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Record W2028441106 · doi:10.4043/24632-ms

Increasing Role of Marine Support in Arctic Offshore Exploration Drilling

2014· article· en· W2028441106 on OpenAlexaff
Don Connelly, Alexander Brovkin

Bibliographic record

VenueOTC Arctic Technology Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsChevron (Canada)
Fundersnot available
KeywordsArcticSubmarine pipelineOffshore drillingDrillingThe arcticMarine engineeringChevron (anatomy)Oil explorationEnvironmental sciencePetroleum engineeringEnvironmental resource managementOceanographyGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract The remoteness of Arctic well sites, lack of infrastructure and severe environmental conditions dictate a high level of self-sufficiency in an offshore exploration drilling project. Chevron employs the concept of an Arctic Drilling System that, in addition to the appropriate Arctic Mobile Offshore Drilling Unit (MODU), will comprise of a number of marine vessels providing multi-functional support for drilling operations. The recent Macondo incident in the Gulf of Mexico has significantly impacted the conventional view on the multiple functionality of Arctic marine support by adding an in-field independent well containment capability that also has many functions. This paper will look into the range of functions and tasks that are expected of muti-purpose vessels supporting an Arctic offshore exploration drilling project and will demonstrate the major considerations that need to be taken into account when compiling a highly effective marine support fleet. The scarse supply of existing Arctic multi-support vessels versus expected industry demand will be demonstrated to emphasize the requirement for new designs and new construction.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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