MétaCan
Menu
Back to cohort
Record W4253258370 · doi:10.2118/2004-126

Compositional Changes for Athabasca Bitumen in the Presence of Oxygen Under Low Temperature Conditions

2004· article· en· W4253258370 on OpenAlexafffundabout
Njideka I. Jia, R.G. Moore, S.A. Mehta, K. Van Fraassen, M. Ursenbach, E. Zalewski

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
KeywordsAsphaltOxygenMaterials scienceEnvironmental sciencePetroleum engineeringGeologyChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Research described in this paper was conducted in support of a more extensive study that has been ongoing at the University of Calgary to quantify the effect of the presence of low levels of oxygen in the unheated portions of an Athabasca Reservoir undergoing in situ combustion and to evaluate if low temperature oxidation reactions could be used to achieve in situ upgrading. The objective of the overall program was to understand the compositional changes that might occur at temperatures ranging from those of the native reservoir to those experienced in a steam injection oil recovery process. The research program was originally started to quantify what were anticipated as detrimental compositional changes when oil is oxidized at native reservoir temperatures. The program was then extended to quantify the possible enhancement of the rate of cracking which might be achieved by oxidizing the oil at low temperatures, then heating it to temperatures typical of a steam injection operation. This paper will concentrate on the compositional changes of Athabasca bitumen in contact with nitrogen and air. The experiments were performed in an oscillating batch reactor with or without core and synthetic brine. The rate of oscillation was evaluated as a parameter to examine the role of mass transfer rates. Viscosity is reported in addition to the compositional data expressed in terms of the components: maltenes, asphaltenes and coke. The data has direct applicability to recovery processes involving the injection of air or a gas containing oxygen as an impurity. Typical applications of this nature include In-situ combustion, flue gas injection, and replacement of a gas cap with air or injection of CO2 containing oxygen as an impurity. Introduction Historically, the petroleum industry has tried to improve the recovery rate of heavy oils and oil sands that have reserves threetimes those of conventional oil reservoirs, but cannot be produced by conventional means. Current methods used to improve in situ bitumen production are cyclic steam stimulation and steam assisted gravity drainage. Steam injection increases the temperature in the reservoir, thereby reducing the bitumen viscosity and increasing its mobility. Sustained steam injection is facing barriers of water availability, high natural gas costs and air quality, hence air injection is again being considered as a method for in situ energy generation. In order to develop realistic designs for air injection or in situ combustion projects in bitumen reservoirs, it is necessary to understand the various reactions that are involved. Three major reactions have been reported when in situ combustion (ISC) is utilized: (1) thermal cracking, (2) liquid phase low temperature oxidation (LTO), and (3) high temperature oxidation (HTO) of vapor, liquid and solid hydrocarbon fractions, Low temperature oxidation and thermal cracking reactions are associated with immobile fuel deposition during the in situ combustion process. Low temperature oxidation reaction (LTO) is the terminology used to describe the oxygen addition reactions that occur in the liquid phase of oils. The temperature range over which these reactions occur extends from the reservoir temperature up to a nominal limit of 300 °.

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 score0.280
Threshold uncertainty score0.994

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 science0.0010.000
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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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

Citations3
Published2004
Admission routes3
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicPetroleum Processing and AnalysisFrench-language works237,207