Coupled Vadose Zone and Atmospheric Surface-Layer Transport of Carbon Dioxide from Geologic Carbon Sequestration Sites
Bibliographic record
Abstract
Geologic CO 2 sequestration is being considered as a way to offset fossil fuel–related CO 2 emissions to reduce the rate of increase of atmospheric CO 2 concentrations. The accumulation of vast quantities of injected CO 2 in geologic sequestration sites may entail health and environmental risks from potential leakage and seepage of CO 2 into the near-surface environment. We are developing and applying a coupled subsurface and atmospheric surface-layer modeling capability built within the framework of the integral finite difference reservoir simulator TOUGH2. The overall purpose of the modeling studies is to predict CO 2 concentration distributions under a variety of seepage scenarios and geologic, hydrologic, and atmospheric conditions. These concentration distributions will provide the basis for determining aboveground and near-surface instrumentation needs for CO 2 sequestration monitoring and verification, as well as for assessing health, safety, and environmental risks. A key feature of CO 2 is its large density (ρ = 1.8 kg m −3 ) relative to air (ρ = 1.2 kg m −3 ), a property that may allow small leaks to cause concentrations in air above the occupational exposure limit of 4% in low-lying and enclosed areas such as valleys and basements where dilution rates are low. The approach we take to coupled modeling involves development of T2CA, a TOUGH2 module for modeling the multicomponent transport of water, brine, CO 2 , gas tracer, and air in the subsurface. For the atmospheric surface-layer advection and dispersion, we use a logarithmic vertical velocity profile to specify constant time-averaged ambient winds, and atmospheric dispersion approaches to model mixing due to eddies and turbulence. Initial simulations with the coupled model suggest that atmospheric dispersion quickly dilutes diffuse CO 2 seepage fluxes to negligible concentrations, and that rainfall infiltration can cause CO 2 to return to the subsurface as a dissolved component in infiltrating rainwater.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".