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Record W3201291835 · doi:10.2118/205953-ms

A Generalized Model for the Field Assessment of Drilling Fluid Viscoelasticity

2021· article· en· W3201291835 on OpenAlexaff
Temi Okesanya, Ergün Kuru, Heath Garett, Dylan Hadley

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2021
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsViscoelasticityRheometerDrilling fluidRheologyViscometerMaterials scienceXanthan gumElasticity (physics)MechanicsViscosityDrillingComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Recent studies highlight the significant role of drilling fluid elasticity in particle suspension and hole cleaning during drilling operations. Traditional methods to quantify fluid elasticity require the use of advanced rheometers not suitable for field application. The main objectives of the study were to develop a generalized model for determining viscoelasticity of a drilling fluid using standard field-testing equipment, investigate the factors influencing drilling fluid viscoelasticity in the field, and provide an understanding of the viscoelasticity concept. Over 80 fluid formulations used in this study included field samples of oil-based drilling fluids as well as laboratory samples formulated with bentonite and other polymers such as partially-hydrolyzed polyacrylamide, synthesized xanthan gum, and polyacrylic acid. Detailed rheological characterizations of these fluids used a funnel viscometer and a rotational viscometer. Elastic properties of the drilling fluids (quantified in terms of the energy required to cause an irreversible deformation in the fluid's structure) were obtained from oscillatory tests conducted using a cone-and-plate type rheometer. Using an empirical approach, a non-iterative model for quantifying elasticity correlated test results from a funnel viscometer and a rotational viscometer. The generalized model was able to predict the elasticity of drilling fluids with a mean absolute error of 5.75%. In addition, the model offers practical versatility by requiring only standard drilling fluid testing equipment to predict viscoelasticity. Experimental results showed that non-aqueous fluid (NAF) viscoelasticity is inversely proportional to the oil-water ratio and the presence of clay greatly debilitates the elasticity of the samples while enhancing their viscosity. The work efforts present a model for estimating drilling fluid elasticity using standard drilling fluid field-testing equipment. Furthermore, a revised approach helps to describe the viscoelastic property of a fluid that involves quantifying the amount of energy required to irreversibly deform a unit volume of viscoelastic fluid. The methodology, combined with the explanation of the viscoelasticity concept, provides a practical tool for optimizing drilling operations based on the viscoelasticity of drilling fluids.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.279
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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