Formation of liquid bridges between porous matrix blocks
Bibliographic record
Abstract
Abstract It is widely accepted that, in fluid flow and transport in fractured porous media, there exists some degree of block‐to‐block interaction that may lead to capillary continuity. The formation of liquid bridges causing interaction between blocks will affect oil recovery from naturally fractured reservoirs. However, the accurate modeling of the growth and detachment of liquid bridges that may cause capillary continuity between matrix blocks remains a controversial topic. In an attempt to improve our understanding of the problem, a mechanistic model is developed in this work for the formation of liquid bridges between porous blocks. The proposed model considers growth and detachment of pendant liquid droplets perpendicular to the horizontal and smooth fracture between porous matrix blocks. The liquid bridge model is then coupled with various upscaled fracture capillary pressure models to study the liquid bridge formation process. An expression is obtained that relates the commonly used fracture capillary pressure to the critical length of the liquid element. Results based on various fracture capillary pressure models reveal that the threshold Bond number is an important parameter in the formation of liquid bridges. We introduce a simple mechanistic model for the formation of liquid bridges in a horizontal fracture between two porous blocks, advancing our understanding of the two‐phase flow in fractured porous media. © 2010 American Institute of Chemical Engineers AIChE J, 2011
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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 | 0.000 | 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".