SAGD Wellbore Completion Optimization Using Scab Liner and Steam Splitter
Bibliographic record
Abstract
Abstract Steam assisted gravity drainage (SAGD) process has been widely used commercially in Western Canada for bitumen production. Improving oil production rate and reducing steam oil ratio has been the focus of the industry. In heterogeneous reservoirs, oil production could be impeded by local steam break through and high liquid level above the other section of the producer. Various completion methods have been proposed to improve production efficiency. Steam splitter is proposed to match steam delivery to reservoir requirement and scab liner may be used in producer to maximize oil production. In general, oil drainage into producer may need to be slowed down at some locations and speeded up at other locations of the well. Non-uniformity of the reservoir pay and quality also has a direct impact on oil production and consequently, it is significant to divert required amount of steam to the desired spots of the reservoir. In this study, we address how to design steam splitter and scab liner in order to optimize SAGD production. Results from reservoir simulation with coupled wellbore hydraulics will be presented to show how a wellbore could be optimized by attaining favorite pressure profiles inside the injector and producer liners. This investigation will also address sensitivities on steam splitter location, size and number of holes in splitter, and size and length of scab liner.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".