Imperial College London
Alternate

ProfessorMatthewJackson

Faculty of Engineering, Department of Earth Science & Engineering

Chair in Geological Fluid Dynamics
 
 
 
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Contact

 

+44 (0)20 7594 6538m.d.jackson

 
 
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Location

 

1.34Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Silva:2022:10.3997/2214-4609.202244069,
author = {Silva, V and Regnier, G and Salinas, P and Heaney, C and Jackson, M and Pain, C},
doi = {10.3997/2214-4609.202244069},
title = {Rapid modelling of reactive transport in porous media using machine learning},
url = {http://dx.doi.org/10.3997/2214-4609.202244069},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Reactive transport in porous media can play an important role in a variety of processes in subsurface reservoirs, such as groundwater flow, geothermal heat production, oil recovery and CO2 storage. However, numerical solution of fluid flow in porous media coupled with chemical reaction is very computationally demanding. Simultaneously, the success of machine learning in different fields has opened up new possibilities in reactive transport simulations. In this project, we focus on using machine learning techniques to replace the geochemical kinetic calculations generated by PHREEQC. PHREEQC is an open-source aqueous geochemical code that can be used in stand-alone mode or as a reaction module coupled with a flow and transport simulator. Here, we apply machine learning approaches to produce a fast proxy model of PHREEQC. This enables us to have a coupling between transport and reaction while minimizing the added computational cost. We focus initially on calcite dissolution during CO2 sequestration. Different machine learning techniques are investigated and compared to see which is more appropriate for the calcite dissolution problem. The proposed machine learning approach is designed to deal with different time-step sizes and unstructured elements. It accelerates the numerical simulation and proves to be practical to replace the reaction model presented in PHREEQC. This considerably reduces the computational cost of reactive transport while ensuring excellent simulation accuracy. The rapid modelling of reactive transport in porous media has a broad potential to replace many other phase equilibrium models across a wide range of reactive transport problems.
AU  - Silva,V
AU  - Regnier,G
AU  - Salinas,P
AU  - Heaney,C
AU  - Jackson,M
AU  - Pain,C
DO  - 10.3997/2214-4609.202244069
PY  - 2022///
TI  - Rapid modelling of reactive transport in porous media using machine learning
UR  - http://dx.doi.org/10.3997/2214-4609.202244069
ER  -
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