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Record W1926365506 · doi:10.3968/5957

Finite Element Analysis of Perforated Casing High Stress Area Compressed Volume Coefficient

2014· article· en· W1926365506 on OpenAlexvenueno aff
Wei Li, Bin Yang, Chen Si-Tong

Bibliographic record

VenueAdvances in petroleum exploration and development · 2014
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCasingPerforationVolume (thermodynamics)Stress (linguistics)Finite element methodMaterials scienceCylinder stressMechanicsGeotechnical engineeringStructural engineeringEngineeringComposite materialPetroleum engineeringPhysics

Abstract

fetched live from OpenAlex

In view of the problem that perforation completion result in casing damage, from the perspective of perforating casing overall stress, on the basis of plate and shell opening mechanics model, establishing the cloth of spiral casing perforation holes finite element mechanics model, reflecting the area around the hole stress change after the casing perforation clearly. Comparing different perforation parameters before and after perforating casing under the condition of high stress area volume ratio, concluding the high stress area compressed volume coefficient, Analysis of the differential pressure under the condition of normal production, the influence of different perforation parameters (bore diameter, shooting density, phase angle) to the perforated casing compressed volume coefficient. The results of the analysis shows that different perforation parameters on the perforated casing all affect the compressed volume coefficient. According to the characteristics of the different effect law to different parameters, providing the optimization scheme of perforating parameters that reducing the volume coefficient of perforated casing high stress area. On the whole to reduce perforated casing high stress area compressed volume under normal production conditions. Key words: Perforation; Casing failure; Finite element method; High stress area; Compressed volume coefficient

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinglow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinglow
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.207
Teacher spread0.198 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical · Methods

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

Citations2
Published2014
Admission routes1
Has abstractyes

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