Bibliographic record
Abstract
Abstract Successfully starting up Steam Assisted Gravity Drainage (SAGD) well pairs is crucial in achieving good wellbore temperature conformance and rapid production ramp-up. Performing a SAGD startup effectively and economically is a significant challenge in the industry and reservoirs with mobile water offer an opportunity to optimize and accelerate this process. A traditional SAGD startup involves steam circulation, where steam is injected into a well down the long tubing string to the toe and fluids are produced back to surface at the heel. Steam circulates across the full horizontal length of the well and conductively heats the near reservoir. In reservoirs with mobile water, there can be fluid losses to the reservoir during circulation, allowing for convective heating of the reservoir. In a bullheading startup, return fluids are not produced and all injected steam is forced to leak off into the reservoir. By avoiding fluid returns, bullheading offers significant advantages over circulation in terms of thermal efficiency, steam demand, operational simplicity and facility requirements. This paper examines the effectiveness of a bullheading startup compared to a circulation startup through a simulation study and field trials at Suncor Energy Inc’s Firebag in-situ project.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".