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

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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.