Evolution of seismic velocities in heavy oil sand reservoirs during thermal recovery process Évolution des vitesses sismiques dans les réservoirs de sables bitumineux au cours des procédés de récupération thermique
Bibliographic record
Abstract
In thermally enhanced recovery processes like cycli c steam stimulation (CSS) or steam assisted gravity drainage (SAGD), continuous steam injection entails changes in pore fluid, pore pressure and temperature in the rock reservoir , that are most often unconsolidated or weakly consolidated sandstones. This in turn increa ses or decreases the effective stresses and changes the elastic properties of the rocks. Therma lly enhanced recovery processes give rise to complex couplings. Numerical simulations have been carried out on a ca se study so as to provide an estimation of the evolution of pressure, temperature, pore fluid saturation, stress and strain in any zone located around the injector and producer wells. The approach of Ciz and Shapiro (2007) - an extensi on of the poroelastic theory of BiotGassmann applied to rock filled elastic material - has been used to model the velocity dispersion in the oil sand mass under different con ditions of temperature and stress. A good agreement has been found between these predictions and some laboratory velocity measurements carried out on samples of Canadian oil sand. Results appear to be useful to better interpret 4D seismic data in order to locate the steam chamber.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".