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Record W1572148780 · doi:10.1002/ieam.1653

Air emissions associated with decommissioning California's offshore oil and gas platforms

2015· article· en· W1572148780 on OpenAlexaff
Peter Cantle, Brock B. Bernstein

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2015
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsNuclear decommissioningEnvironmental scienceWaste managementDemolitionPort (circuit theory)Submarine pipelineEnvironmental engineeringEnvironmental protectionEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Abstract The 27 oil and gas platforms offshore southern California are nearing the end of their productive lives and will be decommissioned in the near future. Many are in deep water and are correspondingly large, with the largest, Harmony, in 1200 feet of water and weighing approximately 43 000 tons. Nearly 30% of California's platforms are in water depths that exceed those of any previous decommissioning project anywhere in the world. Decommissioning will involve the operation of diesel-powered heavy equipment for long periods in virtually all phases of the operation (e.g, at the platform, in transit to and from the platform, in port, at offloading, salvage, and recycling facilities) in a region where air quality is a crucial concern for state, federal, and local regulatory agencies, as well as the public. To support future decision making about the choice between decommissioning options, we consider potential air emissions generated under complete and partial (removal to 85 feet below water line) removal options. We describe major emissions categories, and the environmental and human health issues associated with each, and examine how the regulatory system would operate in specific projects. We then describe methods to estimate emissions for a worst-case example involving the largest platform, Harmony. We estimate that complete versus partial removal of Harmony would result, respectively, in 600 or 89 tons of NOx, 50 or 7 tons of carbon monoxide, 29 400 or 4400 tons of CO2, 21 or 3 tons of PM10, and 20 or 3 tons of PM2.5. Complete removal of Harmony's jacket and topsides creates approximately 6.75 times more air pollution than partial removal down to 85 feet below the sea surface. We discuss how the Harmony estimate can be used as a baseline to roughly estimate emissions from decommissioning other platforms, using expected time on station for the major categories of decommissioning equipment. Integr Environ Assess Manag 2015;X:000–000. © 2015 SETAC Key Points The majority of air pollution emissions from platform decommissioning will result from diesel engine combustion during the removal and salvage process. Total air pollution emissions during partial or complete removal varies based on the number of days diesel engines are used. Using the interactive decision model PLATFORM, complete removal of the largest oil platform off the California coast, ExxonMobil's Harmony, would result in an estimated 6.75 times more air pollution than partial removal 85 ft below the sea surface. Complete versus partial removal of Harmony would result, respectively, in 600 or 89 tons of NOx, 50 or 7 tons of carbon monoxide, 29,400 or 4,400 tons of CO2, 21 or 3 tons of PM10, and 20 or 3 tons of PM2.5.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2015
Admission routes1
Has abstractyes

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