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Record W3192684531 · doi:10.4043/30993-ms

The Use of Offshore Wind to Reduce Greenhouse Gas Emissions in Offshore Hydrocarbon Production - A Case Study

2021· article· en· W3192684531 on OpenAlexaffabout
David McLaurin, Mike Paulin, Cheng Peng, Rama Yadlapati

Bibliographic record

VenueOffshore Technology Conference · 2021
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsOffshore wind powerElectrificationSubmarine pipelineGreenhouse gasWind powerShoreElectricity generationEnvironmental scienceEngineeringMarine engineeringEnvironmental engineeringElectricityPower (physics)OceanographyGeology

Abstract

fetched live from OpenAlex

Abstract The move to reduce greenhouse gas emissions in the offshore hydrocarbons production industry has resulted in a growing interest in the possibility of using offshore wind to reduce on-platform power generation. However, the integration of floating wind power into a brownfield development project offshore has not yet been undertaken nor has any operating greenfield projects incorporated floating wind electrification into their design. A number of smaller pilot projects exist in the floating offshore wind area consisting of single prototype floating towers to demonstrate a design concept, but these are providing power back to shore. Where electrification of offshore facilities has taken place, they have utilized shore-based power. In this paper, the authors present a case study of electrifying brownfield and greenfield oil and gas production facilities via offshore wind farms and the technical challenges associated with this transformation. Intecsea has recently completed a generic investigation into the electrification of floating offshore oil and gas host facilities offshore Newfoundland, Canada using floating wind power. Electrification of floating host facilities eliminates or reduces the requirement for local power generation via turbine generators at the host facility, decreasing operational expenditure and total emissions from the facility. This work has included the investigation of existing offshore wind projects, equipment requirements and technical readiness, floating wind array best practices, greenhouse gas emissions reduction and required capital expenditure (capex). In this paper, the authors present a case study of electrifying floating brownfield and greenfield oil and gas production facilities using offshore wind farms and the technical challenges associated with this transformation. Challenges identified for the electrification of floating offshore facilities include: challenges associated with dynamic cabling at different water depths determination of best cable configuration and array layout determination of the best suited support structure (floating foundation) sizing of generator (can have a significant effect on the tower's performance) best anchoring solutions; optimization of power tie-in and storage insufficient real estate or weight capacity (for brownfield applications). The authors provide details on wind farm requirements and tie-in for electrification of offshore production facilities for different scenarios. A summary of modifications/additions required at a brownfield host facility for power supply by wind power array are presented. Related to floating production facilities, an investigation of ongoing project work related to dynamic, disconnectable cables which will operate in the upper end of MVAC, HVAC or HVDC range has been carried out and is presented. For cases selected, avoided GHG emissions and associated capex are estimated and presented. The use of offshore floating wind to supplement/replace on platform power generation is part of the ongoing global energy transition.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.253
Teacher spread0.218 · 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.

Study designBench or experimental
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

Citations2
Published2021
Admission routes2
Has abstractyes

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