Experimental Modeling of the SAGD Process - Enhancing SAGD Performance with Periodic Stimulation of the Horizontal Producer
Bibliographic record
Abstract
Summary Experiments on initial stages of the steam-assisted gravity drainage (SAGD) process were carried out, using two-dimensional (2D) scaled reservoir models, to investigate production process and performance. Expansion of the initial steam chamber, its shape and area, and its temperature distributions were visualized with video and thermal-video pictures. The relationship between isotherms and steam-chamber interface was investigated to study the drainage mechanism. Temperature at the expanding steam-chamber interface was observed to remain nearly constant at close to 80°C. The effect of vertical spacing between the two horizontal wells on oil recovery was also investigated. For the Conventional SAGD case, oil production rate increased with increasing vertical spacing between the wells; however, the lead time for the gravity drainage to initiate oil production became longer. The results suggest that vertical spacing between the wells can be used as a governing factor to evaluate production rate and lead time in the initial stage of the SAGD process. Based on these experimental results, the SAGD process was modified; the lower production well was intermittently stimulated by steam injection, in conjunction with continuous steam injection in the upper horizontal injector. With the modified process (named SAGD-ISSLW), the time to generate near-breakthrough conditions between two wells was shortened, and oil production was enhanced at the rising chamber stage compared with that of the Conventional SAGD process.
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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 | 0.000 | 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".