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Record W2991103767 · doi:10.1115/omae2019-95005

Examination and Analysis of Four-Phase Four-Fluid Flow Techniques in Offshore Pipelines

2019· article· en· W2991103767 on OpenAlexaff
Mohamed Odan, Faraj Ben Rajeb, Mohammad Azizur Rahman, Amer Aborig, Syed Imtiaz, Yan Zhang, Mohamed M. Awad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSubseaPipeline transportFlow assuranceSubmarine pipelineFlow (mathematics)Component (thermodynamics)Petroleum engineeringMarine engineeringWork (physics)Pipeline (software)Phase (matter)Fluid dynamicsEnvironmental scienceTwo-phase flowGeologyComputer scienceEngineeringMechanicsGeotechnical engineeringMechanical engineeringEnvironmental engineeringPhysicsHydrateThermodynamics

Abstract

fetched live from OpenAlex

Abstract Many offshore drilling sites are plagued by infrastructure break-downs and other issues due to the complexity of the systems required for the subsea extraction of oil and gas (O&G). For the most part, the O&G industry uses multi-phase and multi-component pipeline flows to move their product from one site to another or to different areas of the same site. In extreme environments, such as offshore or Arctic sites, the development of Sand particles along the pipelines can bring additional challenges to the project. The present work examines the practicality of applying a system of four-phase four-fluid flows for transporting a multi-phase flow (sand, water, oil and CO2) along subsea pipelines. As a means for precisely measuring and predicting the characteristics of thermo- and hydro-dynamic multi-component mixtures, models representing phase behavior and hydrate equilibrium are built and tested. Additionally, the study looks at heat transfer, mass and momentum in both the flow and pipe walls and develops equations to describe their interrelationships. Another focus of this work it to investigate four-phase multi-component flow systems in order to obtain a deeper understanding of transient flow in various types of pipes, including conditions around the system of four-phase four-fluid flows, and hydro-dynamic variable effects on 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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.227
Teacher spread0.216 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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