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Record W4250844428 · doi:10.2118/2005-169

Phase Behaviour and Compositional Changes Associated With Residual Oxygen in a Miscible CO/Light Oil Reservoir

2005· article· en· W4250844428 on OpenAlexafffundabout
Njideka I. Jia, R.G. Moore, S.A. Mehta, K. Van Fraassen, M. Ursenbach, E. Zalewski

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResidual oilOxygenResidualLight crude oilMaterials sciencePetroleum engineeringPhase (matter)ThermodynamicsChemistryGeologyComputer sciencePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Concern about the effects of global green house gases has made people aware of the need to reduce the atmospheric concentration of industrial waste gas. Alberta, as the center of Canada's petroleum industry is strongly impacted by the politics of green house gases. One way Alberta can achieve the goals of the Kyoto Agreement is to capture the CO2 from the stack gases and to inject it into oil reservoirs to enhance recovery while sequestering at least a portion of the CO2. Previously, fundamentals for CO2 flooding in the light and heavy oils have been widely discussed. This paper will concentrate on oil from the Swan Hills reservoirs of Devon Oil Corporation. Injection of CO2 into this type of reservoir has theoretical potential as CO2 enhances recovery through mechanisms such as oil viscosity reduction, swelling of the oil, vaporization of the oil, reduction of interfacial tension and solution-gas drive during blow-down. These effects are most significant at low temperatures. The phase behavior of different concentrations of CO2-O2 mixtures injected into a light oil reservoir was investigated. Two PVT (Pressure-Volume-Temperature) apparatus were used to visually observe phases and to determine bubble points and swelling of the oil rich phase. The PVT measurements were performed to develop a basic understanding of the phase behavior for the CO2/O2/oil system. The composition of oxidized oil was predicted using a previously developed low temperature oxidation reaction model coupled with phase equilibrium calculations. Introduction Carbon dioxide flooding is a well-established enhanced oil recovery process in regions where CO2 is available. Canada has been slow to implement CO2 floods due to the lack of natural sources; however the public's concern with greenhouse gas emissions and Canada's support of the Kyoto Agreement should provide sufficient volumes of CO2 and environmental incentives to allow for the application of CO2 flooding as a combined EOR/sequestration process. Capturing the CO2 from stack gas is an expensive process and the actual cost will depend on the purity of the CO2 stream that is required. Oxygen is an important impurity as it can modify the native oil properties through low temperature oxidation reactions, promote corrosion in the injection and production piping, and alter the phase behavior of the reservoir fluids. The study from which the work described in this paper originated will examine how oxygen is consumed in the reservoir and will concentrate on the impact of oxidation reactions with regard to the composition and emulsification characteristics of the oil. Lee et al. [9]studied the phase behavior and displacement efficiency of a carbon dioxide-hydrocarbon system which consisted of various mixtures of n-pentane/n-hexane. The results demonstrated that multiple phase behavior involving 2 liquids with and without a vapor phase occurred during carbon dioxide injection in low temperature reservoirs. A number of studies on effectiveness of carbon dioxide displacement under miscible and immiscible conditions have been reported (Rathmell et al.[14], Wilburn et al.[22], Dyer et al.[4], Srivastava and Huang[16,17,18]). The results show that CO2/oil miscibility could be developed and that the swelling behavior depended on the chemical composition of the oil.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
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
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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 designObservational
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

Citations2
Published2005
Admission routes3
Has abstractyes

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