An approach to model three‐phase flow coupling during steam chamber rise
Bibliographic record
Abstract
During steam assisted gravity drainage (SAGD) process, a steam chamber forms due to continuous steam injection. This chamber first moves upward to the top of the reservoir and then spreads sideways. The upward chamber displacement is one of the key factors for optimising the steam injection rate. It is necessary to determine the accurate chamber rise velocity for predicting the oil recovery rate. Recent experiments show that oil flow is coupled to water flow during three‐phase gravity drainage in water‐wet systems. In this paper, we argue that this type of flow coupling can be significant in SAGD operations. We extend Butler's [Butler, J. Can. Petrol. Technol. 1987, 26, 70] and Gotawala and Gates [Gotawala and Gates, Can. J. Chem. Eng. 2008, 86, 1011] analytic models for the rise of interfering steam chambers to account for three‐phase flow and flow coupling. We also show the importance of flow coupling by solving a simple numerical example. We observe that by including three‐phase drainage and the flow coupling at the steam finger edge, the vertical rise velocity of a steam chamber increases. Moreover, the rise velocity is very sensitive to the coupling term introduced in the new models. Even a small value of the coupling term increases the rise velocity significantly. Furthermore, we compare the model predictions with the values measured in six fields.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".