Feasibility of Escape, Evacuation and Rescue for Facilities in Arctic Shear Zone Environments
Bibliographic record
Abstract
Abstract An area of concern for any offshore oil development beyond the "transition zone", the zone where multi-year ice and land- fast ice meet, is Escape, Evacuation and Rescue (EER). Due to the unique environmental conditions, e.g., large ice ridges, conventional evacuation methods such as lifeboats may not be sufficient. The unique environmental challenges in the Alaskan OCS and their potential impact on EER will be described. A feasibility assessment focused on the secondary evacuation component of EER will also be described. This assessment consisted of establishing performance standards related to the expected operating environment, identification of proven and novel evacuation methods, evaluation of these methods versus the performance standards, and prioritization of future work. Finally, recently-conduted data collection and analysis and technology maturation studies related to EER will be discussed. INTRODUCTION Shell's objective in Alaska is to find and develop commercial hydrocarbon resources in the Chukchi and Beaufort Outer Continental Shelf. As with all Shell ventures, the company maintains high operational and social performance standards that will bring, with exploration success, economic expansion and new opportunities to communities across Alaska and the Northwest. Since returning to Alaska in 2005 Shell has embarked on an extensive field data acquisition, R&D and technology maturation effort aimed at supporting exploration and future development. This paper focuses on development of a robust Escape, Evacuation, and Rescue (EER) solution for the Alaska OCS operating environment. The oil industry has operated successfully in Arctic and sub-Arctic locations since the 1960s. However, as the industry pursues offshore developments in the Arctic with more severe environmental conditions, it must develop new solutions and/or extend existing ones to meet these new challenges. One of the key technology challenges is development of a robust Escape, Evacuation, and Rescue (EER) solution for offshore developments in the "shear zone", where land-fast ice interacts with the mobile polar ice pack. Solutions for less severe mobile first year regions, such as Cook Inlet, offshore Newfoundland, offshore Sakhalin Island, and the northern Caspian Sea, have been developed using modified open water equipment and techniques and purpose-built craft.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".