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Application of Data Analytics for Warm Applied Solvent Process Pilot Analysis
This project will develop data-driven models for production performance analysis and optimization for solvent-assisted bitumen recovery operations and related processes. Effective operations of solvent processes are crucial for reducing GHG emissions associated with bitumen extraction processes. Although the recent developments in digital oilfield technologies have enabled real-time surveillance of downhole operating conditions and production data, analyzing a large amount of collected data remains challenging without customized data analytics tools. The industry partner has gathered a comprehensive data set in a pilot study. Machine-/deep-learning approaches will be integrated to establish relationships between reservoir characteristics, operational parameters, and production responses. The project outcomes will offer important insights into how to optimize solvent operations.
Faculty Supervisor:
Juliana Leung
Student:
Partner:
ConocoPhillips Canada
Discipline:
Engineering
Sector:
Mining
University:
University of Alberta
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.