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Record W2805330350 · doi:10.3968/10177

Mechanical Analysis of Drill String Drag and Torque With the Condition of Irregular Borehole

2017· article· en· W2805330350 on OpenAlexvenueno aff
Meng Cai, Wang Lu, Peng Wang, Meng Li

Bibliographic record

VenueAdvances in petroleum exploration and development · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDrill stringDrillingBoreholeDrillDragDrilling engineeringDrilling fluidMeasurement while drillingEngineeringTorquePetroleum engineeringStress (linguistics)GeologyStructural engineeringMechanical engineeringGeotechnical engineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

With the rapid development of drilling technology, air drilling technology and large displacement well, and horizontal well drilling technology are also known as the main directions of the future drilling development. Compared with conventional drilling mode, air drilling is particular, such as drilling fluid with low density, poor lubrication, large friction coefficient between the drill string and borehole, irregular borehole, well trajectory control difficulty, and so on. Along with the change of drilling condition, the stress of drill string is also changed. However, irregular borehole has a direct impact on the fatigue failure and the stress of drill string. Therefore, it is necessary to analyze drill string stress and influence factors in irregular hole. Based on the previous studies, application of drill string mechanics and statistical regression methods is used to study drill string drag and torque, and it can predict the drilling string stress state with the hole enlargement rate, meanwhile, it also provides a theoretical basis for gas drilling and complex drilling technology.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.230

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 head0.009
GPT teacher head0.226
Teacher spread0.218 · 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
Published2017
Admission routes1
Has abstractyes

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