Observations Relating to Non-Condensable Gasses in a Vapour Chamber: Phase B of the Dover Project
Bibliographic record
Abstract
Abstract Non-Condensable Gasses (NCG) are gasses such as carbon dioxide, hydrogen sulphide, methane and nitrogen which can be present in a SAGD steam chamber but do not condense into the liquid phase to any large degree. Recent theoretical and laboratory work has shown that these gasses can enhance the thermal efficiency of SAGD without a significant reduction in productivity. In the laboratory, NCG has been shown to finger into the bitumen ahead of the main steam chamber, bringing with it heat and pressure. This paper looks at temperature and pressure data from the world’s most mature SAGD pilot, Phase B of the Dover project, to determine whether naturally occurring NCG in the form of solution gas can be detected fingering ahead of the main steam front. The paper looks at the propagation of the steam chamber front by examining thermocouple data from observation wells drilled within and outside the SAGD pattern. The steam chamber propagation velocity, which also determines the velocity of the pressure front, is calculated. The pressure build-up profile is used to determine the pressure diffusivity constant, which in turn determines where the pressure front would be located without any NCG fingering. The actual pressure front is compared to the predicted. In every case the actual pressure front travels between five and twelve meters ahead where one would expect it from the theory.
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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 | 0.001 | 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".