The impact of background water flow on the early migration of a CO2 plume in a tilted aquifer during the post-injection period
Bibliographic record
Abstract
The study presents a numerical modelling analysis on CO2 plume migration in a dipping storage aquifer with background flux, which incorporates residual and dissolution trapping of CO2. The purpose of this analysis is to investigate the effect of the background flow velocity on the CO2 plume migration during the early post-injection period. Different velocities of groundwater flow from low to high were considered in the aquifer model. The distribution, migration distance and velocity of the injected CO2 plume as well as the remaining mobile CO2 plume extent are estimated to determine how fast and far the plume propagates with time. Comparison of the results indicate that increasing the background flux velocity causes the plume to migrate longer distances up-dip, while it reduces the height distribution of the plume with time. This reduces the volume of mobile CO2 in the storage aquifer at larger velocities of background flux, hence decreasing the leakage risk of CO2 to the surface. In addition, the CO2 plume decelerates immediately after cessation of injection as its bottom rises vertically and the buoyancy force reduces as the thickness of the plume reduces. However, the plume then accelerates during the initial period of its subsequent lateral migration, as the plume becomes extended, and the buoyancy forces increases somewhat. The degree of lateral extension increases with increasing background water flow velocity, with the leading tip of the plume migrating faster than the trailing edge, until residual and dissolution trapping sufficiently reduce the volume of free phase CO2 that its migration is arrested. Document Type: Original article Cited as: Awag, M., Mackay, E., Ghanbari, A. The impact of background water flow on the early migration of a CO2 plume in a tilted aquifer during the post-injection period. Advances in Geo-Energy Research, 2023, 9(2): 125-135. https://doi.org/10.46690/ager.2023.08.06
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".