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)
Mitacs
Petroleum Technology Research Centre
InnoTech Alberta
Nexen Energy ULC
Variperm Energy Services
Enverus
Cenovus
ConocoPhillips
AI4Society
Future Energy Systems (Canada First Research Excellence Fund) - Link to Researcher Profile
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).
Computational mesh for coupled flow-geomechanics simulation Strain
and strain rate maps
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.
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