Bibliographic record
Abstract
Abstract Adverse mobility ratio at the flood front in displacement processes due to temperature difference between the injected fluid and the medium and/or the inhabitant fluid may result in an instability known as thermo-viscous fingering (TVF). In practice such processes mostly take place in heterogeneous environments where the heterogeneity of the medium alters the preferred path of the flow and interacts with TVF. TVF is encountered in thermal-based enhanced oil recovery processes such as hot water flooding, steam flooding, CSS and SAGD or in fracturing. The fingers growth and development can have either favorable or detrimental impacts on the efficiency of these processes. The effects of adverse thermal or solutal mobility gradients and heterogeneity on the instability are quantified. Concentration, temperature, viscosity, and velocity fields have been generated using nonlinear simulations and studied for a better understanding of the mechanism of fingering at different log mobility ratios, heat exchange rates, and heterogeneity profiles. TVF in fields with wider channels is very similar to that in homogeneous fields and different mechanisms for finger development take place in the highly permeable layers. Heterogeneity works in favor of stability as the number of layers increases. In fields with many layers, the fingers induced by heterogeneity diffuse across the narrow channels and produce a widely diffused front which results in even less instability than in a homogeneous field. Heat exchange with the medium causes the thermal front to lag behind the solutal front and induce backward fingering (for unstable thermal front) or build a stable wall following the solutal front (for stable thermal front) and hence affect the sweep efficiency. The mechanism of TVF in heterogeneous porous media and its interaction with channeling are not well understood. Further studies in this field can illuminate the important factors affecting the displacement of heavy oil in Alberta fields.
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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 | 0.000 | 0.001 |
| 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".