Quantitative estimate of fracture density variations in the Nordegg with azimuthal AVO and curvature: A case study
Bibliographic record
Abstract
We investigated the accuracy of surface seismic attributes in predicting fracture density variations within the Nordegg Formation in west central Alberta. We know from core, drill samples, well-log, and drilling data that the Nordegg zone is fractured to some degree. These fractures are of interest because the reservoir has very low permeability, and therefore natural fractures may materially affect well performance. 3D surface seismic techniques such as amplitude variation with azimuth or azimuthal AVO (AVAz), variation of velocity with azimuth (VVAz), curvature, and coherence techniques are all tools that have been used to predict fractures in a qualitative fashion. In this study, we wanted to understand how well these attributes predicted the reservoir quality in a quantitative fashion. Previous quantitative studies have used image log orientation data or estimated ultimate recoveries (EUR) in vertical wells as validation data. The conclusiveness of these studies has been subject to several problems: firstly, the limited sample statistics provided by vertical wells applied to the validation of lateral variations, and secondly by the potential nonuniqueness of the EUR to fracture density relationship.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".