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Record W2896004418 · doi:10.2118/187255-pa

Settling Velocity of Particles in Viscoelastic Fluids: A Comparison of the Shear-Viscosity and Elasticity Effects

2018· article· en· W2896004418 on OpenAlexafffund
Sumanth Kumar Arnipally, Ergün Kuru

Bibliographic record

VenueSPE Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsSettlingViscoelasticityRheologyShear thinningElasticity (physics)Materials scienceMechanicsShear (geology)ViscosityThermodynamicsChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

Summary The objective of this paper is to determine how fluid shear viscosity and elasticity might influence the particle-settling velocity, and even more so to answer the question of which one of these two rheological properties is more dominant in controlling the particle-settling velocity when viscoelastic drilling fluids are used. The settling velocities of spherical particles (diameters: 1.18, 1.5, 2, and 3 mm) in partially hydrolyzed polyacrylamide (HPAM) polymer fluids were measured using the particle-image-shadow graph (PIS) technique. Two sets of test fluids were formulated by mixing three different grades of HPAM (molecular weights of 500,000, 8 million, and 20 million g/g mol) at polymer concentrations of 0.09, 0.05, and 0.03 wt%. The shear-viscosity and elasticity characteristics of test fluids were determined by performing shear-viscosity and frequency-sweep oscillatory measurements, respectively. The first set of fluids had almost identical shear-viscosity characteristics while showing significantly different elastic properties (quantified in terms of relaxation time). The second set of fluids had similar elastic properties but different shear-viscosity characteristics. In addition, the effect of the particle size on the settling velocities in these test fluids was also investigated. The experimentally measured settling velocities were compared with the values calculated from the Shah et al. (2007) model developed for predicting the settling velocity of spherical particles in power-law (viscoinelastic) fluids as well as the values calculated from the Malhotra and Sharma (2012) correlation developed for settling velocity in shear-thinning viscoelastic fluids in unconfined media. Experimental results showed the following: When the fluids with similar shear-viscosity profiles were used, the settling velocity of spherical particles decreased significantly with the increasing fluid elasticity. The settling-velocity values can be 14 to 50 times overestimated if the effect of the elasticity is not considered. At constant elasticity, the settling velocity of spherical particles also decreased significantly when the fluid shear viscosity was increased. The spherical particle-settling velocity increased pronouncedly as particle diameter increased from 1.18 to 3 mm. However, the magnitude of the increase in settling velocity with the increasing particle diameter is less for the samples with higher elasticity and similar shear-viscosity characteristics. The fluid shear viscosity and the elasticity both seem to have significant effect on the particle-settling velocity. However, from the field operational point of view, fluids with high shear-viscosity values are not always practical to use because the high shear viscosity increases parasitic pressure losses and potentially has a negative effect on the drilling rate. Hence, in such cases increasing the fluid elasticity can help to reduce the particle-settling velocity even at lower shear-viscosity values. By conducting experiments under controlled conditions, we were able to quantify the individual effects of fluid shear viscosity and elasticity on the particle-settling velocity for the first time in drilling literature.

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 score0.047
Threshold uncertainty score0.263

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 head0.008
GPT teacher head0.226
Teacher spread0.219 · 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

Citations55
Published2018
Admission routes2
Has abstractyes

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