Combined experimental interpretation and numerical investigation of the impact of fluid alkalinity on basalt carbonation during CO 2 storage
Bibliographic record
Abstract
Various recent studies have shown that basalt formations have the capacity for long-term secure CO 2 storage through carbon mineralization. Many of these studies have demonstrated extremely rapid rates of mineralization, but the underlying mechanism enabling these elevated reaction rates, and their relation to the processes occurring in proposed basaltic reservoirs, remain poorly constrained. In this work, a 3D micro-continuum reactive transport model was designed to investigate the impact of alkalinity on basalt interactions with CO 2 -rich fluids. Reactive transport models were developed in PFLOTRAN based on 3D imaging data from high-temperature, high-pressure flow-through experiments (Luhmann et al. (2017) Chemical Geology, Water Resources Research). Mineral reactive surface areas in the model were adjusted to produce agreement with chemistry of output fluids sampled during the experiments. The benchmarked model showed that no considerable carbonate was formed during interaction with the relatively low alkalinity, low pH solutions, regardless of the enrichment of basalt-derived Na + , Mg 2+ , and Fe 2+ ions in the reactant fluid. Increasing the alkalinity of the injected fluids consistently yielded higher rates of carbon mineralization. Similarly, introducing a small initial volume fraction of carbonate minerals into the system contributed to increased carbon mineralization, because of the increased fluid alkalinity. These results thus reinforce a conceptual understanding of carbonate mineralization in basalt-hosted CO 2 storage reservoirs that emphasizes the importance of aquifer fluid alkalinity, and caution against extrapolating results from elevated-alkalinity CO 2 storage reservoirs and experiments to others where this is less likely to be representative.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".