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Record W2105915932 · doi:10.1139/cgj-2015-0193

Observations of hydraulic fracturing in soils through field testing and numerical simulations

2015· article· en· W2105915932 on OpenAlexvenueno aff
Muhsiung Chang, Ren-Chung Huang

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
FundersNational Science Council
KeywordsGeotechnical engineeringHydraulic fracturingGeologySoil waterShearing (physics)DrillingPermeability (electromagnetism)Computer simulationPetroleum engineeringEngineeringSoil science

Abstract

fetched live from OpenAlex

Hydraulic fracturing is a potential cause of leakage of earth dams or loss of fluid in drilling and field permeability testing. The effect of hydraulic fracturing on soil grouting is also a major concern. Although hydraulic fracturing has been adopted for decades by the petroleum industry for oil recovery in rock formations, studies on fracturing in soils are relatively few and inconclusive. The aim of this study is to provide further insight into the mechanism of hydrofracturing in soils through a field grouting trial and numerical simulation. We observe hydraulic fracturing in soils during this field trial as predicted by generally accepted groutability requirements. The hydraulic fractures are found vertically developed up to the ground surface. Numerical simulations show the hydraulic fracturing is easier to be initiated in anisotropic stress conditions, where the minor principal stress is the key factor. Numerical simulations also demonstrate significant compressions and shears during injection, suggesting the mechanism of fracturing in soils would be a shearing type. Based on this study, we propose a punching and splitting mode for the hydrofracturing in soils. The equation associated with estimating fracturing pressure is verified, and the results are found to be in good agreement with the cases examined.

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)0.001
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 score0.054
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.052
GPT teacher head0.248
Teacher spread0.196 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGrouting, Rheology, and Soil MechanicsFrench-language works237,207