SAGD Well-Pair Completion Optimization Using Scab Liner and Steam Splitters
Bibliographic record
Abstract
Summary The steam-assisted-gravity-drainage (SAGD) process has been widely used commercially in western Canada for bitumen production. Improving the oil-production rate and reducing the steam/oil ratio (SOR) have been the focus of the industry. In heterogeneous reservoirs, oil production could be impeded by steam breakthrough at one location of the producer and higher liquid level above the other sections of the producer. Various completion methods have been proposed to improve production efficiency. Outflow-control devices (OCDs), such as steam splitters, are used to match steam delivery to reservoir requirements, and inflow-control devices (ICDs) may be used in producers to maximize oil production. Scab liners are the most widely used type of ICD. In general, oil drainage into producers may need to be slowed at some locations and sped up at other locations of the well. In this study, we address how to design SAGD injector and producer completions using steam splitters and scab liners. Results from reservoir simulation with coupled wellbore hydraulics will be presented to show how a well pair 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, as well as size and length of scab liners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".