Standards And Guidelines For The Use Of Satellite-Based Ice Information In The Oil &Gas Sector
Bibliographic record
Abstract
Abstract The oil and gas industry is increasingly focusing its interests andactivities on areas that are prone to ice cover, in the form of sea ice andicebergs. The authors have noticed two significant trends with respect to theice charting to support operations in oil and gas operations:At present, companies who require ice information are developing their owninternal practices based on different experiences.No industry-wide standards exist for ice charting in this sector. As a consequence, the authors have embarked on a project to address thisdeficiency by identifying minimum standards and best practices for theprovision of ice information derived from satellites for companies operating inthe polar and sub-polar regions. The development of a guideline governing icecharts is the primary focus of this project. The project has identifiedrequirements through the oil and gas project lifecycle, has matched these todifferent regions and has categorised satellite-derived ice information byservices and products. The project has reviewed current practices and willestablish a guideline with input and validation from the industry, taking intoaccount current constraints and future opportunities. Ice charting guidelineswill provide clear options to industry. Companies will be able to buildprocesses and systems around guidelines and can be assured that compliantservice providers will be compatible with their systems. Guidelines will alsoincrease access of the market to service providers, leading to increasedcompetition and lower costs. Ultimately, the knowledge of ice chartingcapabilities will be well documented so that they are not lost with staffattrition. This paper presents an overview of the ice charting guidelinesproject and its objectives, schedule, status and deliverables. This project isbeing coordinated through the Oil and Gas Earth Observation Group (OGEO) of theInternational Association of Oil and Gas producers (OGP) with initial seedfunding from the European Space Agency and Shell E&P International. Index Terms—ice charting, ice information, sea ice, icebergs, guideline Introduction The oil and gas industry are increasingly focusing their interests andactivities on areas that are prone to ice cover, in the form of sea ice andicebergs. At present, the approach being taken by companies who require iceinformation is to develop their own internal practices based on differentexperiences. No industry-wide standards exist. In this project, we aim toaddress this deficiency, by identifing minimum standards and best practices forthe provision of ice information derived from satellites for companiesoperating in the polar and sub-polar regions
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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 | 0.104 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".