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Record W1994941095 · doi:10.2118/2008-120

Phase Behaviour and Physical Properties of Athabasca Bitumen, Propane and CO

2008· article· en· W1994941095 on OpenAlexafffundabout
Harvey W. Yarranton, A. Badamchi-Zadeh, Marco A. Satyro, Brij Maini

Bibliographic record

VenueCanadian International Petroleum Conference · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersSyncrude
KeywordsAsphaltPropanePhase (matter)Oil sandsEnvironmental scienceMaterials scienceChemistryThermodynamicsComposite materialPhysics

Abstract

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Abstract The design and optimization of solvent based processes to recover heavy oil are hampered by limited data and modeling capability for mixtures of heavy oils and solvents. Phase boundaries, compositions, and physical properties such as viscosity and density are required. Here, mixtures of propane and CO2 with Athabasca bitumen are considered. Saturation pressures were measured in a PVT cell and the density and viscosity of the saturated liquid phase were determined at temperatures between 0 and 90 °C and pressures up to 5 MPa. Data are reported for CO2-bitumen, propane-bitumen and three propane-CO2-bitumen mixtures. Vapour-liquid and some liquidliquid and vapour-liquid-liquid phase boundaries were determined. Regions where multiple liquid phase formation is likely were identified. A simple analytical methodology for determining the vapour-liquid phase boundary for each mixture was developed. Introduction In Canada, steam based methods are often employed to improve heavy oil recovery. However, the industry is seeking alternatives to these methods because they are energy intensive and are drawing heavily on the available water supply. Solvent based recovery methods are a potential alternative capable of providing high recovery factors without high waterrequirements(1,2). One option is the vapor extraction method(Vapex), which is a solvent-based analogue of the steam assisted gravity drainage (SAGD) process(3–6). Vapex is implemented with a pair of horizontal wells: a production well at the bottom of the reservoir and a solvent injection well located directly above the production well(4). The vaporized solvent is injected through the injector and a chamber of solvent vapour forms around the well. At the walls of the chamber, the solvent diffuses into a surface layer of the heavy oil and dramatically reduces its viscosity. The diluted oil layer is then mobile enough to drain down, under the influence of gravity, into the production well. VAPEX performance depends on the viscosity and density of the liquid phase that forms at the edge of the solvent chamber. In order to design and optimize VAPEX and other solvent based processes, it is critical to be able to: determine the diffusivity of the solvent in the heavy oil; identify the phases that form in the solvent and heavy oil mixtures at various temperatures and pressures; determine the density and viscosity of the liquid phase. Other solvent-based processes (steam and solvent injection for heavy oil recovery and solvent extraction of oil sands) require similar data. Most research on Vapex has focused on physical model experiments with light alkane solvents, particularly mixtures of methane and propane (2). However, mixtures of carbon dioxide and propane may be a more viable option. Currently, carbon dioxide is expensive but costs are expected to decrease if environmental incentives to sequester carbon dioxide are introduced. Carbon dioxide may also be a better Vapex solvent than methane because it is more soluble in heavy oil and reduces the viscosity more (7). However, at typical heavy oil reservoir conditions (pressure of ∼1.2 MPa and temperature of ∼10 ° C), propane and butane have higher solubility and provide greater viscosity reduction than carbon dioxide.

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 score0.255
Threshold uncertainty score0.991

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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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

Citations13
Published2008
Admission routes3
Has abstractyes

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