Quantification of Fluid Saturations in Transparent Porous Media
Bibliographic record
Abstract
Core Ideas Imaging techniques are powerful for investigating multiphase flow in porous media. Transparent porous media can be used to quantify local fluid saturations. Efficient methods to calibrate intensity–saturation relationships are required. Our new procedure uses fewer images and measurements. Predicted saturations based on calibrated images matched independent measurements. Experiments using transparent porous media, where the indices of refraction of the solid grains and the wetting fluid are matched, can be used to quantify fluid saturations from digital images. In this study, significantly more efficient calibration and validation methods for unsaturated transparent porous media were developed, which used just three images and one saturation measurement. Imbibition and drainage column experiments were used to define the pixel intensity and saturation at residual wetting fluid saturation, as well as at residual nonwetting fluid saturation in two gradations of transparent porous media used for validation. The other images were pixel intensity at 100 and 0% wetting fluid saturation. Results from the drainage and imbibition experiments on the two transparent porous media gradations showed a log‐linear saturation–pixel intensity relationship, which agreed with the validation points from this study as well as those using a previous calibration method. We expect that this new calibration procedure will allow efficient development of saturation–pixel intensity relationships for the investigation of multiphase flow using transparent porous media under a variety of conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".