Measurement of emulsion flow in porous media: Improvements in heavy oil recovery
Bibliographic record
Abstract
Many heavy oil and bitumen reservoirs in the world are too small or thin for thermal enhanced oil recovery methods to be economic. In these fields, novel methods of less energy intensive, non-thermal technologies are required. Previous experience has shown that the injection of low concentrations of aqueous alkali-surfactant solutions into the reservoir can significantly improve the oil recovery, beyond that of waterflooding. This is due to the in-situ formation of emulsions, which plug off the water channels and lead to improved sweep efficiency in the reservoir. The proper control of these floods requires methods for monitoring the formation and effect of these emulsions. In this paper, the results of laboratory core floods are interpreted to demonstrate how the pressure and flow response can be related to the formation of these emulsions. A new technique (low field NMR) is also used to directly measure W/O emulsions in porous media. Finally, a numerical study is performed in order to demonstrate how the in-situ formation of emulsions can be simply represented in simulation software.
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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 | 0.001 | 0.001 |
| 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".