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Record W2218439143 · doi:10.3968/7742

Experimental Study on the Down-Speed of Conductor Pipe Influenced by Jetting Displacement in Deepwater Drilling

2015· article· en· W2218439143 on OpenAlexvenueno aff
Hongkun Zhang, Yanli Guo, Zhi Hai Fan, Mingzhao Huang, Lingxiao Wang, Jiahui Chen, Baojiang Sun

Bibliographic record

VenueAdvances in petroleum exploration and development · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsBoreholeDisplacement (psychology)DrillingDeepwater drillingConductorPetroleum engineeringEngineeringMeasurement while drillingMechanical engineeringDrilling fluidMarine engineeringWell drillingDrilling engineeringProcess (computing)Jet (fluid)Geotechnical engineeringMaterials scienceComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Based on the theory of jet drilling technology and displacement optimization, a set of experimental equipment about jet drilling is devised. The laws of conductor pipe down-speed influenced by pump displacement were studied by laboratory experiments. According to the experimental results and analysis, the following conclusions can be drawn. The down-speed of conductor pipe increases with the increasing of displacement, also the drilling speed is boosted. But the unstableness of borehole wall is augmented as well. And this will result in the increasing of waiting time for borehole formation. In the process of conductor pipe jetting, the conductor pipe down-speed and the waiting time of soil returning to a certain bearing capacity should be considered together in order to shorten the entirety drilling time. The research can provide certain references for expensive offshore operation and have important significance to improve the economic benefits of deepwater drilling.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.257
Teacher spread0.235 · 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 designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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