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Record W2146588345 · doi:10.1002/2015gl063093

Hydraulic fracture energy budget: Insights from the laboratory

2015· article· en· W2146588345 on OpenAlexafffund
Sebastian D. Goodfellow, M. H. B. Nasseri, S. C. Maxwell, R. P. Young

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsInduced seismicityHydraulic fracturingFracture (geology)GeologyDeformation (meteorology)Range (aeronautics)Displacement (psychology)Acoustic emissionEnergy budgetEnergy (signal processing)Geotechnical engineeringSeismologyMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract In this paper we present results from a series of laboratory hydraulic fracture experiments designed to investigate various components of the energy budget. The experiments involved a cylindrical sample of Westerly granite being deformed under various triaxial stress states and fractured with distilled water, which was injected at a range of constant rates. Acoustic emission sensors were absolutely calibrated, and the radiated seismic energy was estimated. The seismic energy was found to range from 7.02E−8% to 1.24E−4% of the injection energy which is consistent with a range of values for induced seismicity from field‐scale hydraulic fracture operations. The deformation energy (crack opening) of the sample during hydraulic fracture propagation was measured using displacement sensors and ranged from 18% to 94% of the injection energy. Our results support the conclusion that aseismic deformation is a significant term in the hydraulic fracture energy budget.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.264
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations159
Published2015
Admission routes2
Has abstractyes

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