Microseismic geomechanics of hydraulic-fracture networks: Insights into mechanisms of microseismic sources
Bibliographic record
Abstract
Abstract Microseismic interpretation of hydraulic fracturing requires an understanding of the mechanism of the microseismic sources. Quantitative geomechanical models can predict microseismicity for quantitative comparison with field data and can be used to reconcile 3D seismic earth models, fracture engineering, and fracture monitoring. Because microseismicity represents only one component of the geomechanical response to hydraulic fracturing, a microseismic geomechanics framework can provide insights into the connection with the fracture network. During hydraulic fracturing, microseismicity can be induced by both fluid pressure and stress mechanisms, resulting in wet events directly associated with the fracture network and remote dry events. Accurate interpretation of the hydraulic-fracture characteristics requires distinguishing identification of dry microseismicity not in hydraulic connection with the stimulated fracture network. Predictive microseismic geomechanical models also can be used to infer the primary, conductive hydraulic-fracture networks and to run scenario testing to improve engineering design.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".