MétaCan
Menu
Back to cohort
Record W1995468494 · doi:10.1115/imece2014-37198

Dynamic Modelling of Horizontal Shafts With Annular Surface Contact and Friction: Application to Oilwell Drilling

2014· article· en· W1995468494 on OpenAlexaff
Md. Mejbahul Sarker, Geoff Rideout, Stephen Butt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVibrationBoreholeDrill pipeDrillingGeologyBit (key)Drill bitJoint (building)Directional drillingEngineeringStructural engineeringGeotechnical engineeringAcousticsMechanical engineeringPhysicsComputer science

Abstract

fetched live from OpenAlex

Lateral whirl vibrations in long sections of horizontal oilwell drillstrings, which are essentially enclosed shafts lying on the low side of the wellbore, are potentially destructive to the bit, pipes and downhole tools. Forward or backward whirl can lead to impact with the borehole, and stick slip and bit bounce can cause tool joint failure, twist-off, and bit damage. A complete deviated drillstring has been modelled by having decoupled axial and torsional segments for the vertical and curved portions, and nonlinear three-dimensional multibody segments with lateral vibration in the final horizontal section ending at the bit. The model can predict how axial and torsional bit-rock reactions are propagated to the surface, and the role that lateral vibration near the bit plays in exciting those vibrations and stressing components in the bottom-hole-assembly. The proposed model includes the mutual dependence of these vibrations, which arises due to bit-rock interaction and friction dynamics between the drillstring and wellbore wall.

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)0.001
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.003
GPT teacher head0.162
Teacher spread0.159 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicDrilling and Well EngineeringFrench-language works237,207