An emulation-based approach for interrogating reactive transport models
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)
- 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.
Dataset for training the GBT model https://doi.org/10.5281/zenodo.7113379
Model code and software
Angus Fotherby et al.
Viewed (geographical distribution)
Fotherby et al. presented an emulation-based approach for interrogating reactive transport models. The manuscript is interesting and well-written. It is also important because the modeling community is exploring a trade-off between computationally expensive mechanistic and inexpensive hybrid models. This manuscript developed emulators on RTM runs and touched on mechanistic and hybrid aspects but needed to demonstrate the actual use of emulators. The authors should demonstrate emulators' utility in reproducing 2D (if not 3D) simulations based on their emulators built using 1D simulations (as in the m/s). Otherwise, this manuscript is just another way of doing sensitivity analysis. In addition, the manuscript rehashes previous work done at the Rifle site by one of the coauthors because emulators were developed on that work in addition to methodological details. I would like to see a discussion on scientific insights, which is on the lesser side.
I have some questions as follows:
(1) Lines 16-18: "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." Were new boundary conditions implemented in RTMs that were used to train emulators? If yes, then what new insights can be drawn? If not, how can emulators extrapolate something they have not seen before? If new boundary conditions are within certain bounds that RTMs have already been run is not a big deal because simple interpolation might also work well.
(2) I have a similar question on recognizing an unanticipated dependency of pyrite precipitation on pCO2 in the injection fluid due to the stoichiometry of the microbially-mediated sulfate reduction reaction. The authors claim that this complex relationship was made apparent by the emulator, while the underlying RTM was not specifically constructed to create such feedback. I wonder how emulators expand their horizons when they have reduced representation of RTMs. What is the explanation that reduced order models can tell beyond RTMs capabilities?
(3) Did the authors try exploring the unanticipated dependency of pyrite precipitation on pCO2 using CrunchTope or other simulators (Geochemist's Workbench, ToughReact)? This is a vital conclusion of the manuscript, so the authors need to back this up using RTMs.
(4) It needs to be clarified how emulators can find local maxima, which it seems RTMs cannot. The authors claim that they demonstrate using emulators can maximise specific mineral precipitation or dissolution reactions to find local maxima. Is this something like hot spots and hot moments?
(5) Lines 63-66: "Unfortunately, due to the computational expense of many modern multi-component RTMs …" when the authors can run 10K simulations to build emulators, how they are finding RTMs computationally expensive. The authors might want to say that 2D and 3D RTMs are expensive. So my suggestion would be to demonstrate using at least 2D (if not 3D) simulations that their emulators built using 1D simulations can be reproduced. Otherwise, this manuscript is just another way of doing sensitivity analysis.
(6) Lines 173 and 176-178: The authors acknowledge that ranges of concentrations are somewhat higher than those in natural systems. Then performed, 10,927 runs and 9416 provided useable data. How do they ensure that synthetic data is realistic?
(7) Figure 3: NH4+ and Ca++ fail to capture the trends. Ca++ shows inverse trends. Was the emulator exposed to this particular RTM run? Emulators are not able to reproduce RTM runs.
(8) The authors should define several experiments based on complexities, not include them (and their close conditions), and test emulators' capabilities to reproduce those runs. This exercise will allow them to say how far emulators can be used.