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AI based multi-phase flow meter
The persistent challenge faced by oil producers in measuring multi-phase flows, characterized by a complex mixture of oil, gas, and water from wells, has prompted the development of an AI-based multi-phase flow meter by MLCan. Current approaches to real-time precision are hindered by the inherent complexity of these flows, leading to costly and less accurate measurements. Additionally, conventional techniques involve phase separation, posing environmental risks and generating increased waste. MLCan’s innovative meter, equipped with pressure and temperature sensors, bypasses the need for phase separation, capturing flow data for individual components. Processed through an AI algorithm, this data enables the meter to provide real-time and online estimations. The research aims to comprehensively study oil wells in Alberta, creating a dataset for various oil resources and implementing machine learning algorithms for evaluation.
Faculty Supervisor:
Hadis Karimipour;Eric Limacher;Andy Knight
Student:
Partner:
MLCan
Discipline:
Engineering
Sector:
Mining
University:
University of Calgary
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.