learning models like Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). For instance, it is observed that the Empirical Mode Decomposition exhibits better performance in leakage identification.

AB - Multiphase flows are crucial to the oil and gas industry since most petroleum companies produce and transport both gas and oil simultaneously. Pipeline leaks are frequently caused by corrosion, aging, and metal deterioration. After an incident, the energy sector not only loses money but also raises environmental and safety concerns. Therefore, developing a successful tool for instantaneous leakage identification in pipelines becomes crucial. In the current work, a leaky pipeline carrying multiphase flow is numerically simulated using Ansys-Fluent under plug flow conditions. The obtained numerical results were validated against experimental data collected from an experimental setup. After that, Probability Density Function (PDF), Wavelet Transform (WT), and Empirical Mode Decomposition (EMD) methods were applied to the obtained time series signals. On the other hand, the analysis is complemented by the application of several machine learning models like Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). For instance, it is observed that the Empirical Mode Decomposition exhibits better performance in leakage identification.

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DO - 10.11159/ffhmt25.181

M3 - Conference contribution

SN - 9781990800580

T3 - International Conference on Fluid Flow, Heat and Mass Transfer

BT - Proceedings of the 12 th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT 2025)

PB - INTERNATIONAL ASET INC

T2 - 12th International Conference on Fluid Flow, Heat and Mass Transfer

Y2 - 15 July 2025 through 17 July 2025

ER -

Ferroudji1 H, Al-Ammari WA, Barooah A, Hassan I, Hassan R, Sleiti AK et al. Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions. In Proceedings of the 12 th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT 2025). INTERNATIONAL ASET INC. 2025. 181. (International Conference on Fluid Flow, Heat and Mass Transfer). doi: 10.11159/ffhmt25.181

Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions

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