Bibliographic record
Abstract
Abstract The Senlac SAGD (Steam-Assisted Gravity Drainage) project is Saskatchewan, Canada, does not have the same name recognition as its much bigger brothers in the Alberta Oil Sands but it certainly deserves to be known better. Senlac was the first industrial SAGD project in Canada back in 1997 and since then it has been the site for other technological innovations such as the use of solvent in addition with steam to increase recovery and reduce the Steam Oil Ratio, as well as the testing of wedge wells – wells drilled between SAGD well pairs to benefit from the heat remaining in the reservoir. The reservoir in Senlac is the Dina-Cummings of Lower Cretaceous age and is much smaller than the McMurray formation which is the site of most of the large-scale oil sands project but the oil is only 5,000 cp thus it is mobile at reservoir temperature. This is a significant difference which allows well pairs to achieve excellent production and recovery even though reservoir thickness is only 8-16 m, well below the standard cut-off for SAGD. The presence of bottom water under parts of the field is an added challenge to the operations. The paper will present the field characteristics and production performances as well as the main technological developments such as the Solvent Added Process and the use of wedge wells. The paper will present a complete case study of a SAGD project in a heavy oil reservoir where oil is mobile. Most SAGD project so far have been conducted in bitumen but the paper will show the potential for this technology in thinner and smaller reservoirs.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".