Gas Transport in Shale Nanopores with Mobile High-Viscosity Water Film
Bibliographic record
Abstract
An analytical model for calculating gas velocity profiles and predicting gas apparent permeability enhancement factors in nanopores of shale with nanometer scale characteristic dimensions of different geometries (slit pores and circular pores) is proposed. The proposed model considers the presence of a mobile high-viscosity water film by modifying boundary conditions at a liquid–solid interface and a gas–liquid interface has good agreements with experimental data, and confirms that a mobile high-viscosity water film enhances gas flow capacity. The importance of a mobile high-viscosity water film is further evaluated with a varying pore size, pressure, and surface wettability. In the case of smaller pores and higher pressure, a mobile high-viscosity water film makes more positive contributions to both the gas velocity and a gas apparent permeability enhancement factor. Increasing the contact angle at a solid–water interface implies a reduction in molecular attractions and a decrease in gas flow resistance and thus leads to enhanced gas flow capacity. This study is extended to the case of multiphase flow in nanopores of shale and provides a better explanation of the fluid flow pattern in actual reservoir conditions.
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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 | 0.000 |
| 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) | 0.000 | 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".