Experimental investigation of nonequilibrium capillarity effects: Fluid viscosity effects
Bibliographic record
Abstract
Numerical models have been widely used to simulate multiphase flow in porous media for a variety of applications (e.g., NAPL migration in subsurface aquifers, carbon sequestration, agriculture, paper production, and petroleum reservoir development). The relationship between the difference in phase pressures and saturation is used as one of the important constitutive relationships in numerical models. Theoretical studies have suggested that this relationship should include a damping coefficient or capillarity coefficient ( ) on the basis of thermodynamic considerations. A literature review suggests that the magnitude of this capillarity coefficient varies by over three orders of magnitude. While recent experimental studies have explored the effect of porous medium properties, effect of domain size, hysteresis, and the imposed boundary conditions on the magnitude of , there has been no experimental study investigating the impact of fluid viscosity on . This study reports on a series of primary drainage experiments conducted under both static and dynamic conditions in F70 silica sand. Fluid pairs used included water and silicone oil with two differing viscosities and slightly different densities (used as model nonaqueous phase liquids) in addition to air. Water saturation and both wetting and nonwetting phase pressures were measured in a custom‐built aluminum column using EC‐5 probes and tensiometers at three levels. Results show a strong dependence of the magnitude of the capillarity coefficient on effective fluid viscosity. This implies that consideration should be given for the inclusion of a capillarity coefficient in modeling tools used to simulate multiphase flow when fluids saturations are changing rapidly and when fluids have a large viscosity ratio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".