Characterization of a Two-Phase Pore-Scale Network Model of Supercritical Carbon Dioxide Transport Within Brine-Filled Porous Media
Bibliographic record
Abstract
Sequestration of carbon dioxide in deep underground reservoirs has been discussed for the reduction of atmospheric greenhouse gas emissions in the short- to medium-term until more sustainable technologies are available. Cost and long-term stability are major factors in adoption, so techniques to improve the storage efficiency and trapping security are essential. Such improvements require modeling of the porous geological formations involved in the sequestration process, and comparison to both lab- and field-based experimental studies. To this end, we are developing a comprehensive, large-scale pore-network model to describe multi-phase flow in porous media, including the structural, dissolution, and mineral trapping regimes. To explore the optimal operating parameters for mineralization trapping, we describe a two-phase pore-network model of brine-saturated aquifers and model the invasion of supercritical carbon dioxide (CO2) into the pore structure. Regularly-aligned 2D and 3D pore networks are constructed, and rules-based transport models are used to characterize the saturation behavior over a range of viscosity and capillary parameters, and coordination numbers. Finally, saturation patterns are presented for model caprock and sandstone reservoir conditions, taking into account different contact angles for CO2 on mica and quartz at supercritical conditions. These saturation patterns demonstrate the importance of surface heterogeneities in pore-scale modeling of deep saline aquifers.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".