04 Oct 2022
 | 04 Oct 2022

An emulation-based approach for interrogating reactive transport models

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

Abstract. We present a new approach to understand the interactions among different chemical and biological processes modelled in environmental reactive transport models (RTMs) and explore how the parameterisation of these processes influences the results of multi-component RTMs. We utilize a previously published RTM consisting of 20 primary species, 20 secondary complexes, 17 mineral reactions and 2 biologically-mediated reactions which describes bio-stimulation using sediment from a contaminated aquifer. We choose a subset of the input parameters to vary over a range of values. The result is the construction of a new dataset that describes the model behaviour over a range of environmental conditions. Using this dataset to train a statistical model creates an emulator of the underlying RTM. This is a condensed representation of the original RTM that facilitates rapid exploration of a broad range of environmental conditions and sensitivities. As an illustration of this approach, we use the emulator to explore how varying the boundary conditions in the RTM describing the aquifer impacts the rates and volumes of mineral precipitation. A key result of this work is the recognition of an unanticipated dependency of pyrite precipitation on pCO2 in the injection fluid due to the stoichiometry of the microbially-mediated sulphate reduction reaction. This complex relationship was made apparent by the emulator, while the underlying RTM was not specifically constructed to create such a feedback. We argue that this emulation approach to sensitivity analysis for RTMs may be useful in discovering such new coupled sensitives in geochemical systems and for designing experiments to optimise environmental remediation. Finally, we demonstrate that this approach can maximise specific mineral precipitation or dissolution reactions by using the emulator to find local maxima, which can be widely applied in environmental systems.

Angus Fotherby et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-729', Anonymous Referee #1, 03 Jan 2023
  • RC2: 'Comment on egusphere-2022-729', Anonymous Referee #2, 25 Mar 2023
  • AC1: 'Response to reviewers', Angus Fotherby, 21 Apr 2023

Angus Fotherby et al.

Data sets

Dataset for training the GBT model Angus Fotherby, Harold Bradbury

Model code and software

Omphalos Angus Fotherby

Angus Fotherby et al.


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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 analysis.