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Record W4224212292 · doi:10.2118/209262-ms

Detection of 2.187 Gallon Pipeline Leakage by Optical Sensor Based on Coherent Interaction of Pulse and Depleted Pump Technology

2022· article· en· W4224212292 on OpenAlexaff
Lufan Zou, Omur Sezerman

Bibliographic record

VenueSPE Western Regional Meeting · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsOZ Optics (Canada)
Fundersnot available
KeywordsLeakage (economics)Pipeline transportOptical fiberEnvironmental scienceAttenuationPetroleum engineeringMaterials scienceEngineeringOpticsEnvironmental engineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Pipelines provide one of the safest and most reliable means for transportation of oil and gas. Due to the severity of the economic and environmental impacts associated with leakages, the industry is constantly seeking more efficient and reliable monitoring systems. Extending the sensing range on long-distance pipelines is a challenge. Increasing the optical power to compensate for higher attenuation or lower SNR due to the fiber length increases the risk of fire when a rupture occurs. A coherent probe-pump-based Brillouin sensor with less than 1mW (Laser Class 1M) of cw pump power has been developed for use with sensing fibers up to 200 km. It works by controlling the depletion of the pump beam resulting from the strong coherent interaction of the probe and the depleted pump (CIPDP). The initial results from detection of pipeline leakage using a DSTS based on CIPDP technology are presented. The overall goal of the experimental procedure is to assess the leakage detection capabilities of the optical sensing technologies. A blind test methodology was followed in which the number, position, and time of the leaks were not known beforehand to the sensor manufacturers. Approximately one-third of the pipe diameter was embedded in soil, so leaks were limited to the lower portion of the pipe. The remainder of the pit was filled with water. Within the pipeline, a system of tubing and valves was used to route the test fluid to the appropriate locations to simulate leaks. A leakage volume of 2.187 gal was detected in one minute by a DSTS based on CIPDP technology. This simulated leak happened through a 1/8″ orifice with an injection pressure of 105 psi and a difference of 39°F (21.6°C) between the line temperature and the soil temperature. When the injection pressure dropped to 22 psi and the difference between the line temperature and the soil temperature dropped to 20°F (11.1°C), leakage volumes as small as 6.72 gal were detected in 5 minutes. All leaks with different volumes controlled by injection pressure, orifice, line temperature, and duration were detected successfully.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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