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Record W2055026412 · doi:10.2118/116927-ms

Large Bore Subsea Production Systems for Woodside's Gas Developments

2008· article· en· W2055026412 on OpenAlexaff
G.P. Jenner, Gordon Hughes

Bibliographic record

VenueSPE Asia Pacific Oil and Gas Conference and Exhibition · 2008
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsIntecsea (Canada)
FundersWoodside
KeywordsSubseaPetroleum engineeringProduction (economics)Computer scienceMarine engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Due the high cost of deepwater subsea wells, large bore subsea production systems with high flowrate potential are desirable to lower the development cost of Woodside's gas reserves. With an ever increasing demand for reliable gas supply to LNG facilities, subsea system reliability in Woodside's exsiting and future gas development is of equal importance. With the number of large gas reserves in its portfolio, Woodside has developed an evolving strategy to deliver a suite of standard large bore reliable subsea production components to enable reliable development of gas fields for lower unit cost. This paper will describe how Woodside has worked together with its subsea system partner, FMC Technologies, to develop the equipment and systems required to facilitate reliable production of deep water high rate gas wells, including Standard 7" gas tree;Manifolds and Pipeline termination assemblies;Large bore diver-less connection systems;Umbilicals and control systems;Managing flow assurance issues (Incl. hydrate prevention, sand and flow management); Additionally, the paper will discuss the "stepping stone" approach to developing and proving the large bore system components through initial application on the NWS to the deepwater application of Pluto and will highlight the installation drivers associated with this development approach.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.621

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)0.0000.000

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 head0.018
GPT teacher head0.200
Teacher spread0.183 · 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 designOther design
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

Citations1
Published2008
Admission routes1
Has abstractyes

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