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Record W3012444927 · doi:10.2118/199959-ms

Impact of Carbonates on Reaction Kinetics of a Bitumen Combustion

2020· article· en· W3012444927 on OpenAlexaboutno aff
Connor Pope, Norasyikin Ismail, Berna Hasçakir

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCombustionThermogravimetric analysisAsphalteneActivation energyAsphaltDolomiteKineticsDifferential scanning calorimetryFraction (chemistry)Materials scienceChemical kineticsChemical engineeringMineralogyChemistryThermodynamicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Reaction kinetics experiments are conducted to estimate important combustion parameters for crude oils. However, at elevated temperatures not only crude oil, but also reservoir rock is reactive, and the interaction of reservoir rocks with fluids may change the fate of the In-Situ Combustion (ISC) process. This study investigates the role of carbonates on the reaction kinetics of a bitumen sample from Canada. To reach this goal, Thermogravimetric Analysis/Differential Scanning Calorimetry (TGA/DSC) experiments were conducted at a constant heating rate on a bitumen sample and the blends of bitumen with calcite (CaCO3) and dolomite (CaMg(CO3)2) minerals. The bitumen sample has been divided into its saturates, aromatics, resins, and asphaltenes (SARA) fractions. TGA/DSC experiments were conducted on the individual fractions and their pseudo blends in the presence and absence of carbonates to understand the contribution of each fraction in ISC success and their mutual interactions. Model fitting approach was used to analyze TGA/DSC graphs analytically to obtain activation energy and heat of reaction for each pseudo fraction, their blends, and initial bitumen samples at low (LTO) and high (HTO) temperature oxidation regions. It has been observed that among all SARA fractions, the aromatics fraction alone generated the greatest amount of energy. Saturates are known as the ignitor for the combustion and its ignition characteristics are enhanced with the presence of carbonates. Similarly, the energy generation at low temperature oxidation (LTO) region for saturates becomes more significant for the saturates-aromatics pseudo blend. While the aromatics heat generation increased more for the pseudo blend with asphaltenes in the presence of carbonates, the energy generation of aromatics is negatively affected for the pseudo blend prepared with resins and carbonates. Thus, it was concluded that for the specific bitumen sample worked in this study, resins are the critical fraction determining the ISC fate in a carbonate reservoir. Moreover, we found that thermal decomposition of carbonate minerals negatively affects asphaltenes cracking and combustion reactions since both asphaltenes cracking and thermal decomposition of carbonate rock start at around the same temperature. Our findings indicate that reaction kinetics studies should be conducted in the presence of all reservoir components (rock and fluids). However, because it is difficult to understand the contribution of each component to overall ISC performance, we recommend conducting reaction kinetics experiments on pseudo blends of reservoir fluid components. This procedure has been introduced for the first time with this study and enhanced our understanding towards ISC kinetics but should be extended to different crude oil and reservoir rock pairs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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 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".

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Citations10
Published2020
Admission routes1
Has abstractyes

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