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Grant: Alliance Grants

University of Calgary

$180,000.00

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

FY 2021-22

Summary
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.
Program
Alliance Grants
Location
Alberta, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
558438-2020
Name nom
Hugo, Ronald
Department département
Mechanical and Manufacturing Engineering
Organization id
11
Institution établissement
University of Calgary
Province en
Alberta
Province fr
Alberta
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2020
Award amount
180000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
300
Area of application group en
Energy resources
Area of application group fr
Ressources d'énergie
Area of application en
Energy resources (including production, exploration, processing, distribution and use)
Area of application fr
Ressources d'énergie (production, exploration, traitement, distribution et utilisation de l'énergie)
Research subject code
2100
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Mechanical engineering
Research subject fr
Génie mécanique
Installment
inst:1; 2
Partie
2
Nb partie
3
Application title
Accurate Leak Detection of Upstream Pipelines using Passive and Active Methods utilizing Artificial Intelligence
Keyword
NSERC
Application summary
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.