Impact of a CO2 leakage on groundwater quality. Influence of regional flow using reactive transport models.
Bibliographic record
Abstract
Carbon Capture and Storage in deep and saline aquifers is one of the most hopeful technologies to reduce CO2 emissions into the atmosphere. However, CO2 leakages into shallow freshwater aquifers are a potential risk and potential impacts on groundwater have to be studied. A better understanding on how it could affect groundwater quality, aquifer minerals and trace elements is necessary to characterize a future storage site. Moreover, monitoring and remediation solutions have to be evaluated before storage operations. As part of the ANR project CIPRES, we present here reactive transport works. In a 3D model using ToughReact2, we perform different CO2 leakage scenarios in a confined aquifer, considering both brines and carbon dioxide gas. Our modeling works are based on the Albian aquifer, a strategic water resource for the Paris basin. This layer is the main aquifer overlying the Dogger deep saline aquifer. We consider one groundwater and rocks chemistry of the Albian aquifer composed by the Albian green sand (Quartz, Calcite, Glauconite , Kaolinite) at 700 m deep. The geochemical model was elaborated from experimental data performed in previous study (Humez, 2012). The aquifer consists in a mesh, divided roughly in 20000 cells making a 60 m thick and a 500 m large layer. Furthermore, cells are subdivided near the leakage point to consider local phenomena (secondary precipitation, kinetics, sorption/desorption...). We highlight the importance of surface complexation on trace element transport (As, Zn and Ni). Moreover, we distinguish different geochemical behavior (CO2 plume shape, secondary precipitation of siderite or chalcedony, desorption...) according to different horizontal flow rate influenced directly by the hydrodynamics (regional gradient). Understanding how geochemical reactions and regional flows influence water chemistry, allows to ascertain measurement monitoring and verification plan and remediation works in case of leak considering a given location.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".