Accelerating CO<sub>2</sub> Dissolution in Saline Aquifers for Geological Storage — Mechanistic and Sensitivity Studies
Bibliographic record
Abstract
One of the important challenges in geological storage of CO 2 is predicting, monitoring, and managing the risk of leakage from natural and artificial pathways such as fractures, faults, and abandoned wells. The risk of leakage arises from the buoyancy of free-phase mobile CO 2 (gas or supercritical fluid). When CO 2 dissolves into formation brine, or is trapped as residual phase, buoyancy forces are negligible and the CO 2 may be retained with minimal risk of leakage. Solubility trapping may therefore enable more secure storage in aquifer systems than is possible in dry systems (e.g., depleted gas fields) with comparable geological seals. A crucial question for an aquifer system is, what is the rate of dissolution? In this paper, we address that question by presenting a method for accelerating CO 2 dissolution in saline aquifers by injecting brine on top of the injected CO 2 . We investigate the effects of different aquifer properties and determine the rate of solubility trapping in an idealized aquifer geometry. The acceleration of dissolution by brine injection increases the rate of solubility trapping in saline aquifers and therefore increases the security of storage. We show that, without brine injection, only a small fraction (less than 8%) of the injected CO 2 would be trapped by dissolving in formation brine within 200 years. For the particular cases studied, however, more than 50% of the injected CO 2 dissolves in the aquifer as induced by brine injection. Since the energy cost for brine injection can be small (<20%) compared to the energy required for CO 2 compression for a 5-fold increase in dissolution, such reservoir engineering techniques might be viable and practical for accelerating dissolution of CO 2 . The environmental benefit would be to decrease the risk of CO 2 leakage at reasonably low cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".