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Record W2101626532 · doi:10.1139/t06-063

Forms and sand transport in shallow hydraulic fractures in residual soil

2006· article· en· W2101626532 on OpenAlexvenueno aff
Lawrence C. Murdoch, James R. Richardson, Qingfeng Tan, Shaun C. Malin, Cedric Fairbanks

Bibliographic record

VenueCanadian Geotechnical Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsGeologyGeotechnical engineeringCasingFracture (geology)Hydraulic fracturingResidualGeophysics

Abstract

fetched live from OpenAlex

Four sand-filled hydraulic fractures were created at a depth of 1.5 m, and the vicinities of the fractures were excavated and mapped in detail. All the fractures were shaped like slightly asymmetric saucers between 4.5 and 7.0 m across that were roughly flat-lying in their centers and curved upward to dip between 10° and 15° along their peripheries. Three different colors of sand were injected in sequence to trace the relative ages of the sand in the fracture. The first sand to be injected remained in the vicinity of the injection casing, whereas the last sand moved rapidly to the leading edge. Sand transport occurred through localized, channel-like pathways that extended from the injection casing and then branched into multiple paths as they approached the leading edge. At least four branching pathways of different ages were identified in one fracture, suggesting that this represents a fundamental mechanism of sand transport in these shallow fractures. A theoretical model was developed by adapting Franc2d, a code well-known in structural mechanics, to predict the propagation paths of curved hydraulic fractures at shallow depths. The model predicts fracture forms that are remarkably similar to those in field exposures when properties typical of field conditions are used.Key words: hydraulic fracture, field test, mechanics, modeling.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.004
GPT teacher head0.195
Teacher spread0.190 · 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

Citations24
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207