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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 OpenAlex
David Kenneth Triolo, Tracy Mosness, Rana Khalid Habib

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.040
Threshold uncertainty score0.509

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 head0.017
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