Water-Alternating-Gas (WAG) injection scheme for enhancement of carbon dioxide mineralization in basaltic aquifers
Bibliographic record
Abstract
• WAG injection achieves higher mineral carbonation compared to supercritical CO 2. • Higher WAG ratio accelerates CO 2 mineralization. • WAG cycle frequency has no considerable effect on CO 2 mineralization. • Economic metrics for higher WAG ratios in submarine basaltic aquifer are unfavorable. Carbon storage in basaltic aquifers has demonstrated enormous potential for securely and permanently storing carbon dioxide (CO 2 ) through basalt carbonation. Recent experimental and pilot-scale studies investigating CO 2 injection as either a supercritical or dissolved phase have shown that dissolved phase injection results in faster mineralization than the supercritical phase scenario because it bypasses the rate-limiting CO 2 dissolution step. However, to meet the magnitude of the climate crisis, dissolved CO 2 injection schemes will likely need considerable improvements in efficiency to be implemented at gigaton-per-year levels. Here, we consider whether water-alternating-gas (WAG) injection, long been used in the hydrocarbon industry to enhance extraction efficiencies, can increase injection volumes and reduce energy requirements, while promoting CO 2 dissolution and improving mineralization efficiency. We performed a series of reactive transport simulations of WAG injection (alternating supercritical CO 2 and water) into a submarine basaltic aquifer in the Cascadia basin, an area under active investigation for basalt carbonation demonstration. The findings indicate that implementing WAG and optimizing the injection parameters improves mineralization up to 20% by increasing the quantity of CO 2 in the dissolved phase, which, in turn, allows for greater extents of reaction between dissolved CO 2 and basalt. Our results indicate that, in lieu of implementing fully dissolved CO 2 injection at the field scale, implementing and optimizing WAG schemes for CO 2 mineralization in the basaltic oceanic crust can offer significant advantages over supercritical CO 2 injection. However, the economics of WAG injection are heavily impacted by WAG frequency and water handling capacity, particularly in offshore environments, as considered in this study. Nevertheless, our results indicate WAG injection schemes should be strongly considered when developing a site-specific injection strategy capable of achieving large-scale carbon mineralization in basaltic aquifers. Our findings also stress the importance of conducting appropriate assessments, such as quantifying a workable WAG slug ratio and injection period, along with economic analyses, to develop feasible site-specific large-scale carbon dioxide mineralization strategies in basalts.
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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) | 0.001 | 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 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".