Inferring total organic carbon and major element geochemical and mineralogical characteristics of shale core from hyperspectral imagery
Bibliographic record
Abstract
ABSTRACT Hyperspectral systems that image drill core can capture detail mineralogical information at the millimeter scale and thus have the potential to enable investigators to characterize shale composition and heterogeneity, complementing the direct chemical and x-ray diffraction analysis of core samples and guiding detailed sampling. This method provides insight into petrophysical and geomechanical properties because these properties are significantly correlated to rock composition. We tested this approach on a continuous long core from the shale sequence of the Horn River Group in the Horn River Basin, British Columbia, sampled at a spacing of 1 m (40 in.) and analyzed for geochemical composition. These data enable the calibration of spectral imagery to rock composition and specifically predict total organic carbon (TOC) and the abundance of SiO2, Al2O3, K2O, and CaO. We then imaged nine samples from the Woodford Shale from the Permian Basin, Texas, for a blind test to assess the predictive models. The models were then used to predict TOC and geochemical data over detailed imagery of 300 m (984 ft) of Horn River Group shale core and portray their spatial variability downhole as images and profiles. In its simplest form, hyperspectral imagery can be enhanced to highlight fabric in shale core that otherwise is difficult to visualize because of low brightness. In addition, we show that spectral imagery of shale can also be processed to either convey mineralogical (quartz, clay, and carbonate) or geochemical information. The resulting views can readily be used to guide the selection of samples and may provide tools for scaling reservoir properties from individual plugs to reservoir volumes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".