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Record W1976785349 · doi:10.3997/2214-4609.201411978

Maximizing the Field Value of Edvard Grieg Using 4D Surveillance – The Feasibility Phase

2015· article· en· W1976785349 on OpenAlexaff
P.E. Dhelie, P. Ø. Seljebotn, H. Stenmark, Abel Onana Ndingwan, Terje Kollien, Giles Watts, M. Bacon

Bibliographic record

VenueProceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsGeoscience BC
Fundersnot available
KeywordsNorth seaField (mathematics)Oil fieldGeologyPetroleum engineeringEnvironmental scienceComputer scienceSeismologyRemote sensingOceanography

Abstract

fetched live from OpenAlex

Summary Time lapse seismic and 4D reservoir surveillance technology is a well-developed and extensively used field monitoring technique in the North Sea. Today, a number of fields use various 4D technologies from repeat streamer seismic surveys (e.g. Norne, Schiehallion, Ringhorne, Gullfaks) to highly advanced trenched permanent reservoir monitoring (PRM) systems (e.g.Valhall, Ekofisk, Clair, Snorre and Grane) in order to extend and extract as much value as possible over the life of the field ( Watts 2013 ). The benefits of 4D seismic in extending field life, complex understanding of the field, reservoir monitoring and increasing field recovery factor are all well understood and well established on many North Sea oil fields ( Fayemendy et al. 2012 ).

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.075
GPT teacher head0.350
Teacher spread0.275 · 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
Published2015
Admission routes1
Has abstractyes

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