Requirements on Logistics to Tow Large Gravity-Based Structures to the Chukchi and Beaufort Sea
Bibliographic record
Abstract
Abstract Oil companies are considering large Gravity-Based Structures (GBS) as a potential solution to support operations in new offshore oil and gas field developments in the Arctic seas that are designed for year-round production in potentially 100-meter-deep waters. These structures support multibillion-dollar drilling, production, and storage facilities that are typically commissioned with topsides and consumables before entering the Arctic, and are towed with these elements already installed on the GBS to the installation site. However, the tow not only presents a high risk during the installation phase of the platform, but also has a significant influence on the design and cost of the GBS. Movement of these large offshore structures through the Bering Straits and into position at the installation location includes unique risks that result from the minimal precedence of these tows and the scarcity of local support and contingency ports. This situation suggest that typical approaches to risk identification, design criteria development, and application of risk mitigation approaches may merit reevaluation. This paper will discuss the logistics related to the movement of these large structures and discuss the various cost- and schedule-related risks that have to be identified to support the early design phases of the GBS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".