Bibliographic record
Abstract
Microseismic monitoring is attracting great interest due to the application of passive seismic to shale play completion activities and the successful expansion of the method from downhole to surface and near-surface acquisition geometries. Fundamental to this application is the science behind the interpretations: The inherent capabilities and limitations of downhole, surface and near-surface recording systems and the processing and imaging applications enabled by these recordings must be appreciated to understand the results.These considerations inform issues such as: microseismic event detectability and position uncertainty; the characterization of geological features; sensitivity to hydraulic fracturing methods; rock failure modes; and well to pad to field-wide implications of large scale ‘horizontally drill and hydraulically fracture’ development programs.Ultimately interpretation workflows determine microseismic event pointsets, modelling of discrete fracture networks and calculation of stimulated rock volumes.As microseismic monitoring matures understanding the relationship of recording geometry, imaging capability and interpretation workflows will fuel expanded utilization.Here the basic issues surrounding passive seismic acquisition methods and microseismic interpretation will be reviewed and discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".