On Temperature-Falloff Interpretation in the Circulation Phase of the Steam-Assisted-Gravity-Drainage Process
Bibliographic record
Abstract
Summary Steam-assisted gravity drainage (SAGD) is the preferred method to extract bitumen from Athabasca oil-sand reservoirs in western Canada. Bitumen at reservoir conditions is immobile because of high viscosity, and its saturation is typically large, which limits the injectivity of steam at in-situ conditions. In current industry practice, steam is circulated within injection and production wells. In theory, wells should be converted to SAGD production mode after a period when bitumen is mobile and communication is established between the injector and the producer. Operators use temperature-falloff data to predict successful conversion time. But temperature-falloff data are evaluated qualitatively, and there is not an analytical/numerical framework in which one can use such data. Although the bitumen heating sounds simple, approach wells are failing after steam injection because of steam breakthrough or sand production. Most of these wells are periodically returned to circulation/bullheading to ramp up production rates and heal the hot spots. Most of such failures are associated with early conversion to full SAGD, which shows the need to formulate an analytical/numerical framework to predict the right timing for conversion to full SAGD. In this presentation, the time of flight (ToF) is effectively used to convert spatial variations of temperature into time response of temperature variation at the well sandface. ToF defines the time an oil droplet needs to travel through a medium—more specifically, from its current location to the well sandface. By solving the heat transfer and Darcy's law simultaneously, the ToF is converted to a relationship of the temperature vs. time profile at the producer. This approach has been applied to SAGD well pairs with different geology, and the temperature-falloff trends are presented.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".