Spectral Decomposition in a Heavy-oil and Bitumen Sand Reservoir
Bibliographic record
Abstract
Abstract In this chapter, seismic-attribute spectral decomposition (SD) is used for understanding heavy-oil and bitumen sand reservoir behavior and comprehending their heterogeneities for future reservoir simulation. Spectral decomposition is performed on the migrated stack and on amplitude-versus-offset (AVO) attributes (P- and S-wave impedance reflectivity). Examples provided in this chapter are from reservoirs with cold and thermal production. The observed differences between SD performed on P- and S-wave impedance reflectivity are explained with the solid state of the oil sands at their preproduction reservoir condition. The interpretation of the seismic attributes is based on the poroelastic and viscoelastic behaviors of the heavy oil and/or bitumen. The reservoir characteristics identified on the spectrally decomposed AVO attributes can be summarized as follows: (1) higher energy at the top and base of the reservoir is associated with shale; (2) medium to high energy is an indication of water sand; (3) low energy in the middle of the reservoir is commonly associated with thick bitumen zones that have high absorption; and (4) the bitumen-water interface is identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".