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.

管道对碳基能产物的生产和运输至关重要。 但是，管道系统受到威胁，包括内部和外部腐蚀，裂缝，由于土壤运动引起的压力和第三方损害。流体丧失的事件可能危害人类的生命并对环境有害，这会影响公众对管道系统的信任。为了解决这些威胁并减少流体丧失事件的可能性，需要进一步提出与管道泄漏检测和结构性健康监测有关的技术。管道泄漏检测的挑战之一是处理多相流。然而，多相流中存在的不同流动状态引入了瞬态干扰，使传感器数据的解释变得复杂。此外，管道网络的长度可以跨越公里。信号衰减可能导致泄漏签名落在传感器噪声底部以下，从而导致签名变得无法检测。需要快速，可靠的泄漏监测系统，用于多相流管道，以避免由于管道故障而导致的破坏事件。卡尔加里大学（University of Calgary）团队提出了一种新型的泄漏检测方法，通过将被动和主动泄漏检测技术结合起来，以表征从管道中的泄漏和多态流量中的管道泄漏。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，将使用实验室和现场数据培训机器学习/人工智能分类方案。研究团队将使用现有的多相流动设施，其中包括一个可互换的土壤盒，从而可以研究外部土壤对信号衰减的影响。一旦实现了多相流和土壤衰减的影响，团队将继续进行泄漏检测系统的现场数据。该项目对于保护环境以及向工业赞助商培训HQP至关重要。