Multidisciplinary Thermal Maturity Studies Using Vitrinite Reflectance and Fluid Inclusion Microthermometry: A New Calibration of Old Techniques
Bibliographic record
Abstract
Abstract A critical component of petroleum exploration risk assessment involves quantifying the risks associated with the presence of a viable hydrocarbon system. This requires an accurate estimate of thermal maturity and thermal history. However, in some sedimentary basins, traditional organic-based maturity tools such as vitrinite reflectance cannot be used because of various geologic and sampling limitations. This article establishes fluid inclusion microthermometry as a new inorganic thermal maturity tool that can be used to help fill the void in these situations. This tool uses an empirical calibration of fluid inclusion data and vitrinite reflectance data to estimate thermal maturity. Our empirical approach uses rigorous sample selection criteria that improve the statistical chance of analyzing aqueous fluid inclusions that have been thermally reequilibrated (stretched). This empirical calibration is based on a worldwide set of data that yield a logarithmic correlation having r2 = 0.96 for an ideal sample set and r2 = 0.81 for a nonideal sample set. The amount of data scatter (absolute deviation of measured vitrinite reflectance in % Ro) from the logarithmic correlation line is minimal (±0.12% Ro for the ideal data set). This new calibration, along with the sample selection and data analysis procedures described in this study, forms the basis for a new thermal maturity technique.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".