Fault-seal analysis in the Greater Bay du Nord area, Flemish Pass Basin, offshore Newfoundland
Bibliographic record
Abstract
A 3D subsurface structural model was built in a zone of the Greater Bay du Nord area, Flemish Pass Basin, offshore Newfoundland and Labrador, to carry out a post-drilled, fault-seal analysis in a multi-rift, geological complex setting; this aimed to test fault-seal predictions, calibrate computed static fault-zone attributes and estimate hydrocarbon contact depths. Hydrocarbon exploration campaigns in the Greater Bay du Nord area have primarily targeted rotated fault blocks that often exhibit structural segmentation and compartmentalization. A comprehensive approach that combines empirical and deterministic methods for static fault-seal analysis has been implemented. This approach provides insights into open, base and tight fault-seal scenarios, aiding prospect evaluation in this region. Notably, shale gouge ratios (SGRs) in the range 16–25% serve as a crucial indicator of the transition between fault-rock sealing and non-sealing fault segments. Furthermore, it emphasizes the critical role of hydrodynamics when calibrating or evaluating fault-sealing properties. In areas like the Greater Bay du Nord region, characterized by complex geology, it is imperative to regularly update fault-seal models. These updates should align with the availability of new subsurface data, comprehensive analyses and an improved understanding of the petroleum system. Thematic collection: This article is part of the Fault and top seals 2022 collection available at: https://www.lyellcollection.org/topic/collections/fault-and-top-seals-2022
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".