Computation of the two‐phase flow properties of intermediate‐wet porous media: A pore network approach
Bibliographic record
Abstract
Abstract A pore‐and‐throat network including fractal‐like roughness features along its surface is employed to simulate primary drainage and secondary imbibition by accounting for the quasistatic motion of menisci in pores and throats and varying the contact angle from 0° (strongly water‐wet conditions) to 180° (strongly oil‐wet conditions). The angle of sharpness of roughness features defines a range of contact angles within which the cross‐section of the throat or pore is occupied completely by the one fluid and conditions of intermediate wettability are established. In contrast, outside this range, both fluids may coexist in a pore or throat. Such differences on the fluid distribution at the pore level affect strongly the capillary, electrical and hydraulic properties of the porous medium and are reflected in the capillary pressure, resistivity index and relative permeability curves. The simulator is used to calculate the aforementioned two‐phase flow coefficients as the pore system transits from strongly water‐wet or strongly oil‐wet to intermediate‐wet. The capillary pressure curves are always sensitive to the wetting state and the particular value of the contact angle. The relative permeability and resistivity index curves for secondary imbibition are grouped in families of curves which are sensitive mainly to the wetting state (water‐wet, intermediate‐wet/water‐wet pores, intermediate‐wet/oil‐wet pores, oil‐wet) rather than to the particular value of the contact angle.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".