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Record W4312247035 · doi:10.1115/omae2022-79753

Experimental Study of Newtonian Laminar Annular Horizontal Displacement Flows With Rotating Inner Cylinder

2022· article· en· W4312247035 on OpenAlexaff
Heeseok Jung, I.A. Frigaard, Ruizi Zhang, Alondra Renteria

Bibliographic record

VenueVolume 10: Petroleum Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMechanicsLaminar flowCylinderBuoyancyDisplacement (psychology)Annulus (botany)Rotation (mathematics)Newtonian fluidEccentricity (behavior)Rotational speedPhysicsCentrifugal forceFlow (mathematics)Classical mechanicsMaterials scienceGeometryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract During the primary cementing of oil and gas wells, it is increasingly becoming common to slowly rotate the inner casing to aid the mud displacement. We present an experimental study of laminar horizontal displacement of Newtonian fluids with rotating inner cylinder with the experimental setup detailed in (Renteria et al., J. Fluid Mech, 905, 2020). Inner cylinder rotation is able to distort the axial flow to helical. When the rotational viscous force dominates buoyancy force, the displacing fluid follows the helical flow path and displaces the in situ fluid azimuthally around the annulus. The dimensionless rotational speed is defined as the ratio of rotational velocity and axial velocity, which determines the degree of azimuthal dispersion. Even in a buoyancy-dominant displacement, inner cylinder rotation acts to increase the dispersion. Displacement efficiency analysis shows that increasing rotational speed results in improved displacement. For high eccentricity cases, bottom side residual fluids are observed, which can easily be removed by rotating the inner cylinder. In practice, where the entire length of casing is rotated, the effect of rotation is expected to be more significant and improve the displacement.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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.

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
Published2022
Admission routes1
Has abstractyes

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