Offshore Petroleum Play Fairway Analysis and Geoscience Data Package Program
Bibliographic record
Abstract
Abstract The Nova Scotia Department of Energy and Canada-Nova Scotia Offshore Petroleum Board have carried out a sound technical work program over many years to present potential licensees with the information to encourage them to invest in Nova Scotia. However, following some success, the result in terms of licence bids in Nova Scotia's offshore, wells drilled and hydrocarbons won, has proved disappointing. The value of the hydrocarbon province has not been fully unlocked. Although production and proven discoveries demonstrate that there is a working hydrocarbon system oil companies are leaving Nova Scotia in favour of other opportunities prior to completion of their exploration program. There is a view that, in order to attract new investors, it will be necessary to lower the barriers to entry. One of these is undoubtedly the ability of new investors to easily and quickly develop a good geological understanding of the Nova Scotia offshore basins and thus investment decisions. The intent is to conduct an industry-standard sequence stratigraphic based Play Fairway Analysis to a level of detail that is sufficient to give credibility to the outcome. It is also intended to conduct special studies that can help address the key exploration risks. Furthermore, this paper elaborates on the importance of the Geoscience Data Package for this program, and its effectiveness in re-invigorating interest in the Scotian Margin. Data availability, to support the comprehensive geotechnical evaluation, is key to reducing the barriers to entry into the region, but unfortunately it is not easily accessible to investors. The lack of a publicly available and industry-credible, geological framework, in conjunction with the fact that most geoscience data, specifically seismic data, is not freely available in full digital format is a major hurdle. The Offshore Petroleum Play Fairway Analysis and Geoscience Data Package program, for which the business rational is discussed in this paper, are aimed at addressing this issue.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.017 |
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".