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Record W2075024663 · doi:10.2118/06-08-04

Adding Value to Alberta's Oil Sands

2006· article· en· W2075024663 on OpenAlexaffabout
S.J. Laureshen, Paul Clark, M. P. du Plessis

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsNova Chemicals (Canada)Alberta Energy
FundersSasol
KeywordsPetrochemicalRaw materialSynthetic crudePetroleum industryOil sandsFossil fuelOil refineryPetroleumAsphaltNaphthaWaste managementWork (physics)Government (linguistics)Flexibility (engineering)EngineeringUnconventional oilPetroleum engineeringEnvironmental scienceEnvironmental engineeringChemistryEconomicsMaterials science

Abstract

fetched live from OpenAlex

Abstract A rapidly expanding oil sands industry and a dwindling supply of feedstock for Alberta's ethane-based petrochemical industry have stimulated interest in evaluating bitumen for producing a broad slate of refined products, including petrochemicals. Two industry/government studies evaluated different process schemes for integrating oil sands, refining, and petrochemical operations and convert heavy gas oils into both refined products and petrochemicals. Since market demand for fuels and refined products far exceeds that for petrochemicals, the performance characteristics of the heavy oil conversion processes are important to optimize the volume ratios of the products to meet market volume demands. The paper reviews different heavy oil processing technologies focusing on olefin to fuel product ratios and flexibility to change these ratios. The review includes conventional noncatalytic thermal (steam) cracking, as well as catalytic processes. These technologies are at different stages of commercial development for production of fuels and olefins, and must be evaluated and adapted to meet Alberta's aromatic bitumen-derived heavy gas oils. Work is underway in an industry/government study towards developing an integrated process for the combined production of refined fuels and petrochemical feedstocks. In addition, two workshops were held in February 2005 to address the business and regulatory gaps that needed to be addressed before such a process can be commercialized; the results from the workshops will also be discussed in the paper. Introduction Alberta has an enviable position as a North American energy hub, providing oil and gas to United States markets through an extensive pipeline network. In addition to conventional oil and gas, Alberta has large reserves of coal and coal bed methane, as well as the massive oil sands deposits that underlie 140,800 square kilometres of the province. The oil sands have outstripped conventional oil reservoirs as the primary source of oil in the province. According to the Alberta Department of Energy, production of bitumen and synthetic crude oil was close to 158,987.3 million m3/d (one million BPD) in 2003, as opposed to 100,162 m3/d (630,000 BPD) of conventional oil production. If all new projects, and expansions to existing projects currently planned, take place as scheduled, Alberta's bitumen production is expected to triple by the year 2030. However, the continued expansion of Alberta's oil sands faces significant challenges. Diluent availability is already a problem, water use is facing restrictions, and natural gas is becoming more costly and less available. A further problem is the ability of Canadian and U.S. markets to absorb additional bitumen and synthetic crude production. Refineries in the U.S. Midwest (PADD II), which is the largest traditional market for oil sands products, cannot process the projected increase of heavy feedstock without additional residual upgrading capacity. The alternative is to build upgrading capacity in Alberta, which could increase production costs and make Alberta crude less competitive in the export market. Natural gas cost and availability is an issue for more than the oil sands 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 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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.541
Threshold uncertainty score0.913

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.002

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 head0.004
GPT teacher head0.202
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 designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes2
Has abstractyes

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