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Record W2562821943 · doi:10.1115/1.4035514

Drill Bit Contact Dynamics Including Side Cutting: Simulation and Validation

2016· article· en· W2562821943 on OpenAlexafffund
Alfonso Callejo, Siamak Arbatani, József Kövecses, Masoud Kalantari, Nick R. Marchand

Bibliographic record

VenueJournal of Energy Resources Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDrillingComputer scienceCurvatureContext (archaeology)Finite element methodDrill bitFidelityContact dynamicsDirectional drillingDrillWellboreMechanical engineeringSimulationEngineeringStructural engineeringGeologyPetroleum engineeringMathematical optimizationGeometryMathematics

Abstract

fetched live from OpenAlex

Simulation techniques are increasingly becoming popular in recent years as a way of simulating oil drilling processes. Among them, directional drilling is a specific method that benefits enormously from such numerical techniques, inasmuch as the estimation of the wellbore curvature is crucial and cannot be properly estimated through approximate geometry methods. We present here some of the latest advances in bit contact dynamics, wellbore update algorithms, and experimental validation of side cutting, in the context of a finite element (FE) and finite segment simulation framework. The framework is based on the high-fidelity dynamic simulation of the mechanical system, including detailed geometry, large displacements, and accurate contact forces. The theoretical aspects, along with the experimental results, are thoroughly presented. Overall, this paper constitutes a step toward a more deterministic way of calculating build rates and designing downhole drilling tools.

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)0.000
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 score0.211
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.218
Teacher spread0.209 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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