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Record W2043374707 · doi:10.1080/10916460500527021

Plasma Heating of Carbonate Formations

2007· article· en· W2043374707 on OpenAlexafffundabout
Muftah H. El‐Naas, R. J. Munz, Joanna Rossi, Abdulrazag Y. Zekri

Bibliographic record

VenuePetroleum Science and Technology · 2007
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPorosityCalcium carbonateCarbonateScanning electron microscopePermeability (electromagnetism)MineralogyCalcinationPlasmaArgonCarbonate rockMaterials scienceChemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract Calcium carbonate rock samples were exposed to high temperature argon plasma to investigate the efficiency of plasma heating for fracturing carbonate formations. Plasma temperature can change the basic properties of the rock through fracture and calcination, which in turn can result in a significant increase in its porosity and permeability. Several calcium carbonate cores with a diameter of 25.4 mm and a height of 29 mm were subjected to axial plasma heating at different temperatures and for different periods of time. The experimental results indicated that the top surface temperatures of the samples ranged from 800°C to 900°C and the temperature reached steady state within 4 min. The scanning electron microscope (SEM) and porosity analysis of the treated samples indicated significant changes in the basic structure of the rocks and a substantial increase in both porosity and permeability. A mathematical model was developed to predict the temperature distribution along the axis of the heated sample and to estimate the time needed to achieve steady state. The model predictions were in good agreement with experimental results. Keywords: carbonate formationsfracturingmodelingplasma heatingstimulation ACKNOWLEDGMENT The financial support of the Natural Science and Engineering Research Council of Canada is gratefully acknowledged.

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)0.001
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 score0.099
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.243
Teacher spread0.236 · 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
Published2007
Admission routes3
Has abstractyes

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