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Record W2094616671 · doi:10.2118/2004-129

Investigation of the Oxidation Behaviour of Hydrocarbon and Crude Oil Samples Utilizing DSC Thermal Techniques

2004· article· en· W2094616671 on OpenAlexafffund
J. Li, Siddharth Avnesh Mehta, R.G. Moore, E. Zalewski, M.G. Ursenbach, K.G. Van Fraassen

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrocarbonCrude oilPetroleum engineeringThermalMaterials scienceProcess engineeringChemistryOrganic chemistryThermodynamicsGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract High Pressure Air Injection (HPAI) is an Improved Oil Recovery (IOR) technique in which compressed air is injected into light oil, high-pressure reservoirs. The aim of this process is to react the oxygen from the injected air with a small fraction of the reservoir oil at an elevated temperature to produce a mixture of carbon dioxide and nitrogen. The produced gas flowing from the reaction region mobilizes the oil downstream of the thermal zone, sweeping oil towards the production wells. Knowledge of the oil's oxidation behavior is a key to the successful implementation of the process. However, information on oxidation behavior of oils based on their composition is limited, especially for light oils. An experimental study was designed to examine the oxidation behavior of oils by using the Pressurized Differential Scanning Calorimeter (PDSC) at pressures from 16 psi to 1000 psi. Selected paraffins, aromatic hydrocarbon samples, two light oils and Athabasca bitumen were tested in the research. The PDSC heat flow curves clearly demonstrate the effect of chemical structure of the samples on their oxidation behavior. The pressure impact is also significant on the exothermic behavior of selected samples. An increase of pressure results in an increase in the heat released from oxidation reactions. The peak temperatures of exothermic activity in as low well as high temperature oxidation region drop as pressure increases. The extent of oxidation of hydrocarbon samples is strongly dependent on the nature of the hydrocarbon. The two light oils compared with Athabasca bitumen display different thermo-oxidative behavior Introduction Air injection continues to be an important oil recovery process, used to increase both the amount and the rate of oil recovered from a petroleum reservoir [1,2]. When air is injected into a light oil reservoir, exothermic chemical reactions occur between the reservoir oil and the oxygen contained in the injected air. The reactions are mainly oxidation reactions resulting in heat generation and the production of carbon dioxide, carbon monoxide and water with corresponding consumption of oxygen. The heat of reactions results in a temperature elevation leading to vaporization of some lighter components. Therefore, the driving gas, which can sweep the oil to production wells, is not the injected air but an in-situ generated flue gas, composed of CO, CO2, N2 and vaporized light hydrocarbon components. Air injection is a complex process, involving simultaneous heat, and mass transfer in a multiphase environment coupled with oxidation chemical reactions. Oxidation reactions play an important role in this process. In order to improve the efficiency of the air injection process it is necessary to have additional knowledge of the factors influencing the process and how they affect the oxidation of oil. In recent years, the application of thermal analysis techniques, thermogravimetry (TG/DTG) and differential scanning calorimetry (DSC) to study the combustion behavior of oil has obtained wide acceptance. Attempts to use thermal analysis techniques to study crude oil combustion began with Tadema [3]. He reported the existence of two main reactions, one at a higher and one at a lower temperature. Yoshiki and Philips [4] used DTA and TG at high temperature and pressure.

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 score0.120
Threshold uncertainty score0.985

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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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

Citations6
Published2004
Admission routes2
Has abstractyes

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