Sensitive Carbonate Reservoir Rock Characterization From Magnetic Hysteresis Curves and Correlation with Petrophysical Properties
Bibliographic record
Abstract
Recent work has shown how magnetic susceptibility and hysteresis measurements correlate with several petrophysical parameters in clastic reservoir samples. The present paper applies these techniques to carbonate samples. Carbonate rock typing can be achieved from high field magnetic susceptibility, which indicates a sample’s diamagnetic plus paramagnetic mineral content. High field measurements are very sensitive and can quantify small differences in paramagnetic clay content that X-ray diffraction (XRD) or scanning electron microscopy (SEM) cannot. Temperature dependent hysteresis measurements can also identify and quantify small concentrations of paramagnetic minerals. Experimental magnetic hysteresis curves demonstrated subtle differences between samples in a suite of Middle East carbonates. Significantly, the high field magnetic susceptibility values from the hysteresis curves exhibited extremely good correlations with permeability (small variations in paramagnetic clay content seem responsible for this) and porosity. The low field magnetic susceptibility values, however, did not correlate well with these petrophysical parameters merely because some samples contained small concentrations of ferrimagnetic impurities that contributed to the low field signal. The low field part of a hysteresis curve provides a further sensitive means of characterizing carbonate samples, and can be used to quantify these extremely small concentrations of ferrimagnetic material (down to a few parts per million) that XRD cannot. Magnetic susceptibility values (both low and high field) for some US and North Sea carbonates were generally higher than the Middle East samples, indicating increased ferrimagnetic and paramagnetic (mainly clays) content. This suggested that the reservoir quality of the Middle East carbonates studied was generally better.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".