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Record W2599486791 · doi:10.1002/2016jb013835

Nucleation of dynamic slip on a hydraulically fractured fault

2017· article· en· W2599486791 on OpenAlexafffund
Mohammad Azad, Dmitry Garagash, Mysore G. Satish

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2017
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlip (aerodynamics)SlippingNucleationInstabilityGeologyMechanicsGeotechnical engineeringFault (geology)Shear (geology)SeismologyStructural engineeringPetrologyEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract This work is concerned with the relationship between hydraulic fracturing injection into a fault and the possibility of a seismic slip. The results of this study show that the nucleation of dynamic slip on a fault with slip‐weakening friction is only weakly dependent on the magnitude of the stress perturbation ahead of the propagating hydraulic fracture (HF), or the HF propagation regime, and is mainly controlled by the hydraulic fracture length (i.e., the size of the fully unloaded fault segment at a given time). The growth of the fault slipping patch remains stable when the background shear stress τ 0 is smaller than the residual fault strength τ r under ambient conditions. Otherwise ( τ 0 > τ r ), nucleation of dynamic slip takes place when the hydraulic fracture grows to the critical size ℓ c , which is vanishingly small for critically stressed faults (i.e., when the background stress approaches the fault peak strength, ) and is diverging as when the stability boundary is approached ( ). Our solution for the critical HF size allows to infer the corresponding fluid injection volume that may lead to slip instability on a fault with given frictional properties and background stress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.337
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2017
Admission routes2
Has abstractyes

Explore more

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