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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".