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Record W2038898027 · doi:10.4043/20298-ms

SS: Canadian: East Coast Canada R&D and Offshore Development in Northern Frontiers

2009· article· en· W2038898027 on OpenAlexaffabout
David William Finn

Bibliographic record

VenueOffshore Technology Conference · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsArcticInvestment (military)Submarine pipelineBusinessFossil fuelResource (disambiguation)Energy supplyPetroleumNatural resource economicsEnvironmental resource managementEnvironmental scienceEnvironmental protectionEnvironmental planningEngineeringOceanographyGeologyEnergy (signal processing)EconomicsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Abstract Atlantic Canada has seen the successful development of four major offshore projects in a technically-challenging frontier environment with high discovery and development costs. The response to the challenges of ice and a harsh operating environment have contributed to the growth of a significant R&D and engineering consulting capacity that is now being applied to projects in arctic, sub-arctic and other ice-covered regions. Although significant investment is being applied globally towards petroleum resource development in these environments, considerable technical challenges remain. A concerted R&D effort will be required to enable economic development of these resources. With the scarcity of arctic engineering and related capacity in the global R&D/engineering community, collaboration can minimize redundant research effort and share technology development risks and costs. This paper will present a review of some of the " arctic?? R&D capabilities and activities in Atlantic Canada, including ongoing and recently completed projects. Introduction The International Energy Agency's World Energy Outlook 2008 reference scenario projects a 45% increase in global energy demand by 2030, with hydrocarbons accounting for 80% of supply (International Energy Agency, 2008)—the world still faces a " fossil energy future??. This demand will be largely met in three ways: improved recovery from currently producing reservoirs; the development of known but previously uneconomic or low quality reserves such as shale gas and oil sands; and the discovery and exploitation of new resources in remote regions, including deep water and/or harsh environments such as the arctic. The recently-released US Geological Survey assessment of circum-arctic resources estimates that the area north of the Arctic Circle contains 1669 trillion cubic feet of natural gas, 90 billion barrels of oil and 44 billion barrels of natural gas liquids (US Geological Survey, 2008), equivalent to twice the reserves of Canada's oil sands. It is further estimated that 84% of these resources lie in offshore basins. The resources of sub-arctic regions with arctic-like engineering challenges like the Caspian Sea, Sakhalin Island, and the Labrador Shelf are not included in this assessment. The cost of exploration, drilling and transportation in these areas will make economic development challenging, and the development of new technologies and engineering solutions is central to reducing costs and enabling the safe and environmentally-sound development of these resources. Many of the challenges facing northern offshore development are already found on Canada's eastern frontier: sea ice and icebergs, cold temperatures, and severe winds and waves. The threat of iceberg scouring to the integrity of any pipeline built on the Grand Banks of Newfoundland, for example, has made the development of the 4.5 trillion cubic feet (tcf) of proven gas reserves there problematic, and perhaps uneconomic with current technology. Research and technology development that delivers new ideas, improved technology and improved economics in this environment will in some cases be directly applicable to arctic and sub-arctic regions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.180
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes2
Has abstractyes

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