Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
Abstract The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D e ), clay porosities (ε), and adsorption distribution coefficients (K D ) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42260-5 |
| _version_ | 1797452791299440640 |
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| author | Christophe Tournassat Carl I. Steefel Patricia M. Fox Ruth M. Tinnacher |
| author_facet | Christophe Tournassat Carl I. Steefel Patricia M. Fox Ruth M. Tinnacher |
| author_sort | Christophe Tournassat |
| collection | DOAJ |
| description | Abstract The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D e ), clay porosities (ε), and adsorption distribution coefficients (K D ) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these diffusion parameters. These effects include the presence of filters holding the solid sample in place, the variations in concentration gradients across the diffusion cell due to sampling events, the impact of tubing/dead volumes on the estimation of diffusive fluxes and sample porosity, and the effects of O-ring-filter setups on the delivery of solutions to the clay packing. Doing so, the direct modeling of the measurements of (radio)tracer concentrations in reservoirs is more accurate than that of data converted directly into diffusive fluxes. While the above-mentioned effects have already been described individually in the literature, a consistent modeling approach addressing all these issues at the same time has never been described nor made easily available to the community. A graphical user interface, CrunchEase, was created, which supports the user by automating the creation of input files, the running of simulations, and the extraction and comparison of data and simulation results. While a classical model considering an effective diffusion coefficient, a porosity and a solid/solution distribution coefficient (D e –ε–K D ) may be implemented in any reactive transport code, the development of CrunchEase makes it easy to apply by experimentalists without a background in reactive transport modeling. CrunchEase makes it also possible to transition more easily from a D e –ε–K D modeling approach to a state-of-the-art process-based understanding modeling approach using the full capabilities of CrunchClay, which include surface complexation modeling and a multi-porosity description of the clay packing with charged diffuse layers. |
| first_indexed | 2024-03-09T15:13:47Z |
| format | Article |
| id | doaj.art-877b63fc754b4171a29ac30624cd2d05 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:13:47Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-877b63fc754b4171a29ac30624cd2d052023-11-26T13:14:45ZengNature PortfolioScientific Reports2045-23222023-09-0113111310.1038/s41598-023-42260-5Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approachChristophe Tournassat0Carl I. Steefel1Patricia M. Fox2Ruth M. Tinnacher3Earth and Environmental Sciences Area, Lawrence Berkeley National LaboratoryEarth and Environmental Sciences Area, Lawrence Berkeley National LaboratoryEarth and Environmental Sciences Area, Lawrence Berkeley National LaboratoryDepartment of Chemistry and Biochemistry, California State University East BayAbstract The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D e ), clay porosities (ε), and adsorption distribution coefficients (K D ) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these diffusion parameters. These effects include the presence of filters holding the solid sample in place, the variations in concentration gradients across the diffusion cell due to sampling events, the impact of tubing/dead volumes on the estimation of diffusive fluxes and sample porosity, and the effects of O-ring-filter setups on the delivery of solutions to the clay packing. Doing so, the direct modeling of the measurements of (radio)tracer concentrations in reservoirs is more accurate than that of data converted directly into diffusive fluxes. While the above-mentioned effects have already been described individually in the literature, a consistent modeling approach addressing all these issues at the same time has never been described nor made easily available to the community. A graphical user interface, CrunchEase, was created, which supports the user by automating the creation of input files, the running of simulations, and the extraction and comparison of data and simulation results. While a classical model considering an effective diffusion coefficient, a porosity and a solid/solution distribution coefficient (D e –ε–K D ) may be implemented in any reactive transport code, the development of CrunchEase makes it easy to apply by experimentalists without a background in reactive transport modeling. CrunchEase makes it also possible to transition more easily from a D e –ε–K D modeling approach to a state-of-the-art process-based understanding modeling approach using the full capabilities of CrunchClay, which include surface complexation modeling and a multi-porosity description of the clay packing with charged diffuse layers.https://doi.org/10.1038/s41598-023-42260-5 |
| spellingShingle | Christophe Tournassat Carl I. Steefel Patricia M. Fox Ruth M. Tinnacher Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach Scientific Reports |
| title | Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach |
| title_full | Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach |
| title_fullStr | Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach |
| title_full_unstemmed | Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach |
| title_short | Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach |
| title_sort | resolving experimental biases in the interpretation of diffusion experiments with a user friendly numerical reactive transport approach |
| url | https://doi.org/10.1038/s41598-023-42260-5 |
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