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Record W2318003676 · doi:10.1021/ef1009428

Effects of Electrical and Radio-Frequency Electromagnetic Heating on the Mass-Transfer Process during Miscible Injection for Heavy-Oil Recovery

2010· article· en· W2318003676 on OpenAlexaff
Л. А. Ковалева, A. Ya. Davletbaev, Tayfun Babadagli, Z.Yu. Stepanova

Bibliographic record

VenueEnergy & Fuels · 2010
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphalteneViscosityRadio frequencyPorous mediumResidual oilElectric fieldEnhanced oil recoveryMass transferDielectric heatingPrecipitationElectromagnetic fieldMaterials scienceHeat transferPorosityPetroleum engineeringAnalytical Chemistry (journal)MechanicsChemistryComposite materialGeologyChromatographyDielectricMeteorologyOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

This paper deals with the effect of radio-frequency electromagnetic (RF-EM) fields and electrical heating on the mass- and heat-transfer processes in a multi-component hydrocarbon system flowing in porous media. The more specific objective was to determine the major differences between the RF-EM effects and electric heating and eventually to propose the application conditions toward their field-scale applications. Critical parameters, including the viscosity reduction, that affect the recovery of heavy oil under the influence of these heating options with the emphasis on resolving the asphaltene precipitation problem were clarified. It was observed that the EM field influence on the residual oil recovery factor was more critical, and a greater recovery was obtained from the RF-EM case. This was attributed to the fact that the RF-EM field influences polar components of the oil, desorpting these components from the surface of the rock and adding to the production, as indicated by the scanning atomic force microscopy images. This critical role of the RF field on the adsorptive process during the displacement of high-viscosity oils eventually resulted in less asphaltene precipitation and pore plugging.

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 score0.010
Threshold uncertainty score0.437

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 head0.004
GPT teacher head0.192
Teacher spread0.188 · 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

Citations50
Published2010
Admission routes1
Has abstractyes

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