Development of a Microfluidic Device for Rapid Assessment of EOR Additives
Bibliographic record
Abstract
Abstract Microfluidics permits small scale economically controllable testing/analysis environments, and has been leveraged in biochemistry and the life sciences to access previously unavailable efficiencies, most notably in DNA sequencing, protein analysis, and soft material (e.g. tissues, emulsions) synthesis. Recently, these techniques have been applied to petroleum science and the results show promise. Methods have been reported in the literature for studying CO2 diffusion, investigating reservoir fluid phase behaviour, and asphaltene content measurement. These methods drastically reduce sample volume (from litres to nanolitres) and measurement time (from several hours or days to 30 minutes or less) requirements, while maintaining or increasing accuracy offered by traditional methods. In the present study, we used microfluidics to simulate the situation where emulsions form in the SAGD process in two situations; pure steam injection and steam + additives injection. Emulsions were generated in pore-scale geometries (~ 100 µm) without and with an alkaline additive. It was found that alkaline solutions produced finer emulsions (by up to nn%). A weak dependence of mean emulsion diameter on additive concentration was also observed. In addition to informing improvements to EOR, the platform may be adapted for use in studying emulsions formed during oil and gas processing as well (e.g. in valves, pumps).
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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 | 0.000 |
| 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".