Quantitative prediction of residual wetting film generated in mobilizing a two-phase liquid in a capillary model
Bibliographic record
Abstract
This research studies the motion of immiscible two-phase liquid flow in a capillary tube through a numerical approach employing the volume of fluid method, for simulating the core-annular flow and water flooding in oil reservoirs of porous media. More specifically, the simulations are a representation of water flooding at a pore scale. A capillary tube model is established with ANSYS Fluent and verified. The numerical results matches well with the existing data available in the literature. Penetration of a less viscous liquid in a liquid of higher viscosity and the development of a residual wetting film of the higher viscosity liquid are thoroughly investigated. The effects of Capillary number, Reynolds Number and Viscosity ratio on the residual wetting film are studied in detail, as the thickness is directly related to the residual oil left in the porous media after water flooding. It should be noticed that the liquids considered in this research can be any liquids of different viscosity not necessarily oil and water. The results of this study can be used as guidance in the field of water flooding.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".