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Record W3011347783 · doi:10.2118/199910-ms

Feasibility of Electromagnetic Heating for Oil Sand Reservoirs

2020· article· en· W3011347783 on OpenAlexaff
Dongqi Ji, Thomas G. Harding, Zhangxin Chen, Mingzhe Dong, Hui Liu, Zhiping Li, Fengpeng Lai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPetroleum engineeringSteam injectionElectromagnetic heatingElectric heatingElectromagnetic fieldEnvironmental scienceNuclear engineeringElectromagnetic radiationThermalProcess (computing)Process engineeringMechanical engineeringEngineeringElectrical engineeringComputer scienceOpticsPhysicsMeteorology

Abstract

fetched live from OpenAlex

Abstract For methods of thermal heavy oil recovery, an alternative approach of applying electrical energy, such as electromagnetic heating, can be used to generate heat in reservoirs that are not suitable for steam injection or to improve the economics and reduce environmental impact of the heavy oil recovery compared to using steam injection. While much progress in the development of electromagnetic heating technology has been made in recent years, the ability to accurately and effectively mathematically model the application of an electromagnetic heating process to a reservoir has been limited. In this paper, based on our reliable and efficient electromagnetic heating simulator, the effects of operational parameters on electromagnetic heating performance are investigated including evaluation of antenna location, well constraints, and applied power and frequency. The feasibility of electromagnetic heating in oil sands reservoirs has been examined for two cases: a) a single horizontal well containing a heating source and b) a horizontal well-pair with heating sources located in both wells.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score1.000

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)0.0010.000

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 head0.051
GPT teacher head0.279
Teacher spread0.228 · 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.

Study designOther design
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
Published2020
Admission routes1
Has abstractyes

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