Bibliographic record
Abstract
Abstract Oil sands geomechanics plays an important role in the oil sands recovery processes, such as surface mining, cyclic steam stimulation and SAGD, which are widely applied in the development of oil sands resources in Alberta, Canada. Coupled reservoir geomechanical simulation techniques have been developed and used for the design of in situ recovery processes, particularly for SAGD. Thus, a realistic geomechanical model of oil sands material is a critical component in these reservoir geomechanical simulations. This paper presents the development of an oil sands model based on the analysis of laboratory testing results provided by different researchers, including Oldakowski, Samieh and Wong, and Touhidi-Baghni. On the basis of this analysis, 25 numerical experiments were conducted to match these laboratory tests, including the stress paths, as those applied in the laboratory experiments. Consequently, a comprehensive geomechanical model of oil sands material was established based on these numerical experiments. The proposed strain softening model parameters, such as the modulus of elasticity, peak and post-peak friction angle, and dilation angle, can be applied in the coupled reservoir geomechanical simulations of thermal recovery processes, including the SAGD process.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".