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Record W2000537418 · doi:10.4043/12149-ms

Development and Testing of the HydraStar Underwater Mateable, Fiber-Optic, Electric (Hybrid) Connector

2000· article· en· W2000537418 on OpenAlexaff
David Stephens, Gary Brown, Matt Christiansen

Bibliographic record

VenueOffshore Technology Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsSubseaCable glandModular designOptical fiber cableEngineeringComputer scienceMarine engineeringAutomotive engineeringTelecommunicationsOptical fiber

Abstract

fetched live from OpenAlex

Abstract Longer step-out distances, higher data rates and increased electrical noise levels in the umbilical have combined to create a need for fiber optic data links between subsea equipment and the surface operators. For the subsea industry to take full advantage of optical communications requires the use of an underwater mateable optical connector. Subsea mateable optical connectors enable the industry to build modular components that can be assembled on the seafloor and make disconnections and reconnections for future expansion or maintenance purposes. This paper presents an overview of the design, development, testing and track record of such a connector, the next generation Lockheed Martin developed SEA CON®HydraStar connector system. It is also shown that because of the HydraStar's reliability and high technical integrity the Operator can make significant cost savings in CAPital EXpenditure (CAPEX) and OPerating EXpenditure (OPEX). Background There are increasing economic pressures on Offshore Operators to optimize production from subsea oil and gas reservoirs. This includes new reservoirs that are more remote, in deeper water or increasingly complex. The rapid development of modern technology has facilitated the discovery, exploitation and enhanced production of these reservoirs. Examples of how this has been achieved:Increasingly sophisticated and significantly faster seismic streamer array processingMore sophisticated and complex deepwater drilling systemsThe use of high power transmission systems which rule out conventional electrical data communications due to high electrical noise levels (Electro Magnetic Interference (EMI))Fast data transfer from subsea to topside enables immediate assessment of;Reservoir performance and optimizationHealth and status of subsea equipment (for safety and to better understand equipment maintenance regimes)Raw subsea dataIncreasingly sophisticated subsea and downhole control and monitoring systems to cater for;Intelligent subsea well systemsMultilateral well systemsSeparation and processing systemsProduction boosting systemsFast control to ensure subsea/downhole high power pumps and motors can be safely controlled within operational parameters at remote distancesDownhole and/or subsea electrical and/or optical instrumentation such as pressure, temperature, flow rate, water cut, 3-phase measurements, oil-in-water, water-inoil, seismic sources and sensors etc. to provide more and accurate information about the depleting or changing state of the reservoirsDownhole high temperature electronics systems or optical systems because higher downhole and reservoir well temperatures mean conventional electronics cannot be used reliablyHigher subsea electrical voltage and power requirements for subsea and downhole systems and applications that cater for longer step-outs and deeper water. To assist the use of this type of technology subsea requires communication systems that can transfer data at much faster rates (i.e. higher bandwidth) than currently available using conventional electrical communication systems. It is the advent and use of optical communications systems that has helped these technologies to develop and will continue to facilitate them in their application for use in subsea oil and gas environs. Introduction In general there are three main reasons to use optical communication systems:

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.181
Teacher spread0.169 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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