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Record W1998592782 · doi:10.2523/iptc-16722-ms

The Liwan Gas Project: A Case Study of South China Sea Deepwater Drilling Campaign

2013· article· en· W1998592782 on OpenAlexaff
David Kenneth Triolo, Tracy Mosness, Rana Khalid Habib

Bibliographic record

VenueInternational Petroleum Technology Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsHusky Energy (Canada)
FundersChina National Offshore Oil CorporationDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsCasingDrillingPetroleum engineeringGeologyDeep waterMining engineeringEngineeringOceanographyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract In September 2006, Husky Energy discovered the Liwan gas reservoir in its South China Sea Block 29/26. A 6th generation, deep water semi-submersible was contracted and subsequently drilled 26 deep water wells (700 - 1600 m water depth) consisting of 10 exploration, 10 appraisal and 6 development wells, tested 6 wells between November 2008 and November 2011. The exploration program discovered several additional sandstone, gas-bearing reservoirs two of which will also be commercialized. During the 1,011 days of drilling and testing activity, the rig and supporting onshore teams did not incur a lost time accident. The rig team drilled 61,593 m of hole below the mudline and ran the associated strings of casing. Non-productive time consumed 265 days (26.2%) consisting of 65 days waiting on weather, 147 days of rig repair, and 53 days of other unscheduled events.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.267
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations5
Published2013
Admission routes1
Has abstractyes

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