Reservoir management and advanced optimization for thermal and thermal-solvent based recovery processes using fundamentals, scaled models, and machine learning

The project deals with optimization of thermal and solvent aided processes for heavy oil recovery. Using machine leaning methods, scaled models and real-time data integration; a work-flow for steam allocation under uncertainity for SAGD multilateral well pad operations will be developed. Big data analytics and artificial intelligence algorithms will be applied to improve the economic, environmental, planning, and forecasting aspects of reservoir management.


 

Publications, Activities, and Awards

  • Closed-Loop reservoir management using nonlinear model predictive control: A field case study
  • Data Analytics and Machine Learning for SAGD/Heavy Oil Real-time Closed Loop Reservoir Predictions and Optimization
  • Heavy Oil Recovery Processes and Reservoir Optimization with GHG Sequestration
  • Integration of data-driven models for inference of reservoir heterogeneities in SAGD production analysis
  • Modelling Reduction in Residual Oil Saturation during Enhanced Oil Recovery Process
  • Optimization of Steam Allocation for Heavy Oil Reservoirs
  • Overview of Research Advancement - In-Situ Heavy Oil Recovery
  • Overview of Research Advancement in In-Situ Heavy Oil and Shale Recovery and GHG Emissions Management
  • Polynomial-Chaos-Expansion Based Metamodel for Computationally Efficient Data Assimilation in Closed-Loop Reservoir Management
  • Progress on Optimization of Steam-Solvent Thermal Process for Efficient In-Situ Recovery and Reducing GHG Emissions
  • Research Collaboration Meeting with NRCan
  • Reservoir management and advanced optimization for thermal and thermal-solvent based in-situ recovery processes - Joint Workshop
  • Zero Net Carbon (Less Water and Energy) thermal process for in-situ oil sand recovery with GHG capture and utilization