Kanadan Boforinmerellä tapahtuvien offshore-koeporausten logistiikkasuunnittelu ja säännöstöarviointi
Bibliographic record
Abstract
This thesis was done in order to find feasible solutions for logistics-and regulations related challenges when conducting offshore exploration drilling operations in the Canadian Beaufort Sea on exploration licenses (EL's) 451 and 453. Most of the challenges-, and solutions related to operating on these locations are generic for other similar areas in the arctic seas. It is estimated that a great majority of worlds undiscovered petroleum resources are located in the arctic areas, which are largely covered by seas. Only a handful of arctic offshore exploration drilling projects have been carried out in similar conditions as described in this thesis, and information gained from the exploration project will be of great value when planning further offshore developments in arctic areas. Solutions used in the exploration campaign described, must be very flexible because the EL's 451 and 453 are ca. 1oo-2ookm offshore and water depth in the area varies from some tens of meters to ca. 200m. The coastline of the Canadian Beaufort Sea is extremely far away from any larger settlements and has very poor logistics infrastructure, -harsh arctic climate and -heavy sea ice conditions. Several solutions for overcoming the challenges arising from the local conditions are suggested in this thesis. Strict Canadian environmental-and offshore oil-and gas regulations are assessed in the thesis and proposed solutions for applying these regulations are brought up. Main findings of this thesis are that strict interpretation of rules and best industry practices are to be followed when operating in the Canadian Beaufort Sea, in order to ensure safety of the operations as well as to avoid conflict with any of the stakeholders or interest groups. This may be achieved by gathering knowledge from operations carried out in the past with similarities to this, negotiating with regulatory bodies, sharing information with other operators and using other, less conventional information sources as non-governmental organisations such as environmental organisations and local interest groups. New technologies must be applied and existing technologies must be adjusted to better suit the harsh conditions in order to ensure operational reliability in conditions where supplies, spare parts and other commodities will be extremely difficult to get. The used equipment, assets and on-site project organisation must be highly self-sufficient and this may be achieved by having large near-or on site storage capability and highly specialised support vessel fleet. The drilling units must be capable of surviving in the harsh ice conditions by themselves with little assistance and the drilling operations and equipment used in them must be designed to allow discontinuation of the drilling operations on a short notice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.046 |
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; both teacher heads agree on what is shown here.
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".