Impact of Flow Control Devices on SAGD Performance from Less Heterogeneous to Strongly Heterogeneous Reservoirs
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage (SAGD) has proven itself to be a commercial success in McMurray oil sands reservoirs. In this process, steam delivered into the reservoir mobilizes bitumen which then flows under gravity to the production well. A countercurrent flow situation results where steam rises and bitumen and condensate drains within the reservoir. Given the vertical and horizontal flow within the reservoir, SAGD performance is strongly affected by reservoir heterogeneity. In the field, poor SAGD performance commonly arises from two challenges: first, steam breakthrough from the injector to the producer and second, non-uniform chambers along the length of the well pairs. Flow control devices (FCDs) offer the potential to improve SAGD performance but it remains unclear how to place and design these devices to maximize steam conformance and minimize steam-to-oil ratio. Here, to understand the behavior of FCDs in SAGD operations, detailed reservoir simulations, including wellbore hydraulic modeling, are conducted in a simple clean sand model and a detailed point bar model dominated by inclined heterolithic strata. The study includes the use of FCDs on the injector only, the producer only, as comparisons to conventional well completions cases. The results indicate that SAGD performance improved by using FCDs with better control of steam breakthrough between the wells.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".