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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Cited articles

Abd, A. S. and Abushaikha, A. S.: Reactive transport in porous media: a review of recent mathematical efforts in modeling geochemical reactions in petroleum subsurface reservoirs, SN Appl. Sci., 3, 401,, 2021. 
Ahmmed, B., Mudunuru, M. K., Karra, S., James, S. C., and Vesselinov, V. V.: A comparative study of machine learning models for predicting the state of reactive mixing, J. Comput. Phys., 432, 110147,, 2021. 
Anderson, R. T., Vrionis, H. A., Ortiz-Bernad, I., Resch, C. T., Long, P. E., Dayvault, R., Karp, K., Marutzky, S., Metzler, D. R., Peacock, A., White, D. C., Lowe, M., and Lovley, D. R.: Stimulating the In Situ Activity of Geobacter Species To Remove Uranium from the Groundwater of a Uranium-Contaminated Aquifer, Applied and Environmental Microbiology, 69, 5884–5891,, 2003. 
Arora, B., Dwivedi, D., Faybishenko, B., Wainwright, H. M., and Jana, R. B.: 10. Understanding and Predicting Vadose Zone Processes, in: Reviews in Mineralogy & Geochemistry, vol. 85, edited by: Druhan, J. L. and Tournassat, C., De Gruyter, 303–328,, 2020. 
Bain, J. G., Blowes, D. W., Robertson, W. D., and Frind, E. O.: Modelling of sulfide oxidation with reactive transport at a mine drainage site, J. Contam. Hydrol., 41, 23–47,, 2000. 
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 fluidrock simulation and showcase two applications to different fluidrock simulations. This approach has applications for improving model development and sensitivity analyses.