Bibliographic record
Abstract
Summary Recent low-frequency experiments with bitumen sands suggest that with increasing temperatures, the reduction of viscosity in them is nearly equivalent to a similar reduction of the observation seismic frequency. This equivalence implies that inherently, bitumen sands and likely other heavy-oil containing rocks can be represented by mechanical systems with linear viscosity. Meanwhile, these mechanical systems can be structurally complex, likely more complex than the usual viscoelastic models of ‘standard solids’. For a meaningful petrophysical interpretation, it is important to not only summarize the observed properties such as the empirical moduli and Q, but also to seek first-principle, physical properties underlying these observations. Because of their inherent complexity, descriptions of heavy-oil compounds should contain multiple internal variables, similarly to pore fluids in Biot’s mechanics of fluid-saturated rock. A realistic constitutive model should also include coupling between internal variables, also similar to poroelasticity. A general class of such models can be formulated by using Lagrangian continuum mechanics and matrix constitutive constants (densities, rigidities, viscosities and damping). Here, a model of such kind is applied to modeling laboratory measurements of Ells River bitumen sands. A single internal variable with combined viscoelastic and poroelastic effects successfully predicts the observed frequency-dependent P-wave modulus and attenuation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".