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Mechanistic Leak-Detection Modeling for Single Gas-Phase Pipelines: Lessons Learned from Fit to Field-Scale Experimental Data

2013· article· en· W1889120062 on OpenAlexvenueno aff
Ali Edrisi, S. I. Kam

Bibliographic record

VenueAdvances in petroleum exploration and development · 2013
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsLeakGas leakPipeline transportPetroleum engineeringEnvironmental scienceInletEngineeringMarine engineeringEnvironmental engineeringChemistryMechanical engineering

Abstract

fetched live from OpenAlex

The use of pipelines is one of the most popular ways of delivering gas phases as shown by numerous examples in hydrocarbon transportation systems in Arctic regions, oil and gas production facilities in onshore and offshore wells, and municipal gas distribution systems in urban areas. A gas leak from pipelines can cause serious problems not only because of the financial losses associated but also its social and environmental impacts. Therefore, establishing an early leak detection model is vital to safe and secure operations of such pipeline systems. A leak detection model for a single gas phase is presented in this study by using material balance and pressure traverse calculations. The comparison between two steady states, with and without leak, makes it possible to quantify the magnitude of disturbance in two leak detection indicators such as the change in inlet pressure (ΔP in ) and the change in outlet flow rate (Δq out ) in a broad range of leak locations (x leak ) and leak opening sizes (d leak ).  The results from the fit to large-scale experimental data of Scott and Yi (1998) show that the value of leak coefficient (C D ), which is shown to be the single-most important but largely unknown parameter, ranges from 0.55 to 4.11, and should be a function of Reynolds number (N Re ) which is related to leak characteristics such as leak location (x leak ), leak opening size (d leak ), leak rate (q leak ) and system pressure. Further investigations show that between the two leak detection indicators, the change in outlet flow rate (Δq out ) is superior to the change in inlet pressure (ΔP in ) because of larger disturbance, if the pressure drop along the pipeline is relatively small compared to the outlet pressure; otherwise, the change in inlet pressure (ΔP in ) is superior to the change in outlet flow rate (Δq out ). Key words : Leak; Leak detection modeling; Pipeline; Leak coefficient; Gas flow in pipe

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)0.003
metaresearch head score (Gemma)0.007
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
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 head0.103
GPT teacher head0.314
Teacher spread0.212 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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