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Uncertainty propagation in pore water chemical composition calculation using surrogate models
Pierre Sochala1,2, Christophe Chiaberge3, Francis Claret3
1CEA, DAM, DIF, 91297, Arpajon, France. pierre.sochala@cea.fr.
Scientific Reports
|September 6, 2022
Summary
Assessing nuclear waste repository safety requires understanding pore water chemistry. This study quantifies uncertainties in geochemical models, revealing key parameters influencing chemical conditions in host rock formations.
Area of Science:
- Geochemistry
- Environmental Science
- Nuclear Engineering
Background:
- Accurate geochemical modeling is crucial for nuclear waste repository safety assessments.
- Uncertainties in experimental data and thermodynamic parameters can significantly impact model predictions of pore water chemistry.
- Previous studies have not fully evaluated the influence of these uncertainties on repository performance assessments.
Purpose of the Study:
- To conduct an uncertainty propagation study on a reference geochemical model for the Callovian-Oxfordian clay formation.
- To evaluate the influence of experimental artifacts and thermodynamic database uncertainties on pore water speciation.
- To identify key input parameters and their impact on the chemical conditions relevant to nuclear waste repositories.
Main Methods:
- A reference geochemical model of the Callovian-Oxfordian clay formation pore water chemistry was used.
- Nineteen input parameters related to experimental characterization and thermodynamic databases were perturbed.
- Polynomial chaos expansions were employed for uncertainty propagation, with simulations run using the PHREEQC code.
- Statistical analysis included marginal distributions, bivariate correlations, and global sensitivity indices.
Main Results:
- The study identified critical input parameters that significantly influence pore water chemistry predictions.
- Sensitivity analysis revealed which experimental and thermodynamic uncertainties have the most substantial impact on model outputs.
- The influence of different assumed uncertainty distributions for input parameters was evaluated.
Conclusions:
- Uncertainty quantification is essential for robust geochemical modeling in nuclear waste repository safety assessments.
- Identifying and constraining key uncertain parameters can improve the reliability of performance predictions.
- This work provides a framework for evaluating model uncertainties in similar geological environments.
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