Areas of Focus


Numerical simulation of reactive, thermal, multiphase flows in heterogeneous and fractured subsurface porous media

Data analytics and machine learning applications in subsurface and geo-energy engineering applications

Inference of subsurface heterogeneities based on an multifaceted approach using flow, engineering, and geological data




Selected Funding Sources and Industry Partners (Past and Current)


Natural Sciences and Engineering Research Council of Canada (NSERC)


Petroleum Technology Research Centre

InnoTech Alberta

Nexen Energy ULC

Variperm Energy Services





Future Energy Systems (Canada First Research Excellence Fund) - Link to Researcher Profile





Primary Research Themes

1. Numerical Reservoir Modeling and Flow Simulation of Fractured Reservoirs


We focus on developing techniques to construct models of reservoir heterogeneity using data from diverse measurement scales and sources (static geologic and dynamic production information). Much of the recent research effort has been focused on fractured reservoirs because history-matching and characterization of such reservoirs are challenging for various reasons: (1) fracture properties typically are not Gaussian-distributed, rendering most covariance-based reservoir modeling techniques inappropriate, and (2) it is computationally challenging to couple flow, geomechanics, heat transfer, and geochemistry in simulation models.  

Our group is also developing efficient coupled flow-geomechanics simulation approaches for simulating multi-phase flow through models, discretized on unstructured grids, with discrete fractures. This research would have wide-ranging applications in hydrology, tight gas/shale gas/tight oil reservoirs, geothermal reservoirs, fluid storage (e.g., CO2) systems where fractures occur at multiple scales (micro, macro, and hydraulically-induced).



                                                                                        Model generated using FracMan®

                      Computational mesh for coupled flow-geomechanics simulation                                                          Strain and strain rate maps                      



2. AI in (Subsurface) Engineering Applications


We combine recent advances in data analytics, machine learning, and artificial intelligence (AI) and our domain expertise to develop innovative data analytics techniques for practical subsurface (geoenergy) engineering problems. The outcomes of our research are useful for the design and decision-making in reservoir management, where physical modeling is often extremely complex.



Example - Estimating Methane Emission Sources from Satellite, Environmental, and Oil/Gas Well Data



3. Design of Thermal- and Solvent-Based Subsurface Fluid Flow Processes


We focus on the design of thermal- and solvent-based subsurface flow processes. One particular application is heavy oil recovery, where novel solvent-based techniques offer the potential to reduce GHG emissions and water consumption. We study the impacts of multi-scale heterogeneities on recovery mechanisms (e.g. dispersion) and reservoir performance. We develop pore-scale models, field-scale simulations, and various optimization approaches to model relevant process physics and design the associated system parameters (e.g. choice of solvent, injection schedules, steam-to-solvent ratios).



                                                                           Pore-scale model of solvent transport (injection point is on the right)