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Record W1990235643 · doi:10.2118/2005-078

Understanding the Impact of Refined Product Properties on Synthetic CrudeOil and Bitumen Marketability

2005· article· en· W1990235643 on OpenAlexaboutno aff
W.L. Mazurek, Terry Kemp, G.W. Bruce, John Forrest

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltProduct (mathematics)Computer scienceProcess engineeringMaterials scienceEngineeringMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract It is critical that companies upgrading heavy oil understand the impact of Synthetic Crude Oil (SCO) properties on the ability to market feedstocks to conventional refineries in the USA. Due to the proximity and existing pipeline infrastructure, the USA is a natural market for crude oils derived from Alberta's oil sands deposits. There are also significant political drivers for the USA to develop crude supplies in a country with which it has strong cultural, economic, and political ties. This paper outlines the key properties of refined distillates and discusses how these properties are affected by the various hydrocarbon components contained in bitumen derived crude oil. The impact of integrating bitumen and SCO into a conventional refinery are also discussed in terms of its ability to meet these important distillate specifications. Introduction Synthetic crude oil has unique properties compared to conventional crude oils that required special considerations. These qualities make upgrading bitumen to saleable products more difficult than upgrading a conventional sweet crude which has been the historical feedstock to US refineries. A conventional refiner in the US faces a number of issues when considering running bitumen derived feedstocks, either synthetic crude oil (SCO) or bitumen blends. Some of the processing issues include a lack of heavy conversion capacity, insufficient metallurgy to process high acid crude oils, and difficulties meeting final product specifications. This paper will focus on the difficulties in meeting key product properties while producing diesel, jet fuel, and FCC feed from SCO and bitumen. Bitumen Bitumen Characteristics In its natural state, bitumen is an extra-heavy oil with the consistency of roofing tar. It contains significant quantities of asphaltene material and is highly aromatic. Due to its heavy nature, bitumen has high levels of sulfur, nitrogen, and metals. Bitumen also contains organic acid compounds at levels high enough to cause serious corrosion issues in downstream processing units. On its own, bitumen cannot be sent directly to US refiners since it does not meet pipeline density and viscosity specifications. For bitumen to enter into US markets, it must be blended with a lighter material to form dilbit or synbit, or it must be upgraded to SCO. The diluent used for bitumen can be either a light hydrocarbon (gas condensate or naphtha) to form dilbit or a full range sweet synthetic crude oil to form synbit. Dilbit is approximately 20–30% condensate, while synbit is approximately 50% bitumen, 50% synthetic crude. The target pipeline specifications are gravity >19 °API and viscosity <350 cSt at the designated temperature (usually ground temperature). Product Properties Diesel Fuel Diesel fuels are considered to be the hydrocarbon compounds boiling between 500 – 650 °F. While outside of the USA and Canada, diesel is a significant light duty passenger vehicle transportation fuel, in the USA and Canada diesel is primarily used in heavy-duty transportation and industrial equipment. In the USA, on-road diesel (the largest diesel market1) properties must as a minimum meet the specifications in ASTM D-9752. Canadian automotive diesel quality is governed by CGSB 3–517, which is similar to the ASTM D-975 specification.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.733

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 head0.067
GPT teacher head0.256
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations1
Published2005
Admission routes1
Has abstractyes

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