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Record W2395504457 · doi:10.1130/g37826.1

Stick-split mechanism for anthropogenic fluid-induced tensile rock failure

2016· article· en· W2395504457 on OpenAlexaff
Mirko van der Baan, David W. Eaton, Giona Preisig

Bibliographic record

VenueGeology · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsGeologyMechanism (biology)Failure mechanismUltimate tensile strengthGeotechnical engineeringGeochemistryMining engineeringSeismologyPetrologyComposite materialMaterials science

Abstract

fetched live from OpenAlex

Abstract Fluids play a critical role in natural and human-induced rock failure. It is unclear, however, if propagation of a tensile fracture is inherently an episodic or continuous process. For example, typical average propagation speeds of hydraulic fracture tips on the order of 1–10 m/min suggest continuous crack growth, possibly at subcritical stress intensities. In contrast, using field observations and numerical and mathematical analyses, we show that fracture growth due to anthropogenic hydraulic fracturing is most likely to occur in an episodic fashion, characterized by stick-split behavior that is analogous to stick-slip motion of earthquakes. The stick-split mechanism is regulated by cyclic variations in fluid pressure near the crack tip, in which each successive failure produces a local pressure drop that temporarily halts or slows fracture propagation. A pressure drop results in partial fracture closure, producing noncontinuous fracture propagation through a process that is reminiscent of hand clapping. Rupture speeds for individual failure events are on the order of the shear-wave velocity of the medium; thus, continuous crack growth is not a likely mechanism for anthropogenic hydraulic fracturing treatments despite slow average tip propagation speeds.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.221
Teacher spread0.212 · 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 designBench or experimental
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

Citations22
Published2016
Admission routes1
Has abstractyes

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