Zhangxing Chen

Zhangxing Chen

Professor of UCalgary

University of Calgary

Dr. Chen’s research has created a state-of-the-art reservoir simulation toolkit that allows for multiple parallel runs, faster computation and rigorous optimization. He has developed numerical techniques that are faster and have greater accuracy than are currently available. These techniques will assist history matching methods and increase the effectiveness of field optimization. In turn, these advancements will improve the workflow, including risk and uncertainty analysis. There will be an overall significant improvement in the modeling and simulation processes of enhanced oil recovery for unconventional oil and gas (tight and shale oil and gas, CBM), heavy oil and oil sands.

Dr. Chen’s group is focused on optimizing industry capacity to extract energy resources. Specifically, the group focuses on modeling and simulation of advanced energy recovery processes, such as: Carbon Capture and Storage (CCS); Cyclic Steam Stimulation (CSS); Steam-Assisted Gravity Drainage (SAGD); Expanding Solvent Steam-Assisted Gravity Drainage (ES-SAGD); Vapor Extraction Process (VAPEX) for Heavy Oil and Bitumen Reservoirs; Hydraulic Fracturing for Shale and Tight Oil and Gas and CBM (coal bed methane); and, Underground Coal Gasification (UCG).

Key areas of research include (1) derivation of physical and mathematical models; (2) upscaling from geomodels to reservoir simulation models; (3) development and study of high order and accurate numerical methods (e.g., finite volumes and finite elements); (4) development and analysis of linear and nonlinear solvers (new pre-conditioners, solvers, parallelization technology, and solution schemes); (5) validation and applications; (6) reservoir simulation software development. Mathematical modeling and computer simulation are important for process design and optimization and reservoir performance prediction.

Interests
  • Carbon Capture and Storage
  • Numerical Modeling
  • Numerical methods
  • Mathematical Modeling and Upscaling for Reservoir Performance Prediction