Rupture Behavior of Hydraulic Fracture Induced-triggered Seismicity
Bibliographic record
Abstract
Summary Through the use of a unique hybrid seismic recording network, we investigate the rupture characteristics of induced-triggered events with M>0 associated with a hydraulic fracture stimulation in an unconventional reservoir. This unique approach incorporates high-frequency recordings utilizing downhole 3C 15 Hz omni-directional geophones situated near the reservoir and thereby the rupture initiation, intermediate-depth downhole 3C Force Balance Accelerometers (0.1Hz) and geophones (4.5Hz), and near-surface, low-frequency 3C recordings obtained using Force Balance Accelerometers (0.1Hz) and geophones (1 Hz, 2Hz and 4.5Hz) that allowed for investigation of overall rupture characteristics within the frequency bandwidth of effectively 0.1Hz to over 500Hz. For these larger events, the recordings allowed for an in-depth investigation of the dynamics of the sub-fracture failures during the rupture process and growth of the overall fracture from initiation to arrest. Our initial results suggests that overall shearing is the dominant mode of failure, whereas the rupture characteristics of the sub-fracture failures are more complex than a simple shearing process and include strong tensile components of failure. Our measurements of rupture complexity, seismic efficiency, rupture velocity and estimates of stress release further support the idea that the sub-fractures are characterized by failures of multiple asperities that exhibit self-similar behavior within themselves.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".