AVO inversion for sand-distribution prediction in oil sands reservoir
Bibliographic record
Abstract
In the Athabasca oil sands, which is a large deposit of heavy oil, located in northeast Alberta, Canada, it is common that impermeable shale intricately exists within the reservoirs and can potentially act as permeability baffles. For reservoir management, it is important to precisely delineate the intrareservoir shale. The main goals of this study are to establish a rock physics model of poorly consolidated, heavy oil-saturated sands and to estimate density by applying three-term AVO inversion to P-P reflected and P-S converted wave data.We first explore viscoelastic features of heavy oil by using ultrasonic velocity measurement data collected over a wide temperature range. By using viscoelastic model, temperature and frequency dependences of the bulk and shear moduli are predicted. Furthermore, we establish a rock physics model of poorly-consolidated, heavy-oil saturated sands. For the case of inclusions in a matrix, Generalized Singular Approximation method is used to obtain the effective properties. The model incorporates the viscoelastic features of heavy oil to estimate velocity dispersion associated with the viscosities.Density has a large contrast between reservoir and shale and is a desired property for reservoir delineation in the Athabasca oil sands. A P-P and P-S joint AVO inversion method is developed by extending an Bayesian inversion technique to multicomponent data. We apply the developed method to the Hangingstone oilfield to estimate density volume. The estimated density is practically consistent with the well log, implying that the method can provide a quantitative description of oil sands 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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".