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Record W4312705870 · doi:10.1115/omae2022-78264

A Generalized Model for Field Assessment of Particle Settling Velocity in Viscoelastic Fluids

2022· article· en· W4312705870 on OpenAlexaff
Hongbo Chen, Ergün Kuru

Bibliographic record

VenueVolume 10: Petroleum Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsViscoelasticityMechanicsSettlingHydraulicsDrilling fluidElasticity (physics)ViscosityViscometerMaterials scienceMechanical engineeringPhysicsEngineeringThermodynamicsDrilling

Abstract

fetched live from OpenAlex

Abstract It has been long known that drilling fluid viscoelastic properties have a significant impact on various elements of drilling hydraulics design (e.g., assessment of frictional pressure loss, particle settling velocity, hole cleaning, etc.). However, efforts for considering the viscoelastic fluid properties in drilling hydraulics design are traditionally hindered by the fact that there was no practical methodology to measure these critical fluid properties in the field. Previous studies have highlighted the advantages of using the concept of “energy dissipation” for the quantitative evaluation of fluid elasticity. Energy dissipation theory provides a more comprehensive description of the fluid elasticity by considering the two characteristics of the fluid simultaneously; stretchiness (oscillation strain % at the crossover point of G’ and G” curves) and the stress corresponding to crossover point (i.e., flow point). In a recent study, we have shown that the “energy dissipation” of viscoelastic fluids can be correlated to fluid physical properties such as apparent viscosity and funnel viscosity, which can be conveniently measured using standard field-testing equipment (i.e., Rotational Viscometer and Funnel viscometer). Based on these findings, a new methodology for the field assessment of drilling fluid viscoelasticity has been developed, opening new opportunities to develop improved hydraulics models. In this paper, using the new methodology developed in a recent study, we present a new generalized model for the field assessment of the particle settling velocity in viscoelastic fluids. The main objectives of the study were to: 1) Develop a generalized model for the field assessment of particle settling velocity in shear-thinning viscoelastic fluids by using the energy dissipation concept as an indicator of the fluid viscoelasticity; 2) Investigate the significant factors that influence the particle settling velocity to help provide a solution to the potential problem encountered in field drilling operations. We prepared ten different fluids, which were divided into two groups based on their shear viscosity values. In each group, five fluids were having similar shear viscosity and variable elasticity values. Nineteen different spherical particles were used to conduct particle settling experiments with a density range from 2700 kg/m3 to 6000kg/m3 and a diameter range from 1mm to 4mm. Rheological characterizations of the fluids have been conducted by using funnel viscometer, API Rotational viscometer, controlled shear rate, and amplitude sweep test measurements. Based on the experimental results and theory of the particle settling in non-Newtonian fluids, we developed a new model that can be used for predicting particle settling velocity in viscoelastic fluids. The statistical analyses had shown that the root means square error and mean absolute percentage error of model predictions were 0.0032 m/s and 4.1 percent, respectively.

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 score0.412
Threshold uncertainty score0.712

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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