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Record W2004480607 · doi:10.2118/2006-031

Mathematical Modeling of Thermal Conversion of Athabasca Bitumen

2006· article· en· W2004480607 on OpenAlexafffundabout
Kevin Chan, C. Diaz-Goano, Heather D. Dettman, Theo de Bruijn

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicCoal Combustion and Slurry Processing
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNorth China University of Technology
KeywordsAsphaltOil sandsComputer scienceThermalPetroleum engineeringEnvironmental scienceGeologyMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Abstract Modeling of a thermal cracking process involves establishing a set of kinetic reactions that transform the feed into products. In a typical feed, there exists a large number of real components. Thus, an exact feed composition is not known. The most common method to overcome this difficulty is to define pseudo-components based on one or more physical properties such as boiling point or molecular weight. In this work, we propose a set of model thermal cracking reactions based on nuclear magnetic resonance (NMR) data. NMR carbon type analysis improves the characterization of the feed and products by providing the contents of different carbon species (carbon bonds). This additional information enables a more descriptive and fundamental set of reactions to be developed for the model. The reactions chosen describe the types of carbon bonds that break and form in the feedstock under visbreaking conditions. The set of reactions is the centre of the pseudo-component model. Introduction Visbreaking is a thermal cracking process that has existed for many decades. It is a relatively mild process used primarily to reduce the viscosity of the heavy ends of crude oil feedstocks that results in low conversion of heavy-end material. For example, typically there is <30 wt % conversion of residue with boiling points (BP) > 524 ° C to lower-boiling components. Visbreaking is widely used in Europe and Asia due to the properties of available feedstocks and demand for heavier products such as fuel oil. In Alberta, an increasing proportion of the oil produced is extra heavy (i.e. Athabasca bitumen). Consequently, higher severity processes such as coking and residue hydrocracking are needed to convert 60 wt % or more of the residue to lighter products suitable for transportation fuels. However, there has been some interest in visbreaking as a field upgrading process to reduce the need for diluent to meet pipeline specifications for bitumen and heavy oil transportation. Despite visbreaking being a relatively simple process, modeling of the thermal cracking reactions that result in product formation poses many challenges. The greatest difficulty is to find a suitable method to describe the feedstock and product molecules. For the lightest components of crude oils (i.e. naphtha, BP <204 ° C), there are over a thousand molecular species that can be identified and quantified. However, for fractions with boiling points > 204 ° C, virtually every molecule is different. Thus, the challenge is to lump the molecules into a manageable number of groups yet be able to retain enough chemical sensitivity so as to be able to develop fundamental correlations. The most common method to characterize a feedstock is to define pseudo-component lumps based on physical properties like molecular weight or boiling point. However, as these properties provide no specific information of the relative chemical reactivity of the lumps, the kinetic parameters determined are empirical and need to be adjusted for each different feedstock. Recently, nuclear magnetic resonance (NMR) spectroscopy has been used for structural analyses of petroleum fractions. Ali et al.(1) used NMR data to estimate average molecular structures of Kuwaiti vacuum gas oil.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.210
Teacher spread0.194 · 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 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

Citations0
Published2006
Admission routes3
Has abstractyes

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