Bibliographic record
Abstract
In the steam-assisted gravity drainage (SAGD) process, large amounts of steam are injected into the reservoir to mobilize bitumen for its production to surface. The amount of thermal energy injected into the reservoir creates a man-made geothermal system that could be used to yield heat after the SAGD operation is complete. In the research documented here, for the first time, the amount of energy stored in the reservoir is evaluated and potential processes for recovery of the thermal energy are explored. The results reveal that after 10 years of SAGD, about 35% of the total injected energy in the steam remains in the reservoir rock from which the bitumen was extracted. After a 1 year blowdown stage at the end of the SAGD operation, ∼32% of the total injected energy remains in the reservoir rock. Three cases are used to explore the potential for recovering the thermal energy in the reservoir rock. The results show that the best case is one where a new horizontal well is added at the top of the reservoir above the SAGD well pair. In this case, >100 °C water was recovered at high rate for over 1,400 days realizing about 34% recovery of the thermal energy contained in the reservoir rock after the SAGD blowdown stage. The results demonstrate that there is potential for recovering the thermal energy. This recovered energy offers means to make the SAGD process more thermally efficient over its entire energy production life.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".