On Temperature Fall-Off Interpretation in Circulation Phase of Steam-Assisted Gravity Drainage Process
Bibliographic record
Abstract
Abstract Steam-assisted gravity drainage (SAGD) is the method of choice to extract bitumen from Athabasca oil sand reservoirs in Western Canada. Bitumen at reservoir condition is immobile due to high viscosity and its saturation is typically large that limits the injectivity of a steam at in-situ condition. In a current industry practice, steam is circulated within injection and production wells. In theory wells, should be converted to SAGD production mode once bitumen at interval is mobile and communication is established between the injector and the producer. Operators try to use temperature fall-off data to predict successful conversion time. Although the bitumen heating sounds simple approach recently three wells fails after steam injection due to steam break-through or sand production. And they are periodically returned to circulation to ramp up production rates and heal the hot spots. Most such failures are associated with early conversion to full SAGD. This paper presents a method to describe physics based initial steam injection timing and describes different Suncor assets viscosity variation with temperature and a proper interval temperature for initiation of steam injection in SAGD process. In this study an analytical tool is developed using time-of-flight (ToF) concept to match the temperature fall-off data. The tool is used to discuss successful and failed cases in Suncor McKay River asset.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".