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Record W4255918171 · doi:10.2118/2003-176

Low-Temperature Oxidation of Oils in Terms of SARA Fractions: Why Simple Reaction Models Don't Work

2003· article· en· W4255918171 on OpenAlexaff
N.P. Freitag, B. Verkoczy

Bibliographic record

VenueCanadian International Petroleum Conference · 2003
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsWork (physics)Simple (philosophy)ChemistryThermodynamicsPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Abstract The low-temperature oxidation (LTO) reactions of the SARA fractions separated from two crude oils were studied in the presence of their reservoir sands at temperatures between 125 and 230 °C. The results indicated that the usual approach to modelling LTO ? the use of a very few single-step Arrhenius-rate equations ??could not be made to reflect the observed reaction kinetics. Instead, this investigation found that the following reaction characteristics were needed for accurate reaction modelling:a change in the order of reaction with respect to oxygen concentration from 1/2 to 1 as temperature rises,the repression of a saturates oxidation reaction by other fractions, anda prominent induction period exhibited by the saturates fraction. The compositions and yields of the ultimate LTO reaction products were measured, and these included relatively stable residues with high oxygen contents. Because the LTO reactions play an important role in enhanced oil recovery by air injection methods, the above information is valuable for the simulation and prediction of these processes. Introduction Enhanced oil recovery processes need to be predictable before they can be seriously considered for widespread field application. One of the main problems limiting the development and application of new process variations for air injection or in-situ combustion is that their field performance and consequently their technical success or failure can simply not be predicted with any reliability. The most serious questions frequently hinge upon the nature of stability of the combustion/oxidation zones. Many studies have provided valuable knowledge as to the nature of the related chemical reactions, but the usefulness of proposed reaction models for numerical simulation prediction is still limited. hree main types of reaction have been found to govern air-injection EOR processes: pyrolysis/coking, low-temperature oxidation, and high-temperature oxidation (combustion). This study was concentrated on the second category of reaction, low-temperature oxidation (LTO). The free-radical nature of low-temperature oxidation of hydrocarbons has long been known. In a 1958 review, Morton and Bell1 confirmed that LTO occurs through a free-radical mechanism in which the production of hydroperoxides is an important first step. They also mentioned the role of inhibitors, discussed catalysis by metal surfaces and metallic salts, and described why long induction periods could occur before the onset of significant oxygen consumption was observed. Initially, most studies of the chemical mechanisms conducted both before and after Morton and Bell's review used pure compounds. The results varied between compounds, and could not be used directly to describe the oxidation of complex mixtures like crude oils in a petroleum reservoir. In 1968, Bousaid and Ramey,2 while investigating high-temperature oxidations, carried out three low-temperature oxidation tests on a heavy oil between 23 and 52 °C. They reported very low values of oxygen consumption, with rates that were correlated with an activation energy of 53,200 J/gmol. Later, Dabbous and Fulton3 published much more extensive results for LTO of two whole oils on crushed Berea sand over the temperature range of 121 to 246 °C. They observed the activation energy to be about 72,000 J/gmol, and noted that the orders of reaction with respect to oxygen were 0.5 and 0.75 for the two oils.

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 categoriesInsufficient payload (model declined to judge)
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 score0.068
Threshold uncertainty score1.000

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)0.0010.000

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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.

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

Citations6
Published2003
Admission routes1
Has abstractyes

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