Simulation of bench-scale CO2 injection using a coupled continuum-discrete approach
Bibliographic record
Abstract
Unintended releases of CO 2 from carbon capture and storage operations presents the risk of atmospheric emissions and groundwater or surface water quality impacts. Given the potential impacts, it is valuable to have tools capable of predicting groundwater concentrations and likely pathways of CO 2 migration in the subsurface. Traditional multiphase flow models struggle to simulate the discontinuous flow expected at leakage sites. This work applied a coupled continuum-discrete model, ET-MIP, to simulate a bench-scale injection of CO 2 . Results demonstrate the capability of ET-MIP to accurately capture gas fingering behaviour, and the complexity of multicomponent mass transfer observed in the experiment. Simulations were computationally efficient, allowing for the use of multiple displacement pressure realizations. CO 2 migration in the subsurface was shown to be sensitive to mass transfer, as i) increased groundwater velocity can dissolve leaked CO 2 prior to reaching the surface and ii) background dissolved gases in the subsurface can impact the rate of upwards gas movement, gas distribution, and the composition and persistence of the gas phase. The sensitivity to mass transfer suggests it may be preferable to monitor for low-solubility gases in the source mixture rather than CO 2 . These findings are applicable to other gases in the subsurface, such as hydrogen or methane migrating from geoenergy wells. • Unintended CO 2 releases from CCS present risk of atmospheric and water impacts. • Coupled continuum-discrete model (ET-MIP) simulated bench-scale CO 2 injection. • ET-MIP was able to accurately model gaseous and aqueous CO 2 migration. • Monitoring low-solubility gases is preferable to CO 2 , due to mass transfer dynamics. • Appearance of gas in subsurface may not indicate CO 2 presence due to mass transfer.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".