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Record W2845257977 · doi:10.1029/2018jb015600

The Elastic Properties of Clay in Shales

2018· article· en· W2845257977 on OpenAlexaff
Colin M. Sayers, Lennert D. den Boer

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsGeomechanicsAnisotropyClay mineralsGeologyShear modulusGeotechnical engineeringBulk modulusPorosityElastic modulusStiffnessShear (geology)MineralogyPermeability (electromagnetism)Materials sciencePetrologyComposite materialChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract The mechanical properties of clay minerals are important in many diverse scientific disciplines, including soil mechanics, civil engineering, materials science, and petroleum exploration. Rock physics provides a link between the elastic properties of rocks and their constitutive properties such as mineralogic composition, porosity, and pore‐fluid content. To accurately characterize shales, rock physics models must account for the anisotropic properties of clay minerals. Due to more compliant regions between clay particles, the elastic stiffness of clay in shales is significantly less than that of its constituent clay minerals. In this paper, the clay in shales is modeled as anisotropic clay platelets surrounded by a softer interparticle region consisting of clay‐bound water and interparticle contacts. Inverting for the elastic properties of this interparticle region indicates that its effective bulk modulus is like that of water. However, it has a nonzero effective shear modulus that is smaller by an order of magnitude, consistent with the expected shear modulus of clay‐bound water. Owing to its simplicity and robustness, it is anticipated that this model of shales, based on the properties of clay minerals and the interparticle medium, will find use in many rock physics applications, including seismic imaging, seismic inversion, and geomechanics.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.057
GPT teacher head0.319
Teacher spread0.263 · 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 designOther design
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

Citations54
Published2018
Admission routes1
Has abstractyes

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