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Record W2611529697 · doi:10.1080/01430750.2017.1324812

Flow behaviours comparison of crude oil–polymer emulsions

2017· article· en· W2611529697 on OpenAlexaff
Mamdouh T. Ghannam, Basim Abu‐Jdayil, Nabil Esmail

Bibliographic record

VenueInternational Journal of Ambient Energy · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsCrude oilPetroleum engineeringFlow (mathematics)Environmental sciencePolymerMaterials scienceMechanicsEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The flow characteristics of crude oil–polymer (COPE) emulsions were investigated in terms of viscosity and shear stress. Two commonly used polymers in the enhanced oil recovery were employed. These two different polymers are Alcoflood and Xanthan gum. Rheostress RS100 was used in this study for measuring and analysing the experimental measurements. A cone and plate sensor of RS100 rheometer was utilised for this investigation. The experimental measurements of viscosity and shear stress of different COPE were examined over the shear rate range of 0.1–1000 s−1. The polymer concentration range of 500–104 ppm was examined and two crude oil concentrations of 25% and 75% by volume were tested. A detailed investigation of the flow behaviour of COPE in the presence of two different polymers was completed. The flow behaviour of all COPE exhibit non-Newtonian shear thinning behaviour that can be presented by the power-law model for crude oil-AF1235, however the other types of COPE can be predicted by the Casson model. For a low polymer concentration of 500 ppm, this investigation showed that the flow behaviour of Xanthan emulsions is slightly higher than the Alcoflood emulsions till a shear rate of 100 s−1. For the higher polymer concentrations, both polymer emulsions exhibited more or less similar flow behaviours till the beginning of the shear thickening for the AF1285 emulsions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.422

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 science0.0010.000
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 head0.015
GPT teacher head0.302
Teacher spread0.287 · 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 designBench or experimental
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

Citations1
Published2017
Admission routes1
Has abstractyes

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