Predictive Modeling of Reservoir Quality Associated with the Dissolution of K-Feldspar During Diagenesis: Lower Cretaceous, Scotian Basin, Canada
Bibliographic record
Abstract
The distribution and quality of the Lower Cretaceous reservoir sandstone units of the Mesozoic–Cenozoic Scotian Basin, offshore eastern Canada, is well known in producing fields but difficult to extrapolate to less-explored areas of the deep-basin floor. Prediction of reservoir risk is complicated by salt tectonism and the strong influence of diagenesis on reservoir quality. This study investigates the burial diagenetic dissolution of detrital K-feldspar in the subarkosic sandstones and the preservation of the resulting secondary porosity. K-feldspar abundance declines with increasing depth, creating secondary porosity, which in open systems is preserved but in closed systems is clogged by carbonates and clays. The distribution of detrital K-feldspar has been simulated using forward stratigraphic modeling and is compared to thermal modeling, fault mapping, and sand distribution to determine the risk due to the reservoir quality, illustrated as common risk segment maps. Sand deposits have the lowest risk of poor reservoir quality along the shelf edge and upper slope of the central and western basin, where growth faulting created an open diagenetic system. This novel combination of petrographic study and forward modeling has applications to other regions where diagenesis has a strong influence on the reservoir quality, such as the Gulf of Mexico.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".