Verification and Application of Sequence Stratigraphy to Reservoir Characterization of Horn River Basin, Canada
Bibliographic record
Abstract
Shale reservoirs, the most important unconventional resource, are difficult to characterize. Shale formations require detailed interpretation of geological, petrophysical, and geochemical analyses, and an integration of these disciplines. In terms of geological interpretation, the commonly used sequence stratigraphy analysis includes a lithofacies analysis. The application of sequence stratigraphy to shales facilitates the ability to relate between lithofacies and mineral composition, petrophysical parameters, and kerogen contents, which are affected by depositional setting. The classification of lithofacies is indispensable for reservoir quality prediction. In this study, porosity, permeability, and TOC content largely depend on lithofacies, and their correlation coefficient is relatively high. The sequence stratigraphic interpretation shows that organic carbon content usually increases within the maximum flooding surfaces and decreases stepwise. However, the relationship between total organic carbon contents and systems tract is less direct and redox dependent.
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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".