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Record W2009310028 · doi:10.2118/2007-199-ea

Mass Transfer Coefficients in Vapour Extraction (VAPEX)

2007· article· en· W2009310028 on OpenAlexaffabout
Lesley James, Ioannis Chatzis

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExtraction (chemistry)Mass transferMaterials sciencePetroleum engineeringMechanicsChromatographyChemistryGeologyPhysics

Abstract

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Abstract The in-situ extraction of heavy oil and bitumen requires enhanced oil production techniques to sufficiently reduce the viscosity of the oil to produce it at economical rates. Vapour extraction (VAPEX) and warm VAPEX (where the solvent condenses in-situ) are two possibilities for producing highly viscous in-situ oil from Canada's vast reserves. It has been recognized, by now, that the mass transfer involved in the vapour extraction of heavy oil includes both diffusion and convection mechanisms. However, to date, researchers have not been able yet to quantify the role of diffusion and convection in the VAPEX process. Before accurate field scale predictions can be made, the mass transfer and fluid mechanics aspects need to be understood. Laboratory scale experiments were conducted using systems of different length and permeability as well as different solvents and solvent flow rates to examine the effect on sweep efficiency and production. Using a mass transfer coefficient approach, results from the experiments are presented and the mass transfer coefficients compared for different systems. Introduction Vapour extraction (VAPEX) is an enhanced oil recovery technique for recovering in-situ heavy oil and bitumen from Canada's vast reserves. The governing mechanisms are mass transfer and gravity drainage. Solvent that is injected into a horizontal injection well where it diffuses and mixes into the heavy oil/bitumen phase and exponentially reduces the oil's viscosity. The live oil (reduced viscosity) oil can then drain via gravity to a production well. VAPEX laboratory results look promising. But, oil companies have yet to embrace VAPEX; there is no public field trial data, the mass transfer mechanisms are not quantified and there is no reliable model to predict field scale production rates from laboratory data. The goal of this work is to experimentally investigate the mass transfer involved in the VAPEX process using a mass transfer coefficient approach. Previous VAPEX experiments performed in glass etched micromodels showed bitumen dissolving into the liquid butane phase. This was observed under conditions of warm VAPEX, where the butane condensed inside the glass micromodel. In comparison, bitumen dissolution was not observed (due to the opacity of the live oil) in the normal mode of VAPEX operation when solvent vapour diffuses directly into the bitumen (James, L.A. and Chatzis, I., 2004) and (Chatzis, I., 2002). The dissolution of bitumen into draining live oil would still be valid and thus, the concept of using mass transfer coefficients was formulated. Mass transfer coefficients have been widely used in chemical processes such as absorption towers, strippers, packed columns, etc. More recently, mass transfer coefficients of non-aqueous phase liquid (NAPL) dissolution in groundwater were found and used in contaminant transport models (Kim, T-J et. al., 1999). Mass transfer coefficients are a measure of the amount of mass transferred from one phase to another through an effective area based on the concentration gradient driving force. Equation (1) shows the mass transfer coefficient (k) as a function of mass flux (NA) and species A mass fraction. In terms of finding the mass transfer coefficient of bitumen (kB) into solvent, the relationship is given by the rate of mass transferred (mB, g/s).

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.859

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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations9
Published2007
Admission routes2
Has abstractyes

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