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Record W4247982597 · doi:10.32920/ryerson.14664486

Experimental determination of solvent gas dispersion in vapex process

2021· preprint· en· W4247982597 on OpenAlexaboutno aff
Randa E El-Haj

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringDispersion (optics)Materials sciencePorosityAsphaltSolventContext (archaeology)PetroleumWaste managementEnvironmental scienceChemistryComposite materialEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Canada has about one-third of the world's known petroleum reserves in the form of heavy oil and bitumen, which can meet our energy needs for the next two centuries. In this context, the vapor extraction (Vapex) of heavy oil and bitumen has drawn considerable attention in recent years. Not only this process has the potential to sequester greehouse gases besides requireing low energy costs and capital investment, but also the capability of in situ upgrading of heavy oil. At present, there is a significant interest in the determination of the dispersion of solvent gases during Valex in order to predict the amount and time scale of oil recovery as well to optimize the field operations. Not much research has been done so far to investigate dispersion in presence of fluid flow that is transverse to gravity such as in Vapex. In this work, the dispersion of butane solvent gas is determined as a linear function of its concentration in heavy oil and bitumen based on Vapex experiments carried out in the Transport Modeling Laboratory at Ryerson University. A cylindrical wire mesh, which had a cavity of 21 cm high and 6 cm diameter, packed with homogenous porous media satursated with Athabasca heavy oil was used as a physical model for heavy oil vapor interface. The physical model was packed with three different sizes of glass beads. The permeanbilites of the different homogenous glass beads packing were tested. For each model, an experiment was conducted at a room temperature with ± 0.5oC variation, and pressure close to butane dew point with variation ± 0.007 MPa. Under these conditions, the physical model was expose to a butane solvent gas, which diffuses into physical model, and gets absorbed in Athabasca bitumen. As a result of the gas absorption, a significant reduction in viscosity was experienced. The diluted live oil was drained along the solvent vapro/oil interface under the action of gravity. The decrease in mass of the physical model was measured and recorded every 1 minute. Average live oil viscosity, average density, and average dissolved butane mass fraction in Athabasca bitumen sample were determined to be 2.742 cP, 0.86 g/cm3, and 0.48 respectively. These experiments were simulated by a mathematical model, which was used to determined the dispersion coefficient of butane gas into Athabasca bitumen. The dispersion coefficient of butane gas was considered as a linear function of its concentration in the porous media. The mathematical model was numerically solved using finite difference method. Different values for dispersion coefficient and butane saturation mass fraction were used in the simulation. Steepest decent method was used to iteratively evaluate dispersion coefficient and minimize the error. The optimum values of dispersion coefficient and butane gas saturation solubility in Athabasca bitumen were determined by matching the calculated and experiemental values of live oil production.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.273
Teacher spread0.263 · 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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

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