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Record W2944545809 · doi:10.1002/nag.2940

Numerical modeling of fracturing in permeable rocks via a micromechanical continuum model

2019· article· en· W2944545809 on OpenAlexafffund
Mahdad Eghbalian, Richard Wan, Mehdi Pouragha, Larry S. Fung

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaSaudi Aramco
KeywordsPoromechanicsMechanicsConstitutive equationMaterials scienceAnisotropyRepresentative elementary volumeGeotechnical engineeringCoalescence (physics)Material propertiesMicroscale chemistryPorous mediumGeologyFinite element methodPorosityPhysicsStructural engineeringEngineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

Summary The paper presents a micromechanical approach to describe the failure of low‐permeability brittle rocks as a multiscale fracturing process based on a poroelastic microcrack‐damage model. Failure is formulated deep down at the fine pore scale as a material degradation phenomenon driven by microcrack growth that also impacts upon hydromechanical properties. A set of damage tensors describes the effect of dual‐scale porosities (nanopores and microcracks) on both the hydraulic and poroelastic rock properties. Essentially, the multiscale model reconstructs the coupling effect of hydromechanical forces at the continuum level from the ground up through the upscaling of multiphase interactions at the fundamental structural level of the material. As a result, many macroscopic characteristics emerge naturally such as friction angle, fracture properties, and most importantly, Biot's coefficient taking on a tensorial form that is generally anisotropic. The model is validated within the framework of finite elements to illustrate various baseline constitutive features such as the effect of microcrack growth on the nonlinear stress‐strain response and the induced anisotropy in the context of lab experimental tests and boundary value problems. Heterogeneities of the rock samples were incorporated by choosing material properties to be stochastic following Weibull and lognormal distributions. Numerical results appropriately replicated typical experimental observations where fracture localization and propagation are shown to be a multiscale phenomenon emerging from microcrack growth and coalescence at the microscale, with concomitant enhancement in fluid conductivity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.338
Teacher spread0.312 · 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 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

Citations10
Published2019
Admission routes2
Has abstractyes

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