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Record W2068687533 · doi:10.2118/2002-133

Oil And Gas Activities In the Program of Energy Research And Development (PERD)

2002· article· en· W2068687533 on OpenAlexaffabout
N. Billette, S-L. Marshall

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCitationLibrary sciencePetroleumDownloadComputer scienceWorld Wide WebOperations researchBusinessEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract The Program of Energy Research and Development (PERD) is a federal program that consists of a vast array of non-nuclear energy R&D activities. The $52.5M/y R&D budget, managed by Natural Resources Canada, is distributed across twelve federal departments and agencies that deliver horizontally on coordinated research activities. The Office of Energy Research and Development has recently implemented results-based management for PERD, which will improve the strategic management of the program. PERD currently invests approximately $14M/y in oil and gas R&D carried out by five federal departments and one agency. Upstream activities include upgrading technologies and advanced separation technologies with emphasis on oil sands bitumen. Offshore and frontier activities include basin assessment and geotechnics, wind-wave-current modelling, managing sea ice, icestructure interactions, transportation safety, marine operations and ship design, management of offshore drilling and production waste, oil spills remediation and environmental impact assessment of offshore wastes and produced waters. Cross-cutting activities include flaring, pipelines and soil and groundwater remediation. An overview of these activities will be presented, as well as future shifts in PERD to meet the S&T needs of key stakeholders and the Canadian public. Introduction PERD is a funding program for federal departments and agencies that participate in energy R&D activities. A network of twelve Departments and Agencies is involved in the delivery of outputs in six broad areas: Hydrocarbons R&D, Transportation, Buildings and Communities, Industry, Power Generation, and Climate Change. The annual R&D budget is approximately $52.5M, and funds 38 programs that address priorities under these six areas. Canada presently stresses a sustainable approach to the development of energy supplies. This involves preserving the ability to respond to demands for the goods and services that energy makes possible, while minimizing the environmental impacts of meeting those demands and maximizing economic benefits now and in the future. The federal government plays a key role in sustainable oil and gas development by funding activities that will assess these resources, investigate options for reducing access costs, and in helping to establish the regulatory and technological environments to promote their potential development. This is a long-term undertaking of patient investment in research and development leading to improved codes of practices, guidelines, regulations and standards. Fossil fuels will play an important role in Canada's energy requirements for the foreseeable future, thereby stressing the importance of the related R&D needs. PERD has invested in oil and gas supply issues since the program was initiated in 1974, with an overall goal of minimizing negative environmental consequences, enhancing knowledge of the resource and maximizing recovery. Table 1 provides the breakdown and relative R&D investment of ongoing oil and gas activities in PERD; the oil and gas portfolio represents approximately twenty-seven percent of the total R&D budget of PERD. The remainder of this paper consists of an overview of key activities that PERD is currently funding. It highlights a number of selected, completed or ongoing projects presently contributing to a safer and more environmentally responsible land-based and offshore industry.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.912

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 head0.037
GPT teacher head0.247
Teacher spread0.210 · 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 designOther design
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
Published2002
Admission routes2
Has abstractyes

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