Research

We investigate the physics, chemistry, and techno-economics of CO2 storage underground

Our research includes exploring fundamental pore scale fluid dynamics, developing digital rocks analysis techniques, increasing the accuracy of field scale reservoir simulation, and evaluating the feasibility of scaling up CO2 storage to climate relevant scales.

Our Research Projects

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StrataTrapper Advanced Modelling of CO2 Migration and Trapping

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THMC4CCS: ThorougH experiMental and numerical investigation of Coupled processes for geologiC Carbon ‎Storage

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Royal Academy of Engineering Senior Research Fellowship
Logo image for ExpReCCS project

ExpReCCS: Expansion of ResourCes for CO2 Storage on the Horda Platform

inFUSE Prosperity Partnership

Shell Digital Rocks Laboratory

Citation

BibTex format

@article{Wu:2023:10.1016/j.fuel.2023.128753,
author = {Wu, Y and An, S and Tahmasebi, P and Liu, K and Lin, C and Kamrava, S and Liu, C and Yu, C and Zhang, T and Sun, S and Krevor, S and Niasar, V},
doi = {10.1016/j.fuel.2023.128753},
journal = {FUEL},
title = {An end-to-end approach to predict physical properties of heterogeneous porous media: Coupling deep learning and physics-based features},
url = {http://dx.doi.org/10.1016/j.fuel.2023.128753},
volume = {352},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AU  - Wu,Y
AU  - An,S
AU  - Tahmasebi,P
AU  - Liu,K
AU  - Lin,C
AU  - Kamrava,S
AU  - Liu,C
AU  - Yu,C
AU  - Zhang,T
AU  - Sun,S
AU  - Krevor,S
AU  - Niasar,V
DO  - 10.1016/j.fuel.2023.128753
PY  - 2023///
SN  - 0016-2361
TI  - An end-to-end approach to predict physical properties of heterogeneous porous media: Coupling deep learning and physics-based features
T2  - FUEL
UR  - http://dx.doi.org/10.1016/j.fuel.2023.128753
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001039138300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
VL  - 352
ER  -
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