Steam-on-a-chip for oil recovery: the role of alkaline additives in steam assisted gravity drainage
Bibliographic record
Abstract
We present a lab-on-a-chip approach to informing thermal oil recovery processes. Bitumen - a major global resource - is an extremely viscous oil which is extracted by injecting steam underground in a process known as Steam Assisted Gravity Drainage (SAGD). Here, a microfluidic network saturated with bitumen provides a physical model of the SAGD reservoir; steam is injected into the chip, and the oil recovery dynamics are visualized and quantified in real-time. The unique advantage of this approach is the pore-scale quantification of fluid phase dynamics under relevant reservoir conditions and pore sizes. High resolution is achieved by leveraging the inherent fluorescence of the native bitumen. The approach is applied to quantify the efficacy of an alkaline steam additive. With the additive, the mean characteristic size of oil-in-water emulsions formed during SAGD is reduced from 150 μm to 6 μm, and the corresponding recovery effectiveness is improved by ~50%. These results demonstrate that pore-scale process quantification enabled by lab-on-a-chip methods can improve the efficacy, and the associated carbon footprint, of energy intensive thermal oil recovery processes.
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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".