Role of Viscous Forces in Foam Flow in Porous Media at the Pore Level
Bibliographic record
Abstract
A pore network model is proposed to simulate the complex process of in situ foam generation, destruction, and propagation as a drainage process. Motivated by the need to account for viscous flow effects, which arise from the viscous drag of moving bubbles, the statistical physical method of the modified invasion percolation with memory algorithm is extended. The model is capable of capturing the flow characteristics of (weak) continuous gas and (strong) discontinuous gas foams based on a minimum number of input parameters, that is, pore throat size distribution, regeneration probability, network dimensionality, and size. The dependence of the flowing foam fraction on the applied pressure gradient is predicted. The steady-state relative permeabilities during the simultaneous flow of a liquid and a gas, with lamella generation, destruction, and mobilization by flow displacement, are computed. The proposed model adequately portrays literature results with the decrease in the gas relative permeability upon introduction of foams in agreement with the reported results for similar porous media. The results find application in optimizing the current population balance models and guide foam-based enhanced oil recovery projects.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".