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Record W2109867855 · doi:10.1061/9780784413272.275

Multiscale Discontinuities Due to Differential Stress around a Pressurized Borehole

2014· article· en· W2109867855 on OpenAlexaff
Hao Xu, Chloé Arson, Seth Busetti

Bibliographic record

VenueGeo-Congress 2014 Technical Papers · 2014
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsClassification of discontinuitiesHydraulic fracturingDifferential stressMesoscopic physicsBoreholeGeologyFracture mechanicsFracture (geology)Damage mechanicsMechanicsRock mass classificationStress (linguistics)Geotechnical engineeringFinite element methodMaterials scienceDeformation (meteorology)Structural engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Most fracture propagation models do not properly represent smaller-scale discontinuities in the process zone. This paper reviews the different modeling strategies available to date to model crack propagation at microscopic, mesoscopic, and macroscopic scales. The differential stress induced damage (DSID) model recently proposed by the authors is then used to simulate fracture propagation around a pressurized borehole with the finite element method. In a pristine rock mass, the damage zone presents several symmetries in three dimensions, which are in agreement with the definition of the damage-driving force controlling the initiation and propagation of damage. If hydraulic fracturing is enhanced by the presence of initial cracks, the propagation of the damage zone depends on the geometry of the initial defects. It is found that simulating rock initial texture by a smeared damaged zone provides good analogs to the viscosity-dominated and toughness-dominated fracture propagation regimes expected during hydraulic fracturing. Future work will be dedicated to the fully coupled formulation of a hydro-mechanical model of damage around hydraulic fractures.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.208
Teacher spread0.201 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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