Effects of Lean Zones on Steam-Assisted Gravity Drainage Performance
Bibliographic record
Abstract
A thorough understanding of the effects of lean zones and the improvement of steam-assisted gravity drainage (SAGD) operations with such heterogeneities is critically important for reducing the disadvantages of lean zones. The numerical model shows: (1) SAGD is most influenced by the single-layer lean zone with the above-injector (AI) location; with the decrease of interval distance and increase of thickness and water saturation in lean zones, the detrimental effect of single-layer lean zones on SAGD performance increases; (2) with the increase of period and decrease of connate and initial water saturations in lean zones, the detrimental effect of multiple-layer lean zones on SAGD performance increases; (3) reducing the injection pressure properly improves SAGD performance in leaky oil sands. The field-scale study indicates: (1) well pair 1 is most affected by lean zones in the studied pad due to the widest distribution of lean zones above its injector, and a hybrid cyclic steam stimulation (CSS)/SAGD method is proposed to overcome the practical problem of a low injection pressure in this area; (2) simulation results prove that the hybrid CSS/SAGD method is better than the conventional SAGD method in leaky oil sands.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".