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Record W2935748450 · doi:10.1002/9781118476406.emoe356

Blowout Risk in Drilling and Production

2018· other· en· W2935748450 on OpenAlexaff
Faisal Khan, Majeed Abimbola, Seyed Javad Hashemi

Bibliographic record

VenueEncyclopedia of Maritime and Offshore Engineering · 2018
Typeother
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRisk analysis (engineering)Risk managementSAFERRisk assessmentProduction (economics)Submarine pipelineOffshore drillingRisk ControlControl (management)Well controlEngineeringDrillingComputer scienceBusinessComputer security

Abstract

fetched live from OpenAlex

Abstract The risk of blowout is one of the major threats for the safe operation of offshore drilling and production. Although conventional risk assessment methods have played an important role in identifying major risks and maintaining safety in offshore facilities, they have several disadvantages due to their structural limitations. The complex dynamic mechanism of blowouts, well‐specific risk factors due to specific reservoir and underground conditions, environmental and operational uncertainties, complexity of the decisions needed to respond to well control incidents, and time‐dependent degradation of blowout barriers require the development and implementation of advanced risk assessment and management techniques to incorporate these risk factors. This article describes the blowout scenarios and important risk factors that should be taken into account for blowout risk assessment. Then, a comparative study of existing blowout risk assessment techniques is provided. The main technological challenges and the requirements to implement an effective dynamic risk management of drilling and production facilities are discussed. The integration of the dynamic risk assessment of blowout with management systems is also discussed, which helps to enable safer complex offshore operations in extreme environments.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.623
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2018
Admission routes1
Has abstractyes

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