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Record W4308460407 · doi:10.1115/1.4056177

Particles Transportation and Deposition Behavior During Horizontal Wellbore Exploitation

2022· article· en· W4308460407 on OpenAlexaff
Liu Yang, Yu Li, Deguo Wang, Dongyang Li

Bibliographic record

VenueJournal of Energy Resources Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsDeposition (geology)MechanicsSpark plugVolumetric flow rateMaterials scienceShearing (physics)WellboreParticle depositionPetroleum engineeringTurbulenceGeologyComposite materialEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Particles deposition and plug-in wellbore cause lots of damage and efficiency reduction during oil and gas exploitation. Transportation has been a focus for safety and production improvement. The annular flow field and particles transport behavior have been investigated using computational fluid dynamics with an renormalization group k–ɛ method. Effects of particles volume concentration, washing fluid concentration with power-law shearing, annular eccentricity, flow rate, and rotation speed have been researched to get the mechanism of particles accumulation and transport behavior. The results show that an increase in eccentricity causes velocity reduction of the annular narrow gap area, particles easily deposit, accumulate, and are hard to transport; increasing flow rate and fluid concentration apparently improve particles transportation and reduce deposition, pressure loss increases; inlet particles volume concentration increases deposition starting position and deposition length, the inner pipe rotation facilitates particles’ second suspension.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.185
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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