The effect of wall roughness on two-phase flow in a rough-walled Hele-Shaw cell
Bibliographic record
Abstract
Many studies focus on the flow of multiple phases in smooth fractures yet most real fractures are rough thus flow regime maps and results for multiphase flow in smooth fractures are not completely applicable to flow in rough fractures. The effect of wall roughness is difficult to understand in multiphase flow in fractures since it leads to heterogeneities of the fracture aperture and potentially alters the roles of capillary and viscous forces in the flow. Here, the effects of wall roughness, fracture orientation, and fluids flow direction within a fracture, modeled as narrow gap in a Hele-Shaw cell, on co-current flow of oil and water were examined. The results are presented in the form of oil and water relative permeability curves. The results demonstrate that roughness impacts phase distribution, flow regimes, and phase relative permeability (a measure of phase interference); roughness increases oil–water phase interference and hysteresis of the flow resistance when scanning up and down in water saturation. Fractal analysis of images of the phase arrangement in the fracture reveals that the fractal dimension (reflects geometry and complexity), lacunarity (gappiness and complexity), and tortuosity relate the complexity of flow and the change in relative permeability behavior. The experimentally derived relative permeability data were fitted to the saturation exponent model and to an equivalent homogenous single-phase model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".