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Record W4380685277 · doi:10.3390/math11122689

Estimating the Critical Velocity of the Incipient Motion of Particles on the Cuttings Bed Surface: An Experimental and Theoretical Analysis

2023· article· en· W4380685277 on OpenAlexaff
Kaixiang Shen, Zhenqiang Xu, Guoshuai Ju, Yingsheng Wang, Zijian Li

Bibliographic record

VenueMathematics · 2023
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsDrill cuttingsAnnulus (botany)DrillingCuttingMechanicsDrilling fluidGeotechnical engineeringShear stressPetroleum engineeringCritical ionization velocityGeologyMaterials scienceEngineeringMechanical engineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

During the drilling of highly deviated and horizontal wells, a pump shutdown causes drill cuttings to settle and form a cuttings bed in the annulus. This study investigated the incipient motion law of the particles on the cuttings bed surface when the drilling fluid starts circulating again. This work could help field engineers to determine a reasonable incipient pump displacement to improve hole-cleaning efficiency. In this study, the effects of the well inclination angle, cuttings size, and different cuttings densities on the critical velocity of particle motion are analyzed experimentally, using a large-scale flow loop. Next, based on a stress analysis of the particles on the cutting bed surface and on the boundary layer flow around the particles, an analytical formula for the surface shear force of the drilling fluid on particles is derived and a critical velocity model for incipient motion is established. Verification is then carried out and combined with the experimental results. This study has important implications for the design of drilling operations and for the management of cuttings transport in oil and gas wells. It can guide the setting and prediction of pump discharge to improve hole-cleaning efficiency.

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 score0.111
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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