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Record W2155694532 · doi:10.1115/imece2013-62572

Dynamic Analysis of Bottom Hole Assembly With External Vibrating Force Excitation

2013· article· en· W2155694532 on OpenAlexafffund
Lei Wang, Stephen Butt, Jianming Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandAtlantic Canada Opportunities Agency
KeywordsInertiaComputational fluid dynamicsDrillingVibrationParametric statisticsFluid dynamicsDynamic pressureMechanical engineeringFlow (mathematics)Dynamic simulationDrilling fluidMechanicsComputer scienceEngineeringSimulationAcousticsPhysics

Abstract

fetched live from OpenAlex

Based on the Downhole Oscillating Device (DOD) newly developed for purpose of improving drilling efficiency, Computational Fluid Dynamics (CFD) simulation is conducted. The axial vibration force created by the DOD is thoroughly investigated. The simulation is focused on the valve part which generates the fluid pressure pulsations. Fluid flow rate and/or back pressure is applied to the model as boundary conditions. The DOD’s application will also induce the inertia phenomenon of the fluid column above the tool. This phenomenon is also studied based on parametric analysis. Within the simulation results, a dynamic model is developed to further investigate the effect of the oscillating force generated by the DOD on drilling efficiency. Nonlinearities in the bit-rock interaction are taken into account in the model. Simulation results show that application of the DOD in drilling may improve the ROP at least by 5%.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.184
Teacher spread0.181 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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