Analysis of the heat losses associated with the SAGD visualization experiments
Bibliographic record
Abstract
Dealing with the heat losses associated with steam-assisted gravity drainage (SAGD) experiments has been an issue for which different heat loss prevention techniques have been developed and utilized in the literature. The aim is to minimize the amount of heat losses from the porous medium to the surrounding environment. Excessive heat losses negatively affect quantifying the energy requirements of the SAGD experiments. In this study, an inverted-bell vacuum chamber was employed to minimize the excessive heat losses while steam was injected under different superheating levels. Local temperatures along the glass micromodels’ height and width were recorded on a real time basis. Details of the heat losses associated with our pore-scale SAGD visualization experiments are described in this paper. According to the results presented, employing extremely low vacuum conditions resulted in effective heat loss prevention in a sense that the convective element of heat loss could be neglected. As a result, radiation heat transfer was the only mechanism of heat transfer that contributed to the heat loss from the micromodels’ surfaces. In each pore-level SAGD experiment, the overall steam consumption to produce one unit of the mobile oil was corrected based on the heat loss analysis of the process to account for the additional volume of steam which was condensed because of the heat loss. The net cumulative steam consumed, corrected for the heat losses, was in very good agreement with the predictions made based on the theory of gravity drainage and its application in performance analysis of the SAGD process.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".