The Viscosity of Methane in Organic Slit Nanopore of Gas-Bearing Shale by Molecular Dynamic Simulation
Bibliographic record
Abstract
Non-Darcy flow is observed in the shale gas reservoir because it is rich in organic nanopores. Generally, the permeability of shale gas reservoirs is modified because of non-Darcy flow. However, the viscosity is much less concerned. It has been verified that the viscosity of dilute gas depends on the size of the pore. In this paper, the viscosity of methane in organic slit nanopore is determined with equilibrium molecular dynamics (EMD) simulation. The result shows that the viscosity of bulk methane would decrease with dropping down pressure, while the confined effect would make the viscosity of methane in the organic slit nanopore lesser than that of the bulk phase, and it decreases severely at low pressure. The confined dense gas viscosity model is obtained by theoretical derivation. The EMD results were fitted with this model to obtain the viscosity correction method for dense methane in organic slit nanopores. The dimensionless viscosity (μeff/μb) would decrease sharply with the Knudsen number between 0.1 and 10. Unlike the confined effect on the dilute gas, the potential contribution of the dense gas and the wall also affects its viscosity. Because of the confined effect on the dense methane, the flow capacity of methane is enhanced 1.5 times at least with the pore being smaller than 10 nm and the pressure being lower than 5 MPa. It means that keeping a low reservoir pressure helps to improve the flow of shale gas. This work can improve the understanding of the importance of gas viscosity with the non-Darcy flow in shale gas reservoirs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".