Improving the SAGD Performance by Introducing a New Well Configuration
Bibliographic record
Abstract
Summary Steam-assisted gravity drainage (SAGD) is a commercially successful recovery process that produces heavy oils and bitumen. The method ensures both a stable displacement front of steam and economical rates by using gravity as the driving force with a pair of horizontal wells for injection/production. Although several ways of improving the performance have been discussed in the literature, the well configuration employed in the process has remained the same as originally proposed by Butler et al. (1981). A systematic attempt to improve the performance by using radically different well configurations has not been reported. This paper presents a study intended to examine the applicability of a new well configuration to SAGD processes in Athabasca and Cold Lake reservoirs in central and northern Alberta. The fully implicit thermal-reservoir simulator, CMG's STARS 2007, with fully coupled wellbores was used to account for frictional pressure drop and heat losses along the wellbore. 3D numerical simulation models were set up, and sensitivity analyses were conducted on injection pressure. After optimization of the injection pressure, an investigation of new well configurations was conducted using these models. The result of this work shows that the SAGD-process performance in Athabasca and Cold Lake reservoirs can be improved significantly by changing the well configuration.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".