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Record W2915757916 · doi:10.2118/0805-0050-jpt

Overview: Offshore Completions (August 2005)

2005· article· en· W2915757916 on OpenAlexaff
J.C. Cunha

Bibliographic record

VenueJournal of Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSubseaSubmarine pipelineCompletion (oil and gas wells)Petroleum engineeringFossil fuelOffshore oil and gasEngineeringEnvironmental scienceGeologyMarine engineeringOceanographyWaste management

Abstract

fetched live from OpenAlex

Last year, in this space, I commented on innovations being implemented on recent offshore completion projects and how offshore completion practices have undergone major changes. Our industry, regarding offshore developments, apparently has an unlimited ability to amaze the world with new advances. Projects that were considered not feasible 5 years ago now are being implemented. Driven by various reasons, including depletion of conventional reserves and increasing oil prices, oil and gas production in deep water became customary in many regions around the world, and it was not a surprise that, during the 2005 Offshore Technology Conference, oil companies started to reveal plans aiming at production of fields under 3000 m of water. Use of intelligent-well completions is spreading globally, not only for large deepwater projects, but also for small offshore discoveries where its use in subsea satellite wells may bring profitability to an otherwise uneconomical project. Smart systems, with remotely operated valves, allow operators to control various producing zones, shutting down those not responding as expected without expensive well interventions. All of that can be achieved while data for each independent zone are simultaneously acquired and recorded. Developing fields that present complex geology and deep reservoirs that have high pressures and temperatures still present challenges for the industry, mainly when unconventional-trajectory wells are used. Some of the issues being investigated involve completion of long horizontal sections in unconsolidated reservoirs; flow assurance, mainly for deepwater conditions; and subsea processing, including gas compression and gas/liquid and oil/water separation. Some of these challenges are addressed well in the papers featured in this issue. Available from the SPE eLibrary: www.spe.org SPE 90552 - “Gravel Packing Deepwater Long Horizontal Wells Under Low Fracture Gradient,” by Chen, Zhongming, SPE, BJ Services, et al. Available from the OTC Library: www.otcnet.org OTC 17399 - “Subsea Gas Compression—Challenges and Solutions,” by Fantoft, R., FMC Kongsberg Subsea A/S OTC 17397 - “Hybrid Riser Towers From an Operator’s Perspective,” by Sworn, A., BP plc

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 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
Published2005
Admission routes1
Has abstractyes

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