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Record W1876428690 · doi:10.3968/5954

Numerical Simulation of the Effects of Slurry Properties on Displacement Efficiency

2014· article· en· W1876428690 on OpenAlexvenueno aff
Zhang Duoyuan

Bibliographic record

VenueAdvances in petroleum exploration and development · 2014
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDrilling fluidCementDisplacement (psychology)RheologyFluentSlurryPetroleum engineeringMaterials scienceConsistency (knowledge bases)ViscosityBingham plasticGeotechnical engineeringComputer simulationDrillingMechanical engineeringEngineeringComposite materialComputer scienceSimulation

Abstract

fetched live from OpenAlex

In the course of cementing, improving the displacement efficiency in the annular is a basic premise to prevent drilling fluid channeling, guarantee the bonding strength of set cement and improve the sealing property of cement sheath. Based on the CFD software of FLUENT, effects of rheological parameters on the displacement efficiency were studied by means of numerical simulation. For Bingham Model or Power Low Model fluid, the simulation results show that increasing consistency coefficient of cement slurry, liquidity index of drilling fluid, plastic viscosity of cement slurry and the yield point of cement slurry or drilling fluid will help to improve the displacement efficiency. On the contrary, increasing liquidity index of cement slurry, consistency coefficient of drilling fluid or increasing plastic viscosity of drilling fluid will decrease the displacement efficiency. Key words: Displacement efficiency; Slurry properties; Numerical simulation; FLUENT

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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