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Direct Measurement of Pore Size and Surface Relaxivity with Magnetic Resonance at Variable Temperature

2023· article· en· W4383557945 on OpenAlexafffund
Peiyuan Yan, Florea Marica, Jiangfeng Guo, Bruce J. Balcom

Bibliographic record

VenuePhysical Review Applied · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRelaxometryRelaxation (psychology)DiffusionPorosityMaterials scienceNuclear magnetic resonancePorous mediumMineralogyThermodynamicsChemistryMagnetic resonance imagingSpin echoPhysicsComposite material

Abstract

fetched live from OpenAlex

A variety of magnetic resonance relaxometry methods have been developed to determine pore size in porous rocks. Uncertainties in pore-size estimates may occur due to uncertainty in the relaxation-diffusion regime. We have developed a direct and rapid one-dimensional method based on Brownstein-Tarr theory to help remedy this problem. The correlation between magnetic resonance relaxation behavior and the temperature-dependent self-diffusion coefficient of pore fluids was employed to estimate the pore size and surface relaxivity of a series of reservoir rocks. Relaxation is anticipated to depend on diffusion in the intermediate regime of Brownstein-Tarr theory. Water-saturated glass-bead packs were employed in initial Carr-Purcell-Meiboom-Gill experiments at variable temperature. The calculated pore size matches the estimated geometric pore size. The proposed method was applied to determine the pore size of Berea, Buff Berea, and Nugget sandstones. The pore sizes determined with the three pore geometries are in good agreement with scanning electron microscopy and micro-computed-tomography measurements. The experimentally observed changes in relaxation times and their corresponding intensities indicate an intermediate Brownstein-Tarr regime for all systems examined in this work.

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 score0.322
Threshold uncertainty score0.485

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 head0.010
GPT teacher head0.282
Teacher spread0.272 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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