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Record W1972015106 · doi:10.5539/mas.v5n3p15

Theory and Application of Buoyancy in Wells

2011· article· en· W1972015106 on OpenAlexvenueno aff
Eirik Kaarstad, Bernt S. Aadnøy

Bibliographic record

VenueModern Applied Science · 2011
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsBuoyancyDrill stringDragWellheadMechanicsPiston (optics)TorqueDrill pipeHamilton's principleDrillEngineeringPhysicsClassical mechanicsMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

The paper presents a generalized buoyancy model for application in wells, called the generalized Archimedes’ principle. There exists confusion in the oil industry about buoyancy effects as related to the force-area method (also known as piston force method) or the Archimedes’ principle, the stability force in buckling and axial forces during well intervention operations. This paper will present a general model called the generalized Archimedes’ principle, which is valid for all cases. The paper presents examples of application to ordinary drilling operations, deviated wells and different fluid densities inside and outside the pipe. For well intervention scenarios, the effects of wellhead shut-in pressures on buoyancy are demonstrated. This simple model, not requiring use of a “stability force”, can also analyze buckling. Two field cases are presented to demonstrate the use of the generalized Archimedes’ principle in torque and drag analysis. When the drill string is run in hole having a float valve installed, the buoyancy factor changes depending on how much air is in the drill string. The generalized Archimedes’ principle may simply be applied to the torque and drag model for accurate lowering force calculations. Using the generalized Archimedes’ principle, a correct assessment of the real axial load of the pipe is obtained, a critical factor for tubular work in petroleum wells. The model is also significant as it is simple and can be applied to all well operations.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.224

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 head0.007
GPT teacher head0.180
Teacher spread0.173 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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