Lateral Drainage in the Jeanne D'Arc and Flemish Pass Basins, East Canada
Bibliographic record
Abstract
Shale-based pore pressure prediction has established that in many wells shale pressures are significantly higher than found in associated pressures taken in both clastic and carbonate reservoirs in the Jeanne d’Arc Basin. Similarly in the Flemish Pass Basin, pressure regressions (deeper reservoirs with less overpressure in reservoir than shales above) are observed. These observations from both basins suggest that some porous horizons have lost pressure relative to their associated shales – this phenomenon is called drainage. However, in some places the shales and reservoirs are at approximately the same pressures. These variations in overpressure at the same levels in different areas are related to the presence or absence of stratigraphic/structural isolation. The lateral drainage is associated with the dewatering of deep, overpressured shales into reservoirs of high lateral extent with flow focused towards leak points in the basins. The recognition of these draining reservoirs has profound implications for the petroleum system in East Canada. These types of reservoirs are not restricted to offshore Canada, however, but found worldwide. They are associated with enhanced seal potential allowing longer than usual hydrocarbon columns to accumulate, as well as hydrodynamic aquifers, with implications for non-structurally controlled hydrocarbon distributions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".