High-resolution sequence stratigraphic analysis and depositional environment reinterpretation of the Jurassic–Cretaceous Fortune Bay and Hibernia Formations in the Jeanne d’Arc Basin, east coast Canada
Bibliographic record
Abstract
ABSTRACT Stratigraphic evolution of tectonically active synrift basins is challenging to reveal due to the complex interplay between eustasy and tectonics. The lithostratigraphic approach fails to restore different lithological units in correct temporal order and masks the discovery of subtle stratigraphic plays. The Jeanne d’Arc Basin, offshore east coast Canada, presents difficulties regarding temporal reservoir correlation and paleogeographic reconstruction. In this study, we established a high-resolution sequence stratigraphic framework of the Lower Cretaceous Hibernia Formation in the Jeanne d’Arc Basin. Three composite (sensu third-order) sequences consisting of 21 parasequences are interpreted with well-defined flooding surfaces and sequence boundaries, based on the combination of seismic, well-log, and core data. Each composite sequence comprises three systems tracts, four of which are selected as mapping units for synchronous paleogeographic reconstruction. Paleogeographic mapping shows different facies distribution in the same geologic times and how they evolved through time. Detailed paleogeographic mapping demonstrates the sequence stratigraphic cyclicity of marine-fluvial-marine and correlates different reservoir units between producing fields. The basal Hibernia sand interval has been reinterpreted to be a marine sandstone package of the highstand systems tract of the underlying Fortune Bay Formation. The results enhance the predictability of reservoir presence and distribution as well as the paired seal intervals at different stratigraphic levels. This work opens new play types, such as the Fortune Bay highstand shoreface play and the transgressive barrier island play, that support the continued exploration of the basin.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".