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Record W2026149935 · doi:10.4043/19270-ms

Small Field Development and Subsea Tieback Technologies

2008· article· en· W2026149935 on OpenAlexaffabout
David Hawkins, W.I. Chipman, Graham Dillabough, Max Ruelokke, Fred Way

Bibliographic record

VenueOffshore Technology Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsPetroleum Research Newfoundland and Labrador
Fundersnot available
KeywordsSubseaSubmarine pipelineSpare partPetroleumProduction (economics)EngineeringOil fieldOil productionPetroleum engineeringGeographyGeologyOceanographyMarine engineeringOperations managementPaleontology

Abstract

fetched live from OpenAlex

Abstract Twenty-four oil and/or gas discoveries have been made offshore Newfoundland and Labrador. Three of the oil discoveries have been developed and a fourth is under consideration. The focus of development activity has been on the larger oil discoveries. As production from the larger discoveries matures, facilities and other infrastructure will become available for development of the remaining smaller discoveries. Development and tie-in of smaller pools and fields provides an opportunity to utilize this spare production capacity at these fields. Currently, there are several satellite tie-in and expansion projects in progress and others are under review. Development of the discovered smaller fields will play an important part in sustaining production from offshore Newfoundland and Labrador. In addition, many of the offshore basins are under explored and represent other opportunities to supply the next round of developments. Introduction Newfoundland and Labrador, Canada's easterly province (Fig. 1) is strategically positioned on international shipping lanes, with unique access to global petroleum markets.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0360.005

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 head0.190
Teacher spread0.163 · 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
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
Published2008
Admission routes2
Has abstractyes

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