Raman Spectroscopy Integrated with Machine Learning as a Tool for Maturity Assessment of Organic Matter: A Case Study in Santos Basin, Brazil
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The thermal maturation assessment of organic matter (OM) is crucial for understanding the hydrocarbon generation and expulsion processes in oil-bearing rocks. Vitrinite, a maceral found in coal samples, has its reflectance (%Ro) measurement widely used as a paleotemperature indicator in organic-rich sedimentary rocks. However, alternative methods are necessary to determine paleotemperature in sedimentary basins where vitrinite is scarce or absent, such as in presalt formations. This work used Raman spectroscopy to determine the reflectance equivalent in OM present in carbonate rocks from a presalt petroleum system (Santos Basin, Brazil). Vitrinite fragments present in palynofacies sections from a well of the Potiguar Basin, Rio Grande do Norte, Brazil, collected in different depths, with %Ro obtained in the range from 0.46 to 2.72%, were used as reference material to model calibration using their Raman spectral parameters, assisted by the Machine Learning Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. The strategy was employed to analyze 30 samples of presalt carbonate rocks containing solid bitumen, whose Raman spectral parameters were used to determine their equivalent reflectance values (% R Raman ). Calibration with vitrinite samples was done using excitation radiation with wavelengths at 532 and 632.8 nm. The % R Raman values were determined for all rock-containing bitumen samples using the exciting radiations. The methodology has proven to be an effective way to determine the OM thermal maturity.
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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".