Breakdown of the Gutenberg‐Richter relation for microearthquakes induced by hydraulic fracturing: influence of stratabound fractures
Bibliographic record
Abstract
ABSTRACT Hydraulic fracturing, a powerful completion technique used to enhance oil or gas production from impermeable strata, may trigger unintended earthquake activity. The primary basis for assessment of triggered and natural seismic hazard is the classic Gutenberg‐Richter (G‐R) relation, which expresses scale‐independent behaviour of earthquake magnitudes. Here we use a stochastic approach to simulate and test magnitude‐distance trends expressed by microseismic catalogues derived from three hydraulic fracture monitoring programmes in North America. We show that a widely observed rapid fall‐off in large‐magnitude events, almost universally quantified using the G‐R b value, may in our case be an artefact of the strongly laminated character of the stimulated oil and gas reservoirs. We also show that, for the three reservoirs considered, mechanical bed thickness can be approximated by a lognormal distribution. For a stratabound fracture network, this leads asymptotically to a Gaussian decay for induced magnitudes. We show that the stratabound model provides a more significant correspondence with our observations. If applicable in general, this result has important implications for determining the energy balance of hydraulic fracture systems (i.e. radiated seismic energy versus injected energy) as well as hazard assessments based on the probability of occurrence of anomalous seismic events.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".