Proxy models for Thermal Production Optimization

Amid the tough challenge of dwindling oil prices, GE is seeking for new technology to create production forecasting and optimization tools that simulate the real operating environments and optimize across the entire process, providing actionable insights that help producers achieve their cost, production, and environmental goals. The objective of this project is to develop data driven models for optimizing bitumen production in SAGD reservoirs. These models will be developed using machine learning and data mining techniques to forecast the key performance indicators like steam to oil ratio, etc. Researchers will be working on a given data set to develop an effective model for forecasting, optimize the reservoir KPI, and improve statistical robustness.

Intern: 
Nancy Hernández-Cerón
Faculty Supervisor: 
Yuriy Zinchenko
Project Year: 
2016
Province: 
Alberta
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