Scale‐up of pore‐level relative permeability from micro‐ to macro‐scale
Bibliographic record
Abstract
Abstract Scaling up relative permeability curves of wetting and nonwetting phase of drainage and imbibition processes from pore scale to macro scale is a challenge. A new method for scaling up relative permeability from micro‐ to macro‐scale is proposed based on electrical analogy of multiphase fluid flow at pore scale. The method is validated against four synthetic porous media generated using homogeneous and heterogeneous grain size distributions, each of which were cut into eight sub‐segments. Single‐phase and two‐phase flow properties were calculated for the main blocks and the subsequent sub‐segments using random network modelling technique. Then, the subsegments were randomly distributed in space to reconstruct the main blocks and the proposed scale‐up method was employed to calculate the relative permeability curves of the reconstructed blocks. Results were compared to the ones obtained directly from the network model of the original blocks and show good agreement between the calculated and scaled‐up relative permeability curves of primary drainage and secondary imbibition. Furthermore, the model was tested on real media. Eight network models were extracted from pore size distribution of core samples obtained from the Green River basin located in the Mesaverde Formation. Flow properties obtained from the network models were validated against experimental data and good agreement was observed. These network models show a higher level of heterogeneity at micro‐scale. Then, the scale‐up methods were employed in order to reconstruct the macro‐scale sample and predict its properties. Scale‐up methods successfully predict the single‐phase and two‐phase flow properties of the sample.
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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 | 0.001 |
| 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".