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Record W1973117872 · doi:10.1190/geo2011-0282.1

Temperature dependence of the magnetic properties of reservoir rocks and minerals and implications for in situ borehole predictions of petrophysical parameters

2012· article· en· W1973117872 on OpenAlexaff
Arfan Ali, David K. Potter

Bibliographic record

VenueGeophysics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetrophysicsParamagnetismMagnetic susceptibilityRock magnetismFerrimagnetismDiamagnetismPermeability (electromagnetism)GeologyBoreholeMagnetizationMineralogyPermeameterMagnetic fieldCondensed matter physicsRemanencePorosityChemistrySoil scienceSoil waterGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

ABSTRACT Recent work has shown strong correlations between magnetic susceptibility and key petrophysical parameters such as clay content and fluid permeability. The magnetic measurements in previous work were mainly undertaken at ambient (room temperature) conditions on core samples. The present study involved theoretical modeling and experimentation on the temperature dependence of the magnetic properties (mass magnetization and magnetic susceptibility) of reservoir rocks and minerals over a range of low and high applied fields. It paves the way for correctly interpreting borehole magnetic susceptibility measurements, and accurately predicting petrophysical properties in situ, from a potentially new suite of low and high field borehole magnetic tools. The temperature dependent magnetic susceptibility measurements provide an improved means of quantifying the diamagnetic versus paramagnetic mineral content in reservoir rocks compared to a single ambient measurement. Paramagnetic clays, such as illite, are important in controlling the fluid permeability in many of these samples, and we again saw correlations between the magnetic measurements, paramagnetic clay content, and permeability. We also show how to derive ferrimagnetic mineral hysteresis curves by subtracting the high field magnetic data from the total signal. The resulting magnetic hysteresis curves give important information concerning the content (often only a few ppm, which X-ray diffraction cannot detect), mineralogy, and domain state of the ferrimagnetic particles.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.249

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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