Seismic Azimuthal Anisotropy: an Important Tool for Detection of Coal Bed Methane, an Unconventional Source of Energy- a review
Bibliographic record
Abstract
Methods of measuring seismic azimuthal anisotropy are being used increasingly to detect fractures in reservoirs. Coal reservoirs are usually more abundant in fractures than any other ore bodies. However, not all the fracture nets have the same feature, neither can they lead to the same permeability and the same anisotropy. In coal exploitation, research on fractures is of vital importance in guiding the layout of working faces, mining and driving, the exploration and development of coal bed methane, the maintenance of roadways and the prevention of water and gas bursts. Therefore, to be able to forecast the direction and density of fractures in coal seams is of great importance for safe production and high efficiency of coal mining. This suggests that estimates of seismic anisotropy can be used for detecting cleats in the coal bed methane (CBM) reservoirs. In this paper we are going to present a review work of the application of azimuthal anisotropy estimated from a small 3D, shot over an area in Western Canada known to contain significant coal beds, and shows that significant seismic anisotropy is associated with them. This suggests that estimate of azimuthal anisotropy can be used for optimizing CBM reservoir management in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".