Optimizing Geochemical and Sediment Sampling in Frontier Areas by Reviewing Past Projects and Analyzing the Benefits of Introducing New Technologies and Practices
Bibliographic record
Abstract
Abstract Due to the large geographical areas typically associated with frontier regions, the need to enhance techniques for gathering information on petroleum systems and prospect charge is critical to ensure efficient and accurate results. Small changes in efficiencies of acquisition techniques and use of ever improving technology can significantly improve the chances of success. The results from a 2015 geochemical sampling program offshore Labrador and Newfoundland were reviewed, to determine areas of improvement and efficiency for future work in this and other frontier areas. Seismic data acquired offshore Newfoundland and Labrador plus surficial satellite seep mapping hinted at active petroleum systems and thus a need for a geochemical survey to assess these was determined. Evaluated areas were divided into two groups, one to identify regional petroleum systems and the other to reduce prospect charge risk. Core samples, heatflow and bathymetry were among the data collected. Using the concept of 'intelligent sampling' the authors are developing systems and procedures to ensure efficiency and improve the chances of analytical success. Dedicated vessels with DP systems are utilized, complete with a full range of multi-beam sonars, sub-bottom profilers, dedicated launch and recovery systems, and sub-surface positioning to ensure coring accuracy. Further innovations included core barrel mounted cameras and coring rope load monitoring. Based on the 2015 survey, modified approaches are proposed to improve sample acquisition, many of which are being implemented in the 2016 survey:core recovery (ensure cores penetrate below the biogenic zone) with revisions to the drop core assembly design;evaluation of appropriate coring methods (gravity, piston and vibro);new technologies for live slick sampling (traditionally difficult in areas of rough weather or sea conditions) are analysed with oil detection radars and seaborne/airborne drones;methods to reduce probability of sample contamination; and,best practice storage methods to meet the needs of the variety of analytical methods proposed.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".