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Record W1990153759 · doi:10.2118/06-09-01

Kinetic Modelling of Thermal Cracking and Low Temperature Oxidation Reactions

2006· article· en· W1990153759 on OpenAlexaffabout
Na Jia, R.G. Moore, S. A. Mehta, M.G. Ursenbach

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersTsinghua University
KeywordsAsphalteneCrackingAtmospheric temperature rangeAsphaltOxygenChemistryCombustionCokeReaction rateBond cleavageChemical engineeringThermodynamicsMaterials scienceOrganic chemistryCatalysisComposite material

Abstract

fetched live from OpenAlex

Abstract Two kinetic models were developed to describe thermal cracking and low temperature oxidation (LTO) reactions of Athabasca bitumen for the in situ combustion process. These unified kinetic models can describe the compositional changes in the Athabasca bitumen under thermal cracking or LTO. Bitumen composition was expressed in terms of the pseudo-components maltenes, asphaltenes, and coke. Oxidation reactions occurring in the low temperature range (less than 300 °C) are complex with two primary oxidation reaction modes: oxygen addition and bond scission. For Athabasca bitumen, oxygen addition reactions are generally dominant in the low temperature range (less than 300 °C) however bond scission or carbon oxide forming reactions occur to some extent at reaction temperatures greater than approximately 150 °C. The variation in the oxygen uptake rate with time at a given temperature is accounted for through rate equation describing the change in composition of the oil and the rate of oxygen uptake at the temperature of interest for each of the pseudo-components. The oxidation model for the low temperature range that was developed in this work accounts for both oxygen addition and bond scission modes of reaction. It is capable of predicting the effect of temperature, pressure, oxygen concentration, and time on bitumen composition and oxygen uptake rates. Introduction For nearly 90 years, in situ combustion (ISC) has been employed to improve recovery from oil reservoirs. Until now, this technology has not been widely used because of the mixed history of success of the field implementation. The failures have resulted from many reasons, however a key point is that the transition of experimental data to commercial pilot stages has not been achieved. This stems from the fact that the fundamental reaction mechanisms of the in situ combustion process have not been understood completely. It is well known that the combustion front advance and air (or fuel) requirement of ISC are determined by the kinetics of the reactions occuring in the vicinity of the burning front. Three major reactions have been reported:thermal cracking;liquid phase low temperature oxidation (LTO); and,high temperature oxidation (HTO) of an immobile hydrocarbon residue. This paper will focus on the first two reactions because they are the two principal reactions associated with fuel deposition during the in situ combustion process. Thermal cracking reactions are traditionally referred to as the fuel deposition reactions for in situ combustion. The carboncarbon bonds of the heavier hydrocarbon components are broken to form low carbon number hydrocarbon molecules, plus an immobile fraction which is referred to as coke. In order to better understand thermal cracking reaction mechanisms and correctly predict product concentration, it is necessary to understand the thermal cracking reaction within the timeframe of an in situ combustion process. A significant amount of data on the thermal cracking of Canadian heavy oils has been published(1–3) and compositional models have been developed to describe the change in the oil composition as a function of temperature and time.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.007
GPT teacher head0.195
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 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

Citations42
Published2006
Admission routes2
Has abstractyes

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