Temperature dependence of the magnetic properties of reservoir rocks and minerals and implications for in situ borehole predictions of petrophysical parameters
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".