The Application of Fishbone Wells in Steam-Assisted Gravity Drainage
Bibliographic record
Abstract
Apart from cost, major challenges facing the recovery of bitumen from Canadian oil sands are the amount of energy utilized per volume of bitumen recovered as well as the amount of greenhouse gas (GHG) emitted to the environment. The situation is even worse in reservoirs that are considered to be challenging or difficult-to-produce due to the reservoir geology. Steam-assisted gravity drainage (SAGD) is the primary in-situ recovery technique for bitumen recovery in Northern Alberta, Western Canada. Within the reservoir, steam chamber conformance is a major control on the efficiency, economic performance, and GHG emissions intensity of the process. There is a search for ways to significantly reduce the costs and emissions of SAGD. Multilateral wells possess the potential to contribute towards this goal. To date, different theoretical designs of multilateral wells have been proposed in literature. One of the most common designs studied is the fishbone well configuration. This configuration has large reservoir contact and thus enhances the productivity of the well. While the merits of the application of multilateral wells are well documented in lighter oil systems, an understanding of the best operating conditions for the use in oil sands reservoirs is poorly understood. The research documented in this thesis examines in detail how fishbone multilateral wells can be used to improve the performance of SAGD. In summary, the results demonstrate that fishbone well are able to improve steam chamber conformance and ultimately minimize cumulative steam-oil ratio (cSOR), maximize ultimate bitumen recovery, improve net present value (NPV), and reduce GHG emissions. Therefore, fishbone multilateral wells should be considered for future SAGD operations.
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 | 0.000 | 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 | 0.001 | 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".