Nanoscale Free Gas Transport in Shale Rocks: A Hard-Sphere Based Model
Bibliographic record
Abstract
Abstract Shale gas is a very important energy resource for humans in the 21st century. However, the mechanism underlying the transoport behavior of shale gas in nanopores (typically 1 nm to 100 nm) remains a huge challenge in industries, as well as in research. In this study, we investigated the free gas transport in nanopores of shale rocks by using the real gas equation of state (EOS) and elastic hard-sphere (HS) model. Excellent results were obtained from the validation of the real gas model on the basis of molecular simulation and experimental data. This paper discusses the following: (1) the model efficiently and reasonably describes the known gas transport behavior in nanopores by establishing the relationship among real gas effect, molecular interactions and collisions, and gas transport behavior; (2) the use of real gas HS EOS considers repulsion, which reduces Knudsen diffusion and laminar slip flow conductance. In addition, packing fraction in EOS provides minimum boundaries for Knudsen number and flow regime; (3) the molecule-wall collision is mainly dominated by pore diameter, and the intermolecular collision is mainly dominated by pressure in nanopores. Under 10 MPa, the molecule-wall collision dominates in nanopores. Otherwise, the intermolecular collision dominates; (4) the laminar slip flow conductance increases with the corresponding increase in strength of intermolecular collision. With increased strength in the molecule-wall collision, Knudsen diffusion conductance increases, thereby improving the transport efficiency, as shown by apparent permeability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".