Sequence stratigraphic distribution of diagenetic alterations in coal‐bearing, paralic sandstones: evidence from the Rio Bonito Formation (early Permian), southern Brazil
Bibliographic record
Abstract
Abstract Linking siliciclastic diagenesis to sequence stratigraphy allows a better understanding of the parameters controlling the spatial and temporal distribution of diagenetic alterations, and hence of reservoir quality. A study of the coal‐bearing, alluvial, deltaic, estuarine and shallow‐marine sandstones of the Rio Bonito Formation, early Permian, Paraná Basin (southern Brazil), reveals that the distribution of diagenetic alterations and of related reservoir quality evolution can be constrained within a sequence stratigraphic framework. Calcite, dolomite, siderite, kaolinite and pyrite cementation is consistently linked to sequence and parasequence boundaries, transgressive and maximum flooding surfaces and is systematically distributed within lowstand, transgressive and highstand systems tracts. Diagenesis of coal layers at parasequence boundaries has promoted the formation of stratabound calcite (detectable in resistivity wire line logs), concretionary pyrite and kaolinite and of silicate grain dissolution in sandstones located above and below these boundaries, particularly in the transgressive systems tract. Meteoric water diagenesis caused grain dissolution and the formation of kaolinite in sandstones below sequence boundaries and in lowstand systems tract sandstones. Carbonate bioclasts and low sedimentation rates in lag deposits at parasequence boundaries, transgressive and maximum flooding surfaces favoured the formation of grain‐rimming siderite. The results of this study are relevant to the exploration of coal‐bed methane and other coal‐bearing reservoirs, where it is crucial to unravel and predict the distribution and quality of reservoirs and compartments.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".