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Record W2090560788 · doi:10.2118/109999-ms

The Challenges of Designing HP/HT Equipment

2007· article· en· W2090560788 on OpenAlexaff
Andrew J. Forsyth, Brian Saucier, Daniel More, Ray Stawaisz, D. R. Gill, R.N. Younger

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2007
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsDeep River Science Academy
Fundersnot available
KeywordsSystems engineeringReliability (semiconductor)Computer scienceProcess (computing)Key (lock)Engineering design processDesign processReliability engineeringField (mathematics)Process designPerspective (graphical)Risk analysis (engineering)EngineeringWork in processOperations managementComputer securityMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The Paper presents an overview of the main design activities for the Completion and Intervention Landing String System that was deployed in the Tahiti field in the Gulf of Mexico. The functional and structural design challenges encountered due to the high operating pressures and temperatures are reviewed. The Paper also illustrates how analysis tools were used to quantify the observations found during test, and subsequently assist in determining a design solution. A key aspect of the design process was to view the design and operations from a holistic perspective by evaluating the design throughout its operational life. This paper will therefore present how this design process established a design solution that increased the reliability and confidence of the Landing String System and therefore reduced the operational risk.

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)0.003
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.023
GPT teacher head0.242
Teacher spread0.219 · 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
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

Citations2
Published2007
Admission routes1
Has abstractyes

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