Simulating volume-controlled invasion of a non-wetting fluid in volumetric images using basic image processing tools
Bibliographic record
Abstract
A new algorithm is presented for simulating volume-controlled invasion of a non-wetting phase into voxel images. This method is complementary to the traditional morphological image opening method which mimics pressure-based invasion. A key advantage of the volume-based approach is that all saturations between 0 and 1 can obtained rather than the irregularly and widely spaced saturation steps obtained by pressure-based methods. Because of the incremental increases in saturation, it becomes possible to correctly predict defending phase trapping, which is not the case when pressure-based steps are applied. The algorithm is validated against morphological image opening and obtains near perfect agreement at equal saturations as expected from theory. It is also demonstrated that a volume-controlled capillary pressure curve can be obtained that displays the characteristic jumps in capillary pressure, and moreover, the envelop of peak pressures yields the pressure-based capillary pressure obtained by morphological opening, so in fact the results of the proposed algorithm are a superset of the morphological approach. Finally, results are compared to multiphase lattice Boltzmann and qualitatively similar results were achieved in substantially less time. The lattice Boltzmann method is more flexible in terms of variable contact angle and inclusion of viscous effects, but for quasi-static volume-based injection of a non-wetting fluid, the proposed method is viable alternative.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".