Large‐Scale Fracture Systems Are Permeable Pathways for Fault Activation During Hydraulic Fracturing
Bibliographic record
Abstract
Abstract Induced seismicity due to fluid injection, including hydraulic fracturing, is an increasingly common phenomenon worldwide; yet, the mechanisms by which hydraulic fracturing causes fault activation remain unclear. Here we show that preexisting fracture networks are instrumental in transferring fluid pressures to larger faults on which dynamic rupture occurs. Studies of hydraulic fracturing‐induced seismicity in North America have often used observations from regional seismograph networks at distances of 10s of km, and as such lack the resolution to answer some of the key questions about triggering mechanisms. To carry out a more detailed analysis of the mechanisms of fault activation, we use data from a dense sensor array located at a hydraulic‐fracturing site in Alberta, Canada. The spatiotemporal distribution of event hypocenters, coupled with measurements of seismic anisotropy, reveal the presence of preexisting fracture corridors that allowed communication of fluid‐pressure perturbations to larger faults, over distances of 1 km or more. The presence of preexisting permeable fracture networks can significantly increase the volume of rock affected by the pore‐pressure increase, thereby increasing the probability of induced seismicity. This study demonstrates the importance of understanding the connectivity of preexisting natural fractures for assessing potential seismic hazards associated with hydraulic fracturing of shale formations and offers a detailed case exposition of induced seismicity due to hydraulic fracturing.
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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 | 0.000 |
| 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 | 0.001 |
| 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".