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Expansion of Tubular with Elastomers in Multilateral Wells

2013· article· en· W2182923164 on OpenAlexaff
M. van de Velden, F. K. Al-Jahwari

Bibliographic record

VenueThe Journal of Engineering Research [TJER] · 2013
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMandrelElastomerMaterials scienceExpansion ratioStress (linguistics)DrillingProcess (computing)Composite materialStructural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

The use of solid expandable tubular technology during the last decade has focused on solving many challenges in well drilling and delivery including zonal isolation, deep drilling, conservation of hole sizes, etc. not only as pioneered solution but also providing cost effective and long lasting solutions. Concurrently, the technology was extended for construction of multilateral in typical wells. The process of horizontal tubular expansion is similar to the vertical expansion of expandable tubular in down-hole environment with the addition of uniformly distributed force due to its weight. The expansion is targeted to increase its diameter such that post expansion characteristics remain within allowable limits. In this study a typical expandable tubular of 57.15 mm outer diameter and 6.35 mm wall thickness was used with two different elastomer seals of 5 and 7 mm thickness placed at equal spacing of 200 mm. The developed stress contours during expansion process clearly showed the high stress areas in the vicinity of expansion region which lies around the mandrel. These high stresses may result in excessive wear of the mandrel. It was also found out that the drawing force increases as the mandrel angle, expansion ratio, and friction coefficient increases. A mandrel angle of 20o requires minimum expansion force and can be considered as an optimum geometrical parameter to lower the power required for expansion.

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)0.001
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 score0.045
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.238
Teacher spread0.225 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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