Bibliographic record
Abstract
Steam-based thermal recovery process is the most commonly used recovery process for bitumen production from oil sands reservoirs. The most used thermal methods are Steam Flooding (SF), Cyclic Steam Stimulation (CSS), and Steam Assisted Gravity Drainage (SAGD). The choice of method depends on the geology, initial reservoir conditions, and the viscosity of the oil. In the research documented here, a detailed examination of the Liaohe heavy oil operation is analyzed from field data. The analysis relies on from a construction of detailed geological and reservoir models and a history match of the CSS and steam-injection gravity drainage operation. The model is then used to evaluate steam flooding and automated control of the recovery process. Also, a submodel from the history-matched reservoir model is used to understand, at fine scale, the dynamics of CSS. The results show that conducting steam flooding post CSS provides an effective means to achieve greater recovery factors at reasonable steam-to-oil ratios. Also, automated control by using proportional-integral-derivative control can yield further improvements of the process performance. The results of the detailed ultra-refined CSS models demonstrate that CSS dynamics are complex due to steam-based dilation and steam condensation. The overall results of the research reveal that CSS is an effective thermal recovery method that can be used with post-CSS processes to produce the majority of oil from the reservoir.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".