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Record W2285838050 · doi:10.1002/mop.29670

Microwave heating of heavy oil reservoirs: A critical analysis

2016· article· en· W2285838050 on OpenAlexafffund
Daniel Oloumi, Karumudi Rambabu

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsAlberta Innovates - Technology Futures
KeywordsMicrowaveMultiphysicsOil sandsMicrowave heatingAsphaltPetroleum engineeringMaterials scienceThermalMicrowave powerElectromagnetic heatingOil shaleThermal conductivityEnvironmental scienceWaste managementGeotechnical engineeringComposite materialEngineeringElectrical engineeringFinite element method

Abstract

fetched live from OpenAlex

ABSTRACT In this article, microwave heating of the heavy oil reservoir, oil‐sand, is critically studied. The study is carried out based on full wave and multiphysics simulations that are performed at 2.45 GHz using both CST Microwave studio and COMSOL. It is demonstrated that most of the microwave power is deposited in bitumen rather in sand due to the dielectric properties of bitumen. Thermal analysis showed that most of the heat is generated in bitumen and is conducted to sand. Although microwave power is selectively deposited into bitumen of the oil‐sand, the temperature gradient between the bitumen and sand is not able to maintain due to high thermal conductivity of the oil‐sand medium. Microwave heating can play very important role to reduce the tailing ponds and protect the environment by minimizing water usage in the recovery process. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:809–813, 2016

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 score0.211
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.207
Teacher spread0.201 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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