Application of 3D marine controlled-source electromagnetic finite-element forward modelling to hydrocarbon exploration in the Flemish Pass Basin offshore Newfoundland and Labrador, Canada
Bibliographic record
Abstract
The Flemish Pass Basin located 450 km east offshore St. John’s, Newfoundland, Canada has seen an increase in exploration activity over the past decade. Risk mitigation is important for deepwater drilling, and marine CSEM interpretation techniques have the potential help de-risk reservoirs in an offshore exploration setting. This thesis uses 3D marine CSEM finite-element forward modeling with comparisons to measured data to (1) show the finite-element forward modeling code can synthesize data from real complex models built using unstructured grids, and (2) use this forward modeling technique to provide additional support and interpretations for two offshore exploration fields in the Flemish Pass Basin: Mizzen and Bay du Nord. In summary, the finite-element forward modeling code was able to synthesize good quality results from complex models built from real data. Sensitivity to the Mizzen reservoir was found, but it is likely below the detectability threshold. This is likely a result of the reservoir being too small and containing uneconomic volumes of hydrocarbons. However, the Bay du Nord reservoir is much larger and is predicted to contain much higher volumes of hydrocarbons. Numerical analysis confirmed a much greater sensitivity to the Bay du Nord reservoir exists.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".