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Record W4386953370 · doi:10.1115/omae2023-101472

Buoyant Miscible Jets in a Viscoplastic Medium With Applications in Plug and Abandonment of Oil and Gas Wells

2023· article· en· W4386953370 on OpenAlexaff
Hossein Hassanzadeh, Saptarshi Joshi, Soheil Akbari, Seyed Mohammad Taghavi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLaminar flowSpark plugMechanicsPenetration (warfare)NozzleJet (fluid)Materials sciencePlug flowFluid dynamicsViscoplasticityThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Plug and abandonment (P&A) is the final stage in the lifetime of oil and gas wells, during which the well is properly sealed to prevent fluid movements between different layers. The main steps of P&A include accessing the annulus section, cleaning the target area, and installing the cement plug barrier. Jet cleaning is an efficient technique for the second step of P&A, in which a heavy fluid is injected to displace lighter undesirable fluids. It is crucial to understand the effects of fluid properties on the jet cleaning process to achieve efficient cleaning. This study focuses on the jet flow dynamics in a miscible buoyant jet, where a Newtonian fluid is discharged through a circular nozzle into a viscoplastic ambient fluid. The effects of different parameters on the jet flow behaviour are analyzed, including the injection velocity and the yield stress of the ambient fluid. The buoyant jet flow behaviour is characterized using various jet features, including the laminar length and the penetration length. Our results show that the yield stress of the ambient fluid resists the jet evolution, decreases the jet penetration length, and changes the laminar length variation.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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
Published2023
Admission routes1
Has abstractyes

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