Trace element mobility during CO<sub>2</sub>storage: application of reactive transport modelling
Bibliographic record
Abstract
The geologic storage of CO2carries both physical and chemical risks to the environment. In order to reduce those risks, it is necessary to provide predictive capabilities for impacts so that strategies can be developed to monitor, identify and mitigate potential problems. One area of concern is related to water quality both in the reservoir and in overlying aquifers. In this study we report the critical steps required to develop chemically constrained reactive transport models (RTM) that can be used to address risk assessment associated with water quality. The data required to produce the RTM includes identifying the individual hydrostratigraphic units and defining the mineral and chemical composition to sufficient detail for the modelling. This includes detailed mineralogy, bulk chemical composition, reactive mineral phase chemical composition and the identification of the occurrence and mechanisms of mobilisation of any trace elements of interest. Once the required detail is achieved the next step involves conducting experiments to determine the evolution of water chemistry as reaction proceeds preferably under varying elevated CO2fugacities with and without impurities. Geochemical modelling of the experiments is then used for characterising the reaction pathways of the different hydrostratigraphic units. The resultant geochemical model inputs can then be used to develop the chemical components of a reactive transport model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".