Bibliographic record
Abstract
Abstract During Steam Assisted Gravity Drainage (SAGD) process, a steam chamber is formed due to continous steam injection. This steam chamber first moves upward to the top of the reservoir and then spreads side ways. The upward displacement of the steam chamber is the key parameter, for determining the optimum rate of steam injection. Slow injection rate will give low oil recovery where as high injection rate will cause steam loss. Furthermore, the cost of steam is more than half of the total cost of the project. Therefore it is necessary to determine the accurate rate of steam chamber rise to make the process economic. Recent experiments show that oil flow is coupled to water flow during three-phase gravity drainage in water-wet system. In this paper, we argue that this type of flow coupling can be significant during SAGD. We extend Butler (1987) analytic model for the rise of interfering steam chambers to account for three phase flow and flow coupling. We also show the significance of flow coupling by solving a simple numerical example. We observe that by including three-phase drainage and the coupling flow in the steam chamber, the vertical rise velocity of steam chamber increases. Moreover the steam chamber rise velocity is sensitive to viscosity-temperature characterstics (m) of oil. As the value of m decreases the rise velocity increases. Furthermore, we compre the model predictions with measured values from five fields, and one experiment.
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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".