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Record W2057308414 · doi:10.2118/166259-ms

Predictions of the Growth of Multiple Interacting Hydraulic Fractures in Three Dimensions

2013· article· en· W2057308414 on OpenAlexaff
Stephen T. Castonguay, Mark E. Mear, Rick Dean, Joseph H. Schmidt

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2013
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsFracture (geology)MechanicsFlow (mathematics)PlanarBoundary (topology)Stress (linguistics)Hydraulic fracturingFocus (optics)ViscosityGalerkin methodStress intensity factorBoundary value problemFluid dynamicsBoundary element methodMode (computer interface)Fracture mechanicsComputer scienceGeologyStructural engineeringFinite element methodMathematicsGeotechnical engineeringEngineeringPhysicsMathematical analysisOptics

Abstract

fetched live from OpenAlex

Abstract This paper presents fracture simulations of multiple, interacting, non-planar fractures in three dimensions. The paper provides a short description of the mathematical formulation of the model but the primary focus is on fracture propagation examples and illustrations of how multiple hydraulic fractures interact in three dimensions. The examples which are presented are intended to provide insight into how the number of growing fractures affects fracture shapes, how changes in fluid viscosity can cause fractures to grow together or grow apart, and how limited-entry at the perforations affects the propagation of interacting fractures. The fracture simulator discussed in this paper models the simultaneous growth of non-planar hydraulic fractures in a three-dimensional linear elastic media, and it can also incorporate stress shadows from hydraulic fractures created during earlier fracture stages. The program uses a symmetric Galerkin boundary element method to model fracture shapes and fracture growth, while flow in the fractures is modeled as power-law fluid flow in arbitrary curved channels. The program uses an effective mode-I stress intensity factor to determine which portions of each fracture will propagate and employs mixed-mode stress intensity factors, KI and KII, to determine propagation directions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.252

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 integrity0.0000.000
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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

Explore more

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