Analysis of Scattered Signal to Estimate Reservoir Fracture Parameters
Bibliographic record
Abstract
We detect fracture corridors and determine their orientation and average spacing based on an analysis of seismic coda in the frequency-wave number (f-k) domain. Fracture corridors have dimensions similar to seismic wavelengths which causes scattering. The distribution of energy in shot records in the f-k domain depends upon its orientation to the fracture strike. In the direction normal to fractures, energy reflected and transmitted at the reservoir level mostly propagates as scattered waves with slower apparent velocities than waves propagating along the fracture channels. The associated f-k spectral differences allow the identification of the preferred fracture orientation and spacing. We apply our technique to a fractured reservoir in the Lynx field, in the Canadian foothills. The estimated preferential fracture orientation is about N40°E, which agrees with regional stress measurements. The average fracture spacing is 75 m on the West side of the survey, while the fractures are more sparse on the East side. We also applied the Scattering Index methodology (Willis et al., 2006) to the same data, post-stack and pre-stack. This technique has higher resolution to map fracture distribution, intensity and orientation, and therefore complements the spectral method in providing an integrated description of reservoir fractures.
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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 | 0.001 |
| 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) | 0.002 | 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".