Grant: Alliance Grants
University of Calgary
$150,000.00
Enhanced Multiphase Pipeline Leak Detection: Integrating Passive and Active Techniques with Edge Computing and Machine Learning
FY 2024-25
Summary
Pipelines are critical in the production and transport of energy products. Pipeline systems are, however, subject to threats including 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. To address these threats and reduce the probability of loss-of-fluid events, pipeline leak detection technology needs to be further advanced. One challenging area for pipeline leak detection involves multiphase flow. Multiphase flow can introduce significant transient disturbances that complicate the interpretation of sensor data. Additionally, pipeline networks are often kilometers in length, and leak signatures can become attenuated through these distances causing the signal to weaken, making the leak signature undetectable. There is a need for fast and reliable leak monitoring systems for application to multiphase flow pipelines.
The UCalgary 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 design and develop a versatile platform for field-applicable data acquisition, implement passive monitoring for leak detection in multiphase flow pipelines under complex geometry, advance active monitoring techniques to identify leaks and structural defects across a range of geometries, and conduct extensive field trials to refine and authenticate the effectiveness of the newly developed edge computing algorithms. The advancements from this project are expected to set new benchmarks in pipeline integrity management, fostering a safer, more sustainable, and economically viable energy transportation network. The project offers a comprehensive training plan designed to equip trainees, including undergraduate and graduate students as well as postdoctoral fellows, with a robust set of research and professional skills. The project is critically important for protecting environment as well as training of HQP with the industrial sponsors.
Program
Alliance Grants
Recipient
Location
Alberta, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
597102-2024
Name nom
Hugo, Ronald RJ
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
2024
Competition year année de concours
2024
Award amount
150000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
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
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Enhanced Multiphase Pipeline Leak Detection: Integrating Passive and Active Techniques with Edge Computing and Machine Learning
Keywords
active excitation; artificial intelligence; edge computing; leak detection; multiphase flow; pipeline; sensing and monitoring; upstream
Application summary
Pipelines are critical in the production and transport of energy products. Pipeline systems are, however, subject to threats including 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. To address these threats and reduce the probability of loss-of-fluid events, pipeline leak detection technology needs to be further advanced. One challenging area for pipeline leak detection involves multiphase flow. Multiphase flow can introduce significant transient disturbances that complicate the interpretation of sensor data. Additionally, pipeline networks are often kilometers in length, and leak signatures can become attenuated through these distances causing the signal to weaken, making the leak signature undetectable. There is a need for fast and reliable leak monitoring systems for application to multiphase flow pipelines.
The UCalgary 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 design and develop a versatile platform for field-applicable data acquisition, implement passive monitoring for leak detection in multiphase flow pipelines under complex geometry, advance active monitoring techniques to identify leaks and structural defects across a range of geometries, and conduct extensive field trials to refine and authenticate the effectiveness of the newly developed edge computing algorithms. The advancements from this project are expected to set new benchmarks in pipeline integrity management, fostering a safer, more sustainable, and economically viable energy transportation network. The project offers a comprehensive training plan designed to equip trainees, including undergraduate and graduate students as well as postdoctoral fellows, with a robust set of research and professional skills. The project is critically important for protecting environment as well as training of HQP with the industrial sponsors.
