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Record W1555708221 · doi:10.3968/7104

The Numerical and Experimental Research on Solid-liquid Two-phase Flow Pattern in the PID

2015· article· en· W1555708221 on OpenAlexvenueno aff
Xueyang Xing, XU Yi-ji

Bibliographic record

VenueAdvances in petroleum exploration and development · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsAnnulus (botany)Drilling fluidSettlingParticle (ecology)MechanicsViscosityFlow (mathematics)DrillingPID controllerTwo-phase flowMaterials sciencePetroleum engineeringGeologyEngineeringMechanical engineeringPhysicsThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Particle impact drilling (PID) technology can effectively improve the speed of drilling in deep and hard formations. It is important to study the particle flow pattern in the annulus since it can ensure the smooth implementation of PID. In this paper, experimental and numerical methods are used to study the law of motion in the annulus. This study finds that the distribution of particles in the annulus is uneven, and the particle settling velocity is hardly affected by the fluid density, fluid velocity, particle volume concentration. Particle settling velocity decreases with the increasing of drilling fluid viscosity, but increases with the increasing of the particle diameter. On this basis, the reasonable parameters of the drilling fluid for PID are obtained. Key words: Petroleum engineering; Particle impact drilling; Solid-liquid two-phase flow

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.060
GPT teacher head0.364
Teacher spread0.305 · 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
Published2015
Admission routes1
Has abstractyes

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