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Record W1521536557 · doi:10.3968/6810

The Structure Parameters Optimization of Variable Direction Joint During Extracting Casing in High-Inclination Directional Well

2015· article· en· W1521536557 on OpenAlexvenueno aff
Chi Ai, Yu Fahao, Fuping Feng, Lü Deqing, Zhenning Yu

Bibliographic record

VenueAdvances in petroleum exploration and development · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCasingStructural engineeringJoint (building)CurvatureEngineeringBent molecular geometryBoreholeFinite element methodGeotechnical engineeringMechanical engineeringGeometryMathematics

Abstract

fetched live from OpenAlex

During extracting casing in high-inclination directional well, the existence of variable direction joint (VDJ) can enhance the build-up ability of washover string. And it can increase the compatibility between washover head and casing to some extent. However, too large or too small bending angle of VDJ will result in that the washover head cuts the upper and bottom parts of casing. Therefore, it is necessary to design reasonable bending angles for casing damage wells with different borehole curvature. In this paper, through the geometrical and mechanical analysis on the washover string with VDJ, a build-up rate calculation model and an axial force calculation model are established respectively. Moreover, through establishing the finite element model of washover tubing-ball-casing combination, the contact stress distribution is obtained. Under the critical condition of casing breaking when the contact stress exceeds the yield stress, the reasonable bending angle of VDJ matching with different casing damage well is designed. We can conclude that the research results play an important significance for in assuring the safe extracting casing. Key words: Variable direction joint; Safe extracting; Parameters optimization; Casing damage well

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 score0.712
Threshold uncertainty score0.394

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 communication0.0000.001
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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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
Published2015
Admission routes1
Has abstractyes

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