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Record W2762108494 · doi:10.1115/omae2017-62310

Investigation of Pressure Losses in Eccentric Inclined Annuli

2017· article· en· W2762108494 on OpenAlexaff
Sayeed Rushd, Rasel A. Sultan, Mohammad Azizur Rahman, Vassilios C. Kelessidis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputational fluid dynamicsPressure dropTurbulenceLaminar flowMechanicsRheologyEccentricity (behavior)Fluid dynamicsDrilling fluidMaterials scienceGeologyGeotechnical engineeringMechanical engineeringEngineeringDrillingPhysicsComposite material

Abstract

fetched live from OpenAlex

Accurate pressure drop estimation is vital in the hydraulic design of annular drillholes in Petroleum Industry. The present study investigates the effects of fluid velocity, fluid type, fluid rheology, drillpipe rotation speed, drillpipe eccentricity and drillhole inclinationon on pressure losses with the presence of cuttings using both experiments and computational fluid dynamics (CFD). The eccentricity of the drillpipe is varied in the range of 0 – 100% and it rotates about its own axis at 0 – 150 rpm. The diameter ratio of the simulated drillhole is 0.56 and it is inclined in the range of 0 – 15°. The effects of fluid rheology are addressed by testing power law and yield power law fluids. Both of the laminar and turbulent conditions are experimentally tested and numerically simulated. Experimental data confirmed the validity of current CFD model developed using ANSYS 16.2 platform. The goal of the current work is to develop a comprehensive CFD tool that can be used for modeling the hydraulic conditions associated with hole cleaning in extended reach drilling.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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 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
Published2017
Admission routes1
Has abstractyes

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