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Record W2802848994 · doi:10.11575/prism/31869

Modelling and Analysis of the Axial Vibration on BHA of Horizontal Drilling Rig

2018· dissertation· en· W2802848994 on OpenAlexfundno aff
Jingxuan Xu

Bibliographic record

VenuePRISM (University of Calgary) · 2018
Typedissertation
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersYangtze UniversityUniversity of Calgary
KeywordsVibrationDrillingDirectional drillingEngineeringMechanical engineeringGeologyDrilling rigStructural engineeringMarine engineeringPetroleum engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

Horizontal drilling technology is a widely applied well drilling technology due to its capability of reducing drilling time and cost, and at same time increasing the production. In practice, oil and gas production companies employ rotary steerable system or positive displacement motor to steer the drill bit to follow a pre-planned well trajectory along a desired direction. Therefore, the efficiency and accuracy are the main concerns for the improvement of horizontal drilling technology and drill string vibration is the factor to influence these two major concerns. This thesis illustrates a dynamic mathematical model for the vibration of the bottom hole assembly (BHA) which is an important part of drill string. The mathematical model considers the friction between the wellbore and the BHA and the effect of drilling fluid. It investigates the major influencing factors on the vibration, such as weight on bit, friction coefficient, viscous damping coefficient, and the number of stabilizers. The dynamic mathematical model is validated by the finite element simulation and analysis. Moreover, it can monitor the deformation of BHA real time and help drillers adjusting the parameters when the well path deviates from the original planned trajectory.

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: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.623

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 head0.006
GPT teacher head0.165
Teacher spread0.159 · 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
Published2018
Admission routes1
Has abstractyes

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