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Record W2164820215 · doi:10.1115/omae2014-24083

A Comparative Study of Hole Cleaning Performance — Water Versus Drag Reducing Fluid

2014· article· en· W2164820215 on OpenAlexaff
Fabio Ernesto Rodriguez Corredor, Majid Bizhani, Ergün Kuru

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDragMechanicsFluid dynamicsMaterials sciencePetroleum engineeringGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Effective hole cleaning in horizontal and extended reach wells (ERD) often requires use of high circulation rates, which may not be always achievable due to the risk of circulating bottom hole pressure reaching the fracture limit of the rock. Achieving good hole cleaning while keeping the circulating bottom hole pressure within the safe operational window is very often the major engineering challenge. A drag reducing fluid with good hole cleaning ability could be a potential solution in this case. In order to see if it is possible to use a drag reducing fluid and still achieve a good hole cleaning, an experimental program was designed and conducted. The main objective of this experimental study was to compare the hole cleaning performances of water and a drag reducing fluid. The hole cleaning experiments were conducted using a 9m long horizontal flow loop with concentric annular geometry (Outer Pipe ID = 95 mm, Inner Pipe OD = 38 mm, ID/OD ratio = 0.4). The drag reducing additive was a commercially available partially hydrolyzed polyacrylamide (PHPA). Water and two drag reducing fluids with 0.07% V/V and 0.1% V/V PHPA concentrations were used. Critical velocities for the initiation of cuttings movement with rolling, saltation/dunes, and suspension modes were determined and compared when using water and drag reducing fluids as a carrier fluid. Critical velocities for the initiation of cuttings movement were found to be lower with water than that of drag reducing fluid in all transport modes.

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 score0.113
Threshold uncertainty score0.389

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 head0.017
GPT teacher head0.218
Teacher spread0.202 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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