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Record W2018192541 · doi:10.2118/2005-180

A Numerical Approach to Simulateand Design VAPEX Experiments

2005· article· en· W2018192541 on OpenAlexaff
X. Wu, M. Polikar, Luciane B. Cunha

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceEngineering drawingEngineering

Abstract

fetched live from OpenAlex

Abstract The Vapour Extraction (VAPEX) process is a promising technique directed towards heavy oil reservoirs that are typically thin and underlain with water, and cannot be exploited economically or technically by conventional thermal recovery methods. The VAPEX technique was developed by Butler and Mokrys in the 1990s as an alternative to Steam-Assisted Gravity Drainage. This process is mechanistically complex and some questions regarding its expected performance are still pending. A numerical model can play a critical role in addressing important questions about the process. Specifically, a numerical model can predict the performance of the process, especially the occurrence and effect of asphaltenes precipitation during the ‘upgrading’ process. This research proposes an alternative approach to simulate numerically the asphaltene precipitation effect of the VAPEX process. The model was constructed using a commercial thermal reservoir simulator. It was then validated using published experimental data. The effect of relative permeability curves, reaction frequency factor, selection of reactant, apparent dispersion coefficient, and operating parameters on performance were investigated. In addition, the model was used to design a physical experiment. The operating conditions of the experiment were optimized to represent the main mechanisms of the VAPEX process. The results of the study indicate that the numerical model can reproduce the process with acceptable accuracy. Moreover, despite the significant viscosity reduction, it wasfound that there was no significant evidence to demonstrate blockage of fluid flow through the porous medium due to asphaltene precipitation. Further experiments would be required to confirm these findings. Introduction The vapour extraction (VAPEX) process1 (Figure 1) has drawn the increasing attention of the heavy oil/bitumen industry since it was developed by Butler and Mokrys in 1991. In this process, the same well configuration, as well as the same counter-current drainage concept as the popular steam-assisted gravity drainage (SAGD)2 process (Figure 2), are used. However, solvents, such as butane and propane3,4 are injected, rather than steam, at or near their dew point. The mobility of the oil is improved through the mass transfer (diffusion and dispersion) effect between the solvent vapour chamber and the oil. The VAPEX process is a non-thermal recovery method. It is more energy efficient and environmentally friendly than thermal processes. It is expected to improve recovery in problematic reservoirs, such as thin reservoirs and reservoirs with bottom water3,5, where thermal recovery methods are not economically or technically feasible. Another benefit of the VAPEX process is the potential to upgrade the oil in-situ3 resulting from asphaltenes deposition. However, the initiation and effect of asphaltenes precipitation on fluid flow during the VAPEX process has not been resolved. Asphaltenes deposition was observed in almost all the physical experiments that Butler and his colleagues performed3–9. However, other researchers10–13 observed less or no asphaltenes deposition in their experiments. It is also plausible that asphaltenes precipitation during the VAPEX process might plug the pore throats under reservoir conditions. Investigations on scaled physical models with significant permeability suggest that such is not the case. However, the performance of reservoirs with a range of the reservoir permeabilities is of concern.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.125
GPT teacher head0.329
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2005
Admission routes1
Has abstractyes

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