A numerical characterization workflow for assessing the strength and failure modes of heterogeneous oil sands
Bibliographic record
Abstract
Understanding the strength and failure modes of overburdens and reservoirs is a critical component in safety assessments for oil sands surface mining and in situ thermal recovery operations. Currently, assumptions of homogeneity are often made for the geomechanical properties of oil sands in conventional slope stability analyses and reservoir simulation. The purpose of this work is to propose a numerical characterization workflow that helps predict the failure mode and shear strength of heterogeneous oil sands interbedded with shale beddings during thermal recovery. Heterogeneous models are generated through sequential indicator simulation with a calibrated constitutive model and geomechanical parameters for each lithology. Numerical simulations with boundary conditions reflecting in situ stress changes are conducted to study the impact of shale beddings on stress–strain response, failure modes, and shear strength of the sheared zone. The results show that shear failures of the weaker shale beddings play a significant role in the elastoplastic behavior and reduced shear strength of heterogeneous oil sands. The shear band and weak plane failures are found to be closely related to the volume fraction, variogram range ratio, and inclinations of shale beddings. The proposed numerical workflow allows for quantitative investigations of geomechanical response for rock mass with complex lithological heterogeneities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".