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Record W1662663348

The Loss of the Sleipner A Platform

2013· article· en· W1662663348 on OpenAlexaff
Justine Barry

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2013
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNatural gas fieldSubmarine pipelineCredibilityEngineeringRisk managementPetroleumContingency planRisk analysis (engineering)BusinessGeologyComputer scienceNatural gasComputer securityWaste management
DOInot available

Abstract

fetched live from OpenAlex

Offshore installations are complex and expensive engineering structures that are unique in terms of their design and operational characteristics. Over the past three decades, there have been major advancements in the technology used to extract oil and gas, which has enabled exploration to extend to challenging and hostile environments, including the Troll field in the Norwegian sector of the North Sea. The Sleipner platform was the first of three concrete gravity base structures to be used to extract oil and gas from this field, and great effort was placed on a timely start-up of gas deliveries in order to ensure the credibility of Norway as a reliable European energy partner. The gravity base structure (GBS) of the Sleipner platform was the twelfth in a series of GBS platforms of Condeep-type designed and built by the company Norwegian Contractors in Gandsfjorden near Stavanger, Norway. On August 23 1991, during a controlled ballasting operation in preparation for deck mating, the Sleipner platform sank. All 14 people onboard the platform was rescued by nearby boats without injuries. The failure involved a total economic loss of about 700 million dollars. Safety is a major concern for all offshore operations and a proper risk management plan is critical in ensuring a safe and successful project. Risk management involves the identification of hazards associated with a specified activity in order to minimize the probability of their occurrence, and/or mitigate their consequences. Risk management is particularly important in hostile ocean environments such as the North Sea, where even the most routine and simple tasks could result in great consequences should trouble strike. The following paper will discuss the sinking of the Sleipner A platform and explore the investigation that took place immediately following the disaster. The paper will also discuss the coastal facilities required to construct Condeep platforms, as well as the role of risk management in the process. Finally, the impact to subsequent offshore structures following the Sleipner platform sinking will be assessed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.278

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 integrity0.0000.000
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 head0.004
GPT teacher head0.164
Teacher spread0.160 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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