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Record W2048609886 · doi:10.1080/10916460701287607

A Critical Review of Electromagnetic Heating for Enhanced Oil Recovery

2008· review· en· W2048609886 on OpenAlexaff
A. Chhetri, M. R. Islam

Bibliographic record

VenuePetroleum Science and Technology · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSteam injectionPetroleum engineeringEnvironmental scienceWater floodingFlooding (psychology)Enhanced oil recoveryMaterials scienceWaste managementGeologyEngineering

Abstract

fetched live from OpenAlex

A variety of enhanced oil recovery methods have been developed and applied to mature and depleted reservoirs in order to improve the efficiency of primary (pressure deleted) and secondary recovery (water flooding) methods. In thermal recovery methods, cyclic steam and hot water injection, steam and hot water flooding, in situ combustion, electrical heating, and electromagnetic heating are used. However, each of these methods is highly energy intensive. Electromagnetic enhanced oil recovery method is a relatively new method and has been of great interest in the recent years. This article presents a critical review of electromagnetic heating of reservoirs for enhanced oil recovery.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.011
GPT teacher head0.299
Teacher spread0.288 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations68
Published2008
Admission routes1
Has abstractyes

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