Optimization of Well-Pair Spacing and Well Configuration for the SAGD Process in the Oilsands Reservoirs
Bibliographic record
Abstract
The performance of steam-assisted gravity drainage (SAGD) is influenced not only by reservoir properties, but also by well-pair spacing.Field operators empirically increase the well-pair spacing for thicker reservoirs.However, a quantitative method is needed to determine the well-pair spacing based on reservoir properties.This study accordingly developed an optimization method for well-pair spacing using the properties of representative oil sands reservoir in Athabasca, Canada.In addition, the economic feasibilities of narrow-spacing and infill well-drilling methods were compared.A sensitivity analysis of vertical permeability and reservoir thickness was conducted to identify reservoir conditions under which SAGD operations are economically viable.The results revealed that the optimal well-pair spacing was the reservoir thickness multiplied by five, subtracting 50 m when the vertical permeability was 500 mD, and five times the reservoir thickness for vertical permeabilities of 1,500 mD and 2,500 mD.The infill well-drilling method with the optimized well-pair spacing showed the highest performance among the well configurations considered.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".