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Record W2559037827 · doi:10.4043/27419-ms

Modeling of Heat Loss from Offshore Buried Pipeline through Experimental Investigations and Numerical Analysis

2016· article· en· W2559037827 on OpenAlexaff
Suvra Chakraborty, Vandad Talimi, Yuri S. Muzychka, Rodney McAffee

Bibliographic record

VenueArctic Technology Conference · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsCentre For Cold Ocean Resources EngineeringMemorial University of Newfoundland
Fundersnot available
KeywordsFlow assurancePipeline transportSubmarine pipelinePermafrostPetroleum engineeringEnvironmental scienceHeat transferNatural convectionGeologyGeotechnical engineeringTrenchConvectionThermal conductionArcticMechanicsMaterials scienceEnvironmental engineeringOceanography

Abstract

fetched live from OpenAlex

Abstract Offshore oil and gas production in arctic areas is challenging due to harsh environmental conditions. Pipeline burial and trenching in those areas are now one of the prime methods to avoid ice gouge risks and other threats. Thermal management becomes critical when the ambient temperature is low such as the typical seawater temperature on the sea bed. The flow temperature and pressure affect viscosity of the fluid traveling through the pipeline and determines the state of the fluid (single or multiphase). The effect of freezing around oil and gas pipes in the vicinity of permafrost is considered as another concern for flow properties of oil and gas in arctic regions. Theoretical shape factor model has been widely utilized to estimate heat loss from buried pipelines. This study examines the validity of using this method for flow assurance calculations. Several steady state and transient experiments have been carried out to model the heat loss considering different parameters such as burial depth, backfill soil, trench geometries etc. Natural convection can play a significant role in the overall heat loss process from the buried pipeline. The total heat loss increases significantly when the backfill soil is loose or sandy. This paper illustrates the effects of heat conduction and natural convection in the heat loss mechanism from buried pipelines. The outcome of this paper will provide valuable heat loss models based on experimental and numerical analysis results. These outputs can be used largely in petroleum industries for designing pipelines in offshore arctic areas and to mitigate several flow assurance issues (e.g. wax and hydrate formation in the pipeline effectively). The methodology used in this research i.e. analyzing the experimental data with two steps of validation will ensure the validity of the proposed model. Using different parameters such as burial depths, trench geometries, and backfill soil this paper provides an effective model for the offshore pipeline design.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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