Accurate Leak Detection of Upstream Pipelines using Passive and Active Methods utilizing Artificial Intelligence

利用人工智能的被动和主动方法对上游管道进行准确的泄漏检测

• 批准号: 558438-2020
• 负责人: Hugo, Ronald
• 金额: $ 13.11万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721100
• 关键词: Accurate; Leak; Detection; Upstream; Pipelines

Pipelines are critical in the production and transport of carbon-based energy products. Pipeline systems are, however, subject to threats including internal and external corrosion, cracks, stress due to soil movement, and third-party damage. Loss-of-fluid events can endanger human life and be harmful to the environment, and this impact public trust in pipeline systems. In order to address these threats and reduce the probability of loss-of-fluid events, technology related to pipeline leak detection and structural health monitoring needs to be further advanced. One of the challenges in pipeline leak detection is dealing with multiphase flow. The different flow regimes that exist in multiphase flow, however, introduce transient disturbances that complicate the interpretation of sensor data. Moreover, pipeline networks can span kilometers in length. Signal attenuation may cause the leak signature to fall below the sensor noise floor, resulting in the signature becoming undetectable. There is a need for fast and reliable leak monitoring systems for multiphase flow pipelines to avoid the damaging events that result due to pipeline failure.The University of Calgary team proposes a novel approach in leak detection by combining passive and active leak detection techniques to characterize leaks from a pipeline with multiphase flow. The team will study data from different sensor systems to build an understanding of the problem. Using the results, a Machine Learning/Artificial Intelligence classification scheme will be trained using both laboratory and field data. The research team will use an existing multiphase flow facility that includes an interchangeable soil box, enabling the effect of external soil on signal attenuation to be investigated. Once a significant understanding of the effects of multiphase flow and soil attenuation has been achieved, the team will move on to field data of the leak detection system. The project is critically important for protecting environment as well as training of HQP with the industrial sponsors.

管道在碳基能源产品的生产和运输中至关重要。 然而，管道系统会受到包括内部和外部腐蚀、裂缝、土壤移动引起的应力以及第三方损坏在内的威胁。流体损失事件可能危及人类生命并对环境有害，这会影响公众对管道系统的信任。为了解决这些威胁并降低流体损失事件的概率，需要进一步发展与管道泄漏检测和结构健康监测相关的技术。管道泄漏检测的挑战之一是处理多相流。然而，存在于多相流中的不同流态引入了使传感器数据的解释复杂化的瞬态扰动。此外，管道网络可以跨越数公里长。信号衰减可能导致泄漏特征下降到传感器噪声基底以下，导致特征变得不可检测。有必要为多相流管道提供快速可靠的泄漏监测系统，以避免由于管道故障而导致的破坏性事件。卡尔加里大学的团队提出了一种新的泄漏检测方法，通过结合被动和主动泄漏检测技术来表征多相流管道的泄漏。该团队将研究来自不同传感器系统的数据，以建立对问题的理解。使用结果，机器学习/人工智能分类方案将使用实验室和现场数据进行训练。研究小组将使用现有的多相流设施，其中包括一个可互换的土壤盒，使外部土壤对信号衰减的影响进行调查。一旦对多相流和土壤衰减的影响有了显著的了解，该团队将继续研究泄漏检测系统的现场数据。该项目对于保护环境以及与工业赞助商一起培训HQP至关重要。