Templates for Quantifying Clay Type and Clay Content from Magnetic Susceptibility and Standard Borehole Geophysical Measurements
Bibliographic record
Abstract
This paper proposes a rapid means of identifying clay type and quantifying clay content from new template crossplots that compare magnetic susceptibility measurements with standard borehole well log data. The templates are similar in format to standard industry charts, but have a number of advantages over the commonly used charts. Laboratory measurements of magnetic susceptibility on core samples and drill cuttings have recently shown strong correlations with key petrophysical parameters, particularly clay content and fluid permeability [1] [2]. A new template crossplot between magnetic susceptibility and borehole spectral gamma ray log data can firstly help to quickly identify the types of clay present in the formation. Additional new template crossplots between magnetic susceptibility and borehole bulk density data allow the mineral contents and porosities of binary mixtures of clay minerals and matrix minerals (such as illite clay + quartz) to be rapidly quantified. The templates can use ambient (room temperature) magnetic susceptibility data from measurements on core samples or drill cuttings in the laboratory or at the wellsite. Furthermore, the paper shows how the templates can potentially be extended to utilize borehole magnetic susceptibility data for in situ estimations of the type and content of clay. This requires accounting for the temperature dependence of the magnetic susceptibility of paramagnetic minerals (such as illite clay), which varies with depth in a borehole. Whilst borehole magnetic susceptibility measurements are rarely part of standard well logging operations, they could be a potentially useful tool for in situ clay type and content quantification, which in turn can help predict fluid permeability.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".