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Record W2946285278 · doi:10.1190/geo2018-0097.1

A program to calculate the state of stress in the vicinity of an inclined borehole through an anisotropic rock formation

2019· article· en· W2946285278 on OpenAlexaff
Wei Li, Douglas R. Schmitt, Maria Tibbo, Changchun Zou

Bibliographic record

VenueGeophysics · 2019
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsBoreholeAnisotropyGeologyStress fieldAzimuthStress (linguistics)Geotechnical engineeringGeometryOpticsPhysics

Abstract

fetched live from OpenAlex

ABSTRACT A borehole existing in any geologic formation concentrates the far-field tectonic and overburden stresses amplifying the magnitudes of certain stress components near the borehole. It is important to understand the magnitudes and patterns of this stress concentration because these lead to damage and can even collapse the borehole if sufficiently strong. The solution of the stress distributed near a borehole can be complicated considering the elastic anisotropy of rocks. We have developed programs (ASCIB3D) in MATLAB and Python to model the stress distribution around an inclined borehole in an arbitrarily oriented anisotropic medium. The program is built on the Lekhnitskij-Amadei solution. The input orientation of the far-field stresses and the elastic stiffness matrix of the medium into the program are geology angles instead of the rotation angles shown in previous studies, making the code more convenient for users. The sign convention for the inverse function, which is ignored in previous studies, is discussed in detail. The results indicate that the program ASCIB3D is a useful tool for modeling the stress distributed around an inclined borehole in the anisotropic formation and analyzing the effect of anisotropy and borehole inclination on stress distribution. The inclination and azimuth of the borehole and the anisotropy of the rocks affect the orientation and strength of the stress concentration.

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 score0.017
Threshold uncertainty score0.207

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 head0.013
GPT teacher head0.253
Teacher spread0.239 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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