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Record W2155055857 · doi:10.1139/t00-096

Laboratory studies on fracturing of low-permeability soils

2001· article· en· W2155055857 on OpenAlexfundvenueno aff
Marolo Alfaro, Ron CK Wong

Bibliographic record

VenueCanadian Geotechnical Journal · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydraulic fracturingGeotechnical engineeringPermeability (electromagnetism)GeologyPore water pressureFracture (geology)Soil waterOverburden pressureSoil science

Abstract

fetched live from OpenAlex

Hydraulic and pneumatic fracturing have been used to improve the effectiveness of most in situ remediation methods for contaminated sites underlain with unfavorable low-permeability soils. This paper presents results of a laboratory experimental investigation to characterize the mechanisms related to the initiation pressure and growth of fractures stimulated from vertical and horizontal wells. The mechanisms of fracture in low-permeability soils appeared to be of a tensile failure mechanism enhanced by the generation of pore pressure as the soil around the well was being sheared due to the radial-tangential stress difference imposed by the injected pressure. The impacts of initial fracture slots on fracture orientation and initiation pressure were also investigated. The test results have demonstrated that the presence of initial fracture slots could reduce the injection pressure required to initiate fracture in the well. The initial slot, however, did not necessarily control the orientation of the propagating fracture. The effect of imposed stresses in the soil was evaluated also and was found to influence the orientation and propagation of fracture. Smaller stress contrast favored multiple deviated fractures, whereas larger stress contrast favored distinct fractures.Key words: soil fracturing, laboratory test, low-permeability soil, fracture propagation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.554

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 integrity0.0000.001
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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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

Citations67
Published2001
Admission routes2
Has abstractyes

Explore more

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