Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 23
Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7059-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-7059-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 23
Methods for assessment of models
 | 
05 Dec 2023
Methods for assessment of models |  | 05 Dec 2023

An emulation-based approach for interrogating reactive transport models

Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn

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Latest update: 13 Dec 2024
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Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
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