Bibliographic record
Abstract
Abstract All unconventional gas plays rely on the presence of natural fractures to enhance or create permeability in the reservoir. Fractures cause significant, measureable changes in 3D seismic data. These changes appear as variations in seismic amplitudes and velocities with shot-receiver azimuth and are known as seismic azimuthal anisotropy. Examples of the seismic identification of fractures are shown for a Mannville Coal and gas shale from Alberta, and the tight gas sands of the Pinedale Field in Wyoming, which is an analog to Alberta's Deep Basin. In the latter example, seismic fracture estimates are shown to be the best predictors of well EUR (Estimated Ultimate Recoverable) and therefore they are used to predict EUR in 3D. The technology to measure seismic azimuthal anisotropy is now well developed and ready to be used to pinpoint higher producing areas of natural fractures in fractured unconventional gas reservoirs such as tight gas, gas shales and coalbed methane. Seismic azimuthal anisotropy measurements have achieved a technical success rate for identifying fractures upwards of 80% in unconventional gas plays. This can significantly impact drilling success in areas where success rates are low. These measurements also indicate the probable fracture strike and so, by identifying where the gas is coming from, they can be used to avoid drilling into depleted pools.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".