Single and multiphase flow leak detection in onshore/offshore pipelines and subsurface sequestration sites: An overview
Author | Rahman, Mohammad Azizur |
Author | Barooah, Abinash |
Author | Khan, Muhammad Saad |
Author | Hassan, Rashid |
Author | Hassan, Ibrahim |
Author | Sleiti, Ahmad K. |
Author | Hamilton, Matthew |
Author | Gomari, Sina Rezaei |
Available date | 2024-06-24T09:50:32Z |
Publication Date | 2024 |
Publication Name | Journal of Loss Prevention in the Process Industries |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1016/j.jlp.2024.105327 |
ISSN | 9504230 |
Abstract | Leaks may occur in existing pipelines, even when designed with quality construction and appropriate regulations. The economic impact of oil spills and natural gas dispersion from leaks can be huge. Failure to detect pipeline leaks promptly will have an adverse impact on life, the economy, the environment, and corporate reputation. Therefore, early detection of leaks, their location, and their size with high sensitivity and reliability are important for efficient hydrocarbon transportation through a pipeline, both in onshore and offshore applications. Although several studies have been conducted on leak detection using various techniques, recent literature that comprehensively investigates and summarizes the different multiphase leak detection techniques could not be found. Therefore, this paper provides a comprehensive review of the different leak detection techniques in pipelines, wellbores, and subsurface sequestration wells. This is done by studying the different multiphase flow leak detection techniques using various Computational Fluid Dynamics (CFD), Mechanistic, Machine Learning models, and digital twin techniques in the pipeline as well as in sub-surface sequestration sites. A comprehensive investigation revealed that a few studies have been conducted related to integrated multiphase flow leak experiments, computational fluid dynamics, mechanistic models, and implementing extended real-time transient monitoring using machine learning. This type of systematic investigation is deemed to be more useful for field applications. Furthermore, a new set of recommendations is provided in the last section which shows how experimental, mechanistic, and CFD simulation data can be used to drive a statistical approach based on modern deep learning and digital twin techniques. This allows for the precise understanding of the leak events such as size, location, and orientation of the leak, without sending a remotely operated underwater vehicle or aircraft to scan the whole pipeline and ocean. |
Sponsor | This publication was made possible by the grant NPRP14S-0321-210080 from the Qatar National Research Fund (a member of the Qatar Foundation). The authors will like to acknowledge QNRF for providing support and guidance without which this work would not have been possible. Statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Elsevier |
Subject | CFD Digital twin Leak detection Machine learning Mechanistic correlation Multiphase flow |
Type | Article |
Volume Number | 90 |
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Mechanical & Industrial Engineering [1339 items ]