Pore-scale investigation of immiscible displacement process in porous media under high-frequency sound waves
Bibliographic record
Abstract
Although experimental and theoretical studies have been performed to identify the effects of elastic waves on multi-phase flow in porous structures, the literature lacks finely tuned experiments at the micro-scale. This paper reports observations and critical analysis of immiscible displacement in micro-scale porous media under ultrasonic energy. A number of experiments are performed on homogeneous and heterogeneous micromodels for varying wave frequency and power, initial water saturation, wettability and injection rates. We show that ultrasonic radiation influences the displacement pattern and yields lower residual non-wetting phase (oil) behind when low injection rates are applied. Higher wave frequency results in faster recovery of oil, but the ultimate recovery is controlled mainly by wave intensity. The presence of initial water saturation has a positive effect on the displacement, especially in an oil-wet medium. Of the possible mechanisms suggested for recovery enhancement under ultrasonic radiation, deformation of pore walls and change in fluid properties due to heating are not an issue in these experiments but other mechanisms including coalescence of oil droplets under oscillation, reduction of wetting films, adherence to grains and the peristaltic movement of fluids due to mechanical vibration were observed to be effective and are discussed in the analysis of the visual observations.
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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) | 0.000 | 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".