Pore Network Modelling of Surface Heterogeneity in Brine-Filled Porous Media for Carbon Sequestration
Bibliographic record
Abstract
Trapping of carbon in deep underground brine-filled reservoirs is a promising approach for the reduction of atmospheric greenhouse gas emissions. However, estimation of the amount of carbon dioxide (CO2) that can be captured in a reservoir remains a challenge. One difficulty lies in the estimation of local capillary pressure effects that arise from mineral surface heterogeneity inherent in underground geological formations. As a preliminary step to address this issue, we present a series of pore network modeling (PNM) simulations of two-phase immiscible flow in 3D structured porous media with contact angle heterogeneity. We present saturation patterns for networks with homogeneous and heterogeneous wettability under typical reservoir conditions, taking into account varying contact angles for CO2 on mica and quartz at supercritical conditions. At lower flow rates, our preliminary results showed higher saturations for the heterogeneous networks than for the homogeneous ones. To characterize the fingering patterns, we have introduced R as the ratio of filled throats to the total network saturation. Based on this measure, the heterogeneous networks demonstrated thicker fingering patterns than the homogeneous networks. These preliminary results highlight the importance of micro-scale surface heterogeneity for the modeling of carbon storage processes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".