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Record W2918996554 · doi:10.3390/en12050888

Numerical Investigation of the Impacts of Borehole Breakouts on Breakdown Pressure

2019· article· en· W2918996554 on OpenAlexaff
Hua Zhang, Shunde Yin, Bernt S. Aadnøy

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBoreholeBreakoutGeologyGeotechnical engineeringDrillingStress (linguistics)SeismologyMaterials science

Abstract

fetched live from OpenAlex

Borehole breakouts appear in drilling and production operations when rock subjected to in situ stress experiences shear failure. However, if a borehole breakout occurs, the boundary of the borehole is no longer circular and the stress distribution around it is different. So, the interpretation of the hydraulic fracturing test results based on the Kirsch solution may not be valid. Therefore, it is important to investigate the factors that may affect the correct interpretation of the breakdown pressure in a hydraulic fracturing test for a borehole that had breakouts. In this paper, two steps are taken to implement this investigation. First, sets of finite element modeling provide sets of data on borehole breakout measures. Second, for a given measure of borehole breakouts, according to the linear relation between the mud pressure and the stress on the borehole wall, the breakdown pressure considering the borehole breakouts is acquired by applying different mud pressure in the model. Results show the difference between the breakdown pressure of a circular borehole and that of borehole that had breakouts could be as large as 82% in some situations.

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)0.003
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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 routes1
Has abstractyes

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