Single and multiphase flow leak detection in onshore/offshore pipelines and subsurface sequestration sites: An overview

IF 3.6 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Mohammad Azizur Rahman , Abinash Barooah , Muhammad Saad Khan , Rashid Hassan , Ibrahim Hassan , Ahmad K. Sleiti , Matthew Hamilton , Sina Rezaei Gomari
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引用次数: 0

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.

Abstract Image

陆上/海上管道和地下封存场所的单相流和多相流泄漏检测:概述
现有管道可能会发生泄漏,即使在设计时采用了高质量的结构和适当的法规。石油泄漏和天然气泄漏造成的经济影响可能是巨大的。如果不能及时发现管道泄漏,将会对生命、经济、环境和企业声誉造成不利影响。因此,在陆上和海上应用中,高灵敏度和高可靠性的泄漏、泄漏位置和泄漏大小的早期检测对于通过管道高效运输碳氢化合物非常重要。虽然已有多项关于使用各种技术进行泄漏检测的研究,但近期还未找到全面研究和总结不同多相泄漏检测技术的文献。因此,本文全面综述了管道、井筒和地下封隔井中的不同泄漏检测技术。本文通过使用各种计算流体动力学 (CFD)、机理、机器学习模型和数字孪生技术,对管道和地下封存场地中的不同多相流泄漏检测技术进行了研究。综合调查显示,在综合多相流泄漏实验、计算流体动力学、力学模型以及利用机器学习实施扩展的实时瞬态监测方面,已经开展了一些研究。这类系统性调查被认为更有助于现场应用。此外,最后一节还提出了一套新的建议,说明如何利用实验、力学和 CFD 模拟数据来驱动基于现代深度学习和数字孪生技术的统计方法。这样就可以精确了解泄漏事件,如泄漏的大小、位置和方向,而无需派出遥控水下航行器或飞机对整个管道和海洋进行扫描。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
14.30%
发文量
226
审稿时长
52 days
期刊介绍: The broad scope of the journal is process safety. Process safety is defined as the prevention and mitigation of process-related injuries and damage arising from process incidents involving fire, explosion and toxic release. Such undesired events occur in the process industries during the use, storage, manufacture, handling, and transportation of highly hazardous chemicals.
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