Fundamentals of Heat Transport at the Edge of Steam Chambers in Cyclic Steam Stimulation and Steam-assisted Gravity Drainage
Bibliographic record
Abstract
Abstract Currently, large-scale commercial in-situ recovery of bitumen from oil sands reservoirs is done by thermal gravity drainage oil sands recovery processes such as cyclic steam stimulation (CSS) and steam-assisted gravity drainage (SAGD). In these processes, steam is injected into the formation, which then heats bitumen until it is sufficiently mobile enough to be moved to the production well. The key goal of these processes is controlled and targeted steam delivery, really heat delivery, to the reservoir, and thus, both heat transfer and consequent bitumen mobilization are key controls on the performance of the processes. Here, we describe conductive and convective heat transfer at the edge of steam chambers and oil mobilization just beyond the edge of the chamber. The results demonstrate the complex interplay between heat transfer and oil mobilization: heat transfer controls the temperature profile beyond the edge of the steam chamber, which sets the oil viscosity profile, which in turn controls bitumen mobilization. Relative permeability, geomechanics, and geologic heterogeneity make the phenomena more complex. The discussion suggests that self-corrective robust recovery processes or dynamic well interventions such as smart wells that yield uniform steam chambers are required to ensure efficient heat transfer and oil mobilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".