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Record W2050193227 · doi:10.2118/145493-ms

The Maule Field: An Economic Small Field Development

2011· article· en· W2050193227 on OpenAlexaff
Jeff Pyle, Klaas Koster, Phil Rose, Gregg Barker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsGeologyDrillingSubmarine pipelineOil fieldPetroleum engineeringGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Apache discovered the Maule field in October 2009 with the 21/10-A52 wellbore drilled from the Forties Alpha production platform. The discovery of a 14m TVD net oil column in Eocene aged Brimmond sands led to the development of the Maule field under the UK government's small field allowance scheme. The field was taken from completion of the discovery well to first oil from a horizontal production well in under 9 months. The Maule field is located on the western margin of the Brimmond Formation turbidite fairway where re-mobilised sands are also present. Steep dips as seen in the development well and steep seismic features indicate that the Maule field reservoir was formed by remobilsation of Brimmond sands. The A52 exploration well was drilled on the basis of a seismic amplitude anomaly. This seismic data along with LWD density image data was used to successfully place the horizontal production well which accessed a 114m MD pay section and flowed in excess of 11,500 bopd. A 2010 4D snapshot taken in early July 2010, 5 weeks after production started, identified the source of the well's rapidly increasing water-cut and identified further infill locations. Despite modest production for an offshore North Sea development the first Maule producer has been an economic success for Apache as a result of integrated subsurface technical work, drilling performance, small field allowance incentives and a sense of urgency.

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: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.379

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 head0.250
Teacher spread0.207 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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