Farshid Torabi, University of Regina

Research

low carbon emission co2 geological storage multiphase flow co2-based eor microfluidics sustainable energy

Latest papers

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios.
Scientific reports · 2023 · senior author
Application of periodic parameters and their effects on the ANN landfill gas modeling.
Environmental science and pollution research international · 2021 · senior author
Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation.
Waste management (New York, N.Y.) · 2020 · senior author

All 3 publications

Latest funding

Microfluidic Insights and Mathematical Modelling of CO2 Storage and Residual Oil Reduction in Heavy Oil Formations during CO2-Based Cyclic Solvent Injection: Transitioning to a Low Carbon Emission Energy Production
NSERC · 2024 · Principal investigator
$40,000
Utilization of Percolation Theory and Multiphysics' Concept to Develop Dynamic Modeling and Accurate Upscaling Approaches for Two-Phase Flow during Immiscible Displacement of Heavy Oil in Porous Media
NSERC · 2017 · Principal investigator
$144,000
Experimental and Numerical Simulation Studies of Cyclic Solvent Injection (CSI) Coupled with Hot-Water Injection and Electro-Thermal Heating in Post CHOPS Reservoirs
NSERC · 2015 · Principal investigator
$20,000

All 4 grants and awards

3 publications.

01

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios.

Kumar Pandey R, Gandomkar A, Vaferi B, Kumar A, Torabi F

Scientific reports2023Senior author

DOI: 10.1038/s41598-023-32187-2 PMID: 36966250

02

Application of periodic parameters and their effects on the ANN landfill gas modeling.

Fallah B, Torabi F

Environmental science and pollution research international2021Senior author

DOI: 10.1007/s11356-021-12498-5 PMID: 33538970

03

Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation.

Fallah B, Ng KTW, Vu HL, Torabi F

Waste management (New York, N.Y.)2020Senior author

DOI: 10.1016/j.wasman.2020.07.034 PMID: 32784123

Microfluidic Insights and Mathematical Modelling of CO2 Storage and Residual Oil Reduction in Heavy Oil Formations during CO2-Based Cyclic Solvent Injection: Transitioning to a Low Carbon Emission Energy Production

Principal investigatorNSERC2024Discovery Grants Program - Individual

Principal investigators: Torabi, Farshid

Keywords: low carbon emission; co2 geological storage; multiphase flow; co2-based eor; microfluidics; sustainable energy

$40,000

Utilization of Percolation Theory and Multiphysics' Concept to Develop Dynamic Modeling and Accurate Upscaling Approaches for Two-Phase Flow during Immiscible Displacement of Heavy Oil in Porous Media

Principal investigatorNSERC2017Discovery Grants Program - Individual

Principal investigators: Torabi, Farshid

$144,000

Experimental and Numerical Simulation Studies of Cyclic Solvent Injection (CSI) Coupled with Hot-Water Injection and Electro-Thermal Heating in Post CHOPS Reservoirs

Principal investigatorNSERC2015Discovery Development Grant

Principal investigators: Torabi, Farshid

$20,000

Experimental study and mathematical modeling of CO2-based solvent vapor extraction in heavy oil reservoirs

Principal investigatorNSERC2010Discovery Grants Program - Individual

Principal investigators: Torabi, Farshid

$110,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Write to this professor

A short, specific email works best. This draft uses one of their recent papers; replace the parts in brackets with your own details before sending.

Applying for

Paper to mentionMention their NSERC grant from 2024

Subject: Prospective PhD student: low carbon emission Dear Professor Torabi, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios." (2023), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Microfluidic Insights and Mathematical Modelling of CO2 Storage and Residual Oil Reduction in Heavy Oil Formations during CO2-Based Cyclic Solvent Injection: Transitioning to a Low Carbon Emission Energy Production" (2024), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]