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Record W1971804921 · doi:10.4043/23930-ms

Real-Time Subsea Fiber-Optic Monitoring

2013· article· en· W1971804921 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOffshore Technology Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsIntecsea (Canada)
FundersNational Aeronautics and Space Administration
KeywordsSubseaSoftware deploymentAgile software developmentSystems engineeringComputer scienceAdaptabilityOptical fiberEngineeringTelecommunicationsSoftware engineeringMarine engineering

Abstract

fetched live from OpenAlex

Abstract Commercially available fiber-optic sensors provide key information for well, reservoir and infrastructure surveillance. These fiber optic systems are now considered robust and see routine application in onshore and platform wells. Engineering, economic and project management challenges, however, delay the extension of these monitoring systems into the subsea environment. We illustrate the value of fiber optic monitoring, describe where fiber optic technology has significant potential in subsea development projects and identify the significant technology gaps which challenge subsea implementation of existing in-well fiber-optic sensing systems. We then define an approach to systematically address these gaps within the time frames of many current subsea projects, while preserving the necessary integrity of the subsea system and associated project delivery. The approach borrows from the Agile project management method and focuses on staged delivery of the fiber optic technology, maturing the technology by deploying select portions and then advancing in subsequent stages to the deployment of more-complex or more slowly evolving technologies, rather than attempting to fully mature the technology off-line prior to any deployment. It operates according to the following principles:–Deploy the more easily or readily adaptable portions of the technology first.–Ensure that each incremental deployment economically provides significant benefits, meeting business and technical surveillance targets.–Ensure that each deployment delivers technology maturation learnings that can be used to evaluate newly developed technology, with direct actionable feedback into further development plans.–Ensure that project plans contain sufficient adaptability and capacity for incorporating late-breaking or unforeseen technology advances, as well as contain contingencies to back off from portions of the technology deployment should best judgment dictate. In addition to accelerating technology advance, this approach assures further progress and technology acceptance along the way. We take inspiration from an historic application of such a method: NASA's journey to safely land men on the moon. In the staged development-deployment of the Gemini and Apollo programs, NASA consistently delivered tangible results with each launch, thus capturing and maintaining the support of the public, their primary stakeholders, throughout the program, as well as quickly making significant technical strides which ensured the accomplishment of their goal. Introduction Subsea wells represent expensive, high profile and high risk investments. It's no surprise that any suggestion to include new technology, such as fiber optic monitoring, in a subsea well is quite frequently rejected almost immediately despite value recognized by subsurface engineering and geoscience experts. Like all proponents of new technology, we share in the frustration that many people experience when greeted with this response. We acknwledge and appreciatethat the rationale for such strong resistance stems from the primary commitment of project managers, technical authorities, and subject matter experts to deliver the subsea project without delay, cost overrun or addition of unmanaged or unmanageable risk. We also observe that while the in-well technology advocates might seem to be at odds with installation project management, both groups actually have the same goal: to see the installation succeed in every respect while deliverying a well with high value.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.459
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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