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Record W2747893769 · doi:10.1071/aj13048

The Coniston development: another offshore challenge in Western Australia

2014· article· en· W2747893769 on OpenAlexaff
Gianluca Di Martino, Roumen Dimitrov Sankoff, Craig Marshall, Bobby Chopra

Bibliographic record

VenueThe APPEA Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsSubmarine pipelineDrillingCompletion (oil and gas wells)Petroleum engineeringEngineeringProduction (economics)Oil productionFossil fuelNew horizonsMarine engineeringCivil engineeringWaste managementMechanical engineering

Abstract

fetched live from OpenAlex

This extended abstract discusses the key challenges associated with the Coniston development; particular emphasis is on engineering, operations and project management aspects. The Coniston development will produce oil and gas from the Coniston and Novara hydrocarbon accumulations, located in permit WA-35-L, about 100 km north of Exmouth, in water depths of about 400 m. The Coniston development will consist of a sub-sea tieback to the existing Van Gogh sub-sea infrastructure and the Ningaloo Vision FPSO, currently producing from the Van Gogh Field. The project was sanctioned by Apache in 2011 and will be on production in 2Q 2014. To maximise reservoir exposure, multilateral wells will be drilled, and completed, employing inflow control devices of latest generation and monitoring production with the installation of tracers. To take advantage of project synergies, gas lift will be provided by Van Gogh wells through a dedicated gas production manifold. The Coniston development represents a remarkable multidisciplinary effort to develop a relatively small-size oil reservoir offshore WA. Some of the challenges overcome are the high oil viscosity, the complexity of the engineering to install new sub-sea infrastructure while minimising the impact on Van Gogh production and maximising the synergies of the tie back, the constant increase in drilling and facilities costs while maintaining attractive project economics, and the more stringent regulations environmental permits and the ability to optimise drilling and operation to achieve production as quickly as possible.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.284
Teacher spread0.247 · 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
Published2014
Admission routes1
Has abstractyes

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