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Record W2513368606 · doi:10.1190/segam2016-13948039.1

Detecting bypassed pay from 3D seismic data using a facies-based Bayesian seismic inversion, Forties Field, UKCS

2016· article· en· W2513368606 on OpenAlexaff
K. Waters, Ana Somoza, Grant Byerley, Phil Rose

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsGeologyInversion (geology)FaciesSeismologySeismic inversionPaleontologyGeographyData assimilationMeteorology

Abstract

fetched live from OpenAlex

The Forties Field, UKCS, is the largest oil field in the UK sector of the North Sea with an estimated STOOIP between 4.2 and 5 billion barrels. Discovered in 1970 by BP, the field was brought online in 1975. In 2003 Apache Corp acquired the Forties field from BP and quickly pursued a program of 4D seismic interpretation to assist in field remediation. After some 40 years of production the field continues to produce at daily production rates exceeding c. 45K bopd. In this paper we present the results of a new inversion technology applied to the Forties field to assist in the detection of direct hydrocarbon indicators and remaining reserves from two vintages of 3D seismic. Presentation Date: Tuesday, October 18, 2016 Start Time: 3:20:00 PM Location: 156 Presentation Type: ORAL

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.682

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 head0.042
GPT teacher head0.280
Teacher spread0.237 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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