Numerical Modeling for Assessing the Effects of Thermodynamic Properties on CO2 Storage in Saline Aquifers
Bibliographic record
Abstract
Summary According to the Intergovernmental Panel on Climate Change (IPCC), global CO2 emissions must be reduced by 50 to 80 percent by 2050 to avoid dramatic consequences of global warming. Geological storage of CO2 in saline aquifers is a promising method for reducing atmospheric CO2 concentration. For this aim, accurate modeling of CO2 sequestration into underground formations (saline aquifers) is required. In petroleum industry, normally this is achieved by using compositional reservoir simulators which is computationally expensive and time consuming. To overcome this, an accurate fluid model was coupled to a flow simulator to model CO2 sequestration in saline aquifers. Next, sensitivity analyses of thermodynamic properties (pressure, temperature and salinity) were done on some saline aquifers of Alberta basin, in Canada to investigate the effects of thermodynamics properties on CO2 dissolution in these aquifers. Results show that salinity has the strongest effect on CO2 dissolution in our studied aquifers compared to temperature and pressure effects. Results of this study enable us to assess the potential of each saline aquifer for CO2 storage and therefore help us in selecting suitable injection sites for CO2 sequestration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".