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

管道在碳基能源产品的生产和运输中至关重要。然而，管道系统容易受到内部和外部腐蚀、裂缝、土壤运动引起的应力和第三方损坏等威胁。液体丢失事件可能危及人类生命和环境，这影响了公众对管道系统的信任。为了应对这些威胁，降低漏液事件的发生概率，需要进一步推进管道泄漏检测和结构健康监测相关技术。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型引入了瞬时干扰，使传感器数据的解释复杂化。此外，管道网络的长度可达数公里。信号衰减可能会导致泄漏特征低于传感器噪声下限，从而导致特征变得不可检测。针对多相流管道泄漏检测中存在的问题，提出了一种基于被动和主动相结合的多相流管道泄漏检测方法。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。利用这些结果，将使用实验室和现场数据来训练机器学习/人工智能分类方案。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箱，从而能够调查外部土壤对信号衰减的影响。一旦对多相流和土壤衰减的影响有了重大的了解，该小组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行业赞助商培训HQP至关重要。

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.