An insight into the formation of liquid bridge and its role on fracture capillary pressure during gravity drainage in fractured porous media
Bibliographic record
Abstract
Abstract The formation of liquid bridges can maintain capillary continuity between matrix blocks during gas/oil gravity drainage in fractured reservoirs. A travelling oil drop draining into a fracture either forms a liquid bridge or breaks into detached drops. However, the different characteristics of a travelling drop during its elongation and required conditions for transformation into a liquid bridge are not well described in the published literature. In this work, a one‐dimensional model based on slender‐drop theory is employed that holds gravity, viscosity, and surface tension forces but ignores inertia. This model, together with Young‐Laplace equation, gives the fracture capillary pressure. Then, the effect of liquid influx rate, viscosity, surface tension, density difference, contact angle, and contact radius on the shape, critical (or maximum) volume, and length of travelling liquid drops is analyzed and compared with the results in the absence of flow (ie, pendant drops). Critical length is shown to be an increasing function of both liquid viscosity and the influx rate, but the effect of surface tension and density is somewhat case dependent. Furthermore, it is found that if fracture aperture is equal to or less than the critical length of a drop, the formed liquid bridge would be stable. Also, fracture capillary pressure is shown to be remarkable in thin fractures (apertures of less than 50 μm) with embedded liquid bridge. On the other hand, the suspended oil drops, even when reaching critical length, cannot provide considerable capillary pressure.
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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 | 0.000 |
| 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".