Examination of wide‐angle, multi‐component, AVO attributes for prediction of shale in heavy oil sands: A case study from the Long Lake Project, Alberta, Canada
Bibliographic record
Abstract
A fundamental geologic problem in the Steam-Assisted Gravity Drainage (SAGD) heavy oil developments in the McMurray formation of Northern Alberta is to determine the location of shales in the reservoirs that may interfere with the SAGD process. Petrophysical analysis shows that a key indicator of the presence of shale in the Long Lake Project is bulk density. In theory, density can be derived from seismic data using Amplitude Versus Offset (AVO) analysis of conventional and/or multi-component seismic data, but this is not widely accepted in practice. However, with billions of dollars slated for SAGD developments in the upcoming years, this technology warrants further investigation. In addition, many attributes can be investigated using modern tools like neural networks; so, the density extracted from seismic using AVO can be compared and combined with more conventional attributes in solving this problem. Results to date show a strong correlation of the density derived using AVO to that measured by the bulk density logs in the wells. The P-impedance also shows an expected correlation to both the presence of shales and to the density attribute. Further work is being done to combine the various attributes derived to date in a neural network for the direct prediction of the Gamma Ray (GR) log.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".