Determination of Total Organic Carbon Content in Shale Formations With Regression Analysis
Bibliographic record
Abstract
By correcting both the positive and negative ΔlogR separation resulting from the resistivity in organic-deficient shales, the traditional ΔlogR correlation is modified, validated, and applied to determine the total organic carbon (TOC) content in shale formations. The TOC content is determined once the Fisher distribution, which represents the significance of each model, and Student's t-distribution, which denotes the significance of every variable in the models, have achieved values equal to or higher than their respective threshold values at a confidence level of 95%. Using a total of 45 sets of logging measurements, the newly proposed correlation is found to be able to reproduce the measured TOC values with a root mean-squared absolute difference (RMSAD) of 0.30 wt % and root mean-squared relative difference (RMSRD) of 23.8%, respectively. Uranium concentration, apart from interval transit time and resistivity, is found to be key in determining the TOC content in organic-rich shale without other radioactive minerals. By combining the reading of DGR (i.e., the difference between the spectral gamma ray with the radioactivity and the computed gamma ray without uranium), the traditional ΔlogR technique has now been improved and extended to the negative ΔlogR separation resulting from the resistivity in organic-deficient shale higher than that in organic-rich shale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 0.001 |
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".