Dynamic pore network model 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 (CO(2)) that can be captured in a given reservoir and the long-term storage stability remain 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 have performed dynamic pore network modelling (PNM) simulations of two-phase immiscible flow in two-dimensional structured porous media with contact angle heterogeneity under typical reservoir conditions. We begin by characterizing the network with a single, uniform contact angle. We then present saturation patterns for networks with homogeneous and heterogeneous contact angles distributions, based on two common reservoir minerals: quartz and mica, both of which have been well-characterized experimentally for their brine-CO(2) contact angles. At lower flow rates, we found moderately 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. The computed saturation patterns demonstrate the importance of considering surface heterogeneity in pore-scale modelling 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) | 0.000 | 0.000 |
| 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 | 0.001 | 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".