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Record W2558119812 · doi:10.4043/27333-ms

Pack Ice Loads on Seasonally Operating Semi-Submersible in the Labrador Sea

2016· article· en· W2558119812 on OpenAlexaffabout
Jan Thijssen, Mark Fuglem, John Dolny

Bibliographic record

VenueArctic Technology Conference · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCentre For Cold Ocean Resources Engineering
Fundersnot available
KeywordsSea iceOceanographyGeologyArctic ice packEnvironmental scienceClimatology

Abstract

fetched live from OpenAlex

Abstract Semi-submersible drilling platforms are typically moved off site given any threat of pack ice incursion. Operations in icy waters requires considerations of, amongst others, ice interations with the facility. The offshore industry will benefit from a standardized methodology to evaluate the capability of semi-submersibles in ice during drilling operations. Operators and drilling contractors are particularly interested in understanding how the drilling season may be extended into the shoulder season. This requires an understanding of variability in site-specific ice conditions throughout the year. Ice load analysis is needed for semi-submersible rigs operating in ice prone regions to determine ice strengthening requirements. Ship-based ice class rules can be considered for the design loads of the pontoons in transit conditions, but there is no standardized methodology for determining ice loads for the operational conditions. This paper focusses on the operational phase, where loads act on the vertical-faced columns. ISO 19906 (2010) offers a framework for determining sea ice loads in the form of a deterministic equation that has been established for fixed structures mainly operating on a year-round basis. The results will generally be quite conservative for seasonal operations. Consideration of ice exposure, to account for the limited drilling season, is permitted by ISO 19906 using probabilistic approaches, though no specific guidance is provided. Seasonal operations can be planned to avoid the most severe winter conditions, allowing for a reduction of the design level ice conditions. This reduction in the severity of sea ice that impacts semi-submersible columns should be accounted for in determining design ice loads. This paper demonstrates application of an analytical approach to include exposure considerations to estimate extreme ice loads for various drilling season extensions. An approach is demonstrated here for determining design sea ice loads to evaluate the capability of a semi-submersible in pack ice conditions. The approach considers the possibility of extended season drilling operations, rather than year-round operations, and may permit more efficient exploration in Arctic and sub-Arctic regions in the future. A study case is presented for a semi- submersible operating in the early ice season at a selected location in the Labrador Sea. The approach can be easily adopted for operations in other regions and other structure types, but is dependent on the availability of reliable data on ice conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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 designObservational
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

Citations2
Published2016
Admission routes2
Has abstractyes

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