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Record W2086140419 · doi:10.4043/22159-ms

Sensitivity Considerations for Overturning Moment Calculations in the High Arctic, Deep Water, Offshore Environment

2011· article· en· W2086140419 on OpenAlexaboutno aff
Joshua Blunt, Victor Y. Garas, Dmitri Matskevitch, Oleg E. Esenkov

Bibliographic record

VenueOTC Arctic Technology Conference · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMoment (physics)Sensitivity (control systems)ArcticContext (archaeology)GeologySea iceSubmarine pipelineArctic ice packGeotechnical engineeringEngineeringClimatologyOceanographyPhysics

Abstract

fetched live from OpenAlex

Abstract This paper examines sensitivity to treatment of multi-year sea ice loads against sloped sided, bottom founded structures for use in the high Arctic, deep water environment (up to 100+m water depth). The sensitivities are examined within the context of real data; an example ice feature has been selected and the influence of seasonal variation is considered as well. An established analytical technique - Ralston's (1977) method for sheet ice loads on conical structures, specified in the ISO 19906 (2009) Arctic offshore structures standard - is used to develop the load criteria for the selected ice feature and various structure slope angles. The study demonstrates gap areas where expert assumptions are required in order to completely characterize the sea ice load demand. Resultant locations for horizontal and vertical ice loads on the structure are shifted within a reasonable domain of occurrence and the corresponding overturning moments are calculated. Results showed significant sensitivity of the ice induced overturning moment to structure diameter along the load resultant plane of action, ice ride-up thickness, and the structure diameter at the maximum ride-up height. For the base case values assumed in this study, increasing the assumed contact diameter may increase the overturning moment. Increasing the ride-up thickness also results in larger overturning moments, while a larger top diameter (lower ride-up height) reduces overturning moment. Variations in these three parameters are not explicitly accounted for in the Ralston method. Introduction Global energy demand has driven oil and gas exploration to some of the most remote regions of the world. One such region is the Arctic offshore environment where loads from sea ice pose significant challenges for offshore structure design. Historically, hydrocarbon exploration in the Arctic offshore has been conducted in relatively shallow water (< 30m) within the continental shelf. Typical methods consisted of using a large gravity-based structure (GBS) designed to withstand the ice loads related to a specific drilling location. In the case of the Canadian Beaufort, as exploration shifts toward the outer reaches of the continental shelf, both the deeper water environment (up to 100+m water depth) and the dominant ice regime (multi-year ice) will have significant influence on the selection of drilling and production concepts. Given these considerations, the question arises as to whether current analytical techniques, primarily calibrated for use in the first-year ice sub-Arctic environment, are robust enough to be used in the high Arctic deep water environment. For sloped and conical structures in particular, operational experience and the amount of full scale data for multi-year ice interactions are minimal. This makes the extension of analytical techniques (particularly adept at characterizing model scale behavior) difficult when full scale behavior is considered. Given these limitations, the analytical relationships can be explored through sensitivity studies to highlight the epistemic (more commonly referred to as Type II) uncertainties in the method. This is performed in order to identify variables that should be emphasized in future basin testing and analytical studies in order to gain a more robust understanding of the ice-induced demands on high Arctic structures.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.999

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)0.0020.000

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 head0.027
GPT teacher head0.204
Teacher spread0.177 · 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.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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