Pore-Level Investigation of Oil-Mobility Enhancement in Heavy-Oil Reservoirs
Bibliographic record
Abstract
Summary One of the most important properties for understanding multiphase flow in porous media is relative permeability. In two-phase flow, the relative permeability to a given phase is generally assumed only to be a function of its saturation, independent of the properties of fluids involved and/or flow conditions and ranging from zero to unity. Considering the physical principles of multiphase flow through porous media, the momentum transfer or viscous coupling appears as a hidden driving mechanism that might lead to higher than expected oil-flow rates. In an effort to provide a better understanding of oil mobility in heavy-oil reservoirs, a capillary model is used to assess the importance of lubrication in two-phase flow and to determine the effect of the viscous coupling on relative permeabilities. Different cross-sectional geometries are analyzed. The problem is addressed analytically even for the unequal-viscosity case by making use of the Galerkin method. The concept of contact angle is used in the determination of the fluid distribution inside a noncircular channel. The model results show that the viscosity ratio theoretically affects relative permeabilities, especially in systems involving heavy oil.
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 | 0.000 |
| 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 | 0.000 |
| 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".