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Record W2110532901 · doi:10.2118/03-02-04

Investigation of the VAPEX Process Using CT Scanning and Numerical Simulation

2003· article· en· W2110532901 on OpenAlexfundno aff
David Cuthiell, Christine McCarthy, T. Frauenfeld, Scott E. Cameron, G. Kissel

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersSuncor Energy Incorporated
KeywordsMixing (physics)Viscous fingeringDispersion (optics)Petroleum engineeringSolventMechanicsSteam-assisted gravity drainageProcess (computing)Porous mediumMaterials sciencePorosityGeologyChemistryComputer sciencePhysicsComposite materialOil sandsOptics

Abstract

fetched live from OpenAlex

Abstract The "VAPEX" process, a solvent analogue of Steam Assisted Gravity Drainage, has attracted considerable attention as a recovery method for heavy oil. However, to date, there are still many questions about the nature and magnitude of basic process mechanisms, and whether the process can produce economic oil rates. The experiments discussed in this paper were aimed at quantifying some of the basic mechanisms, in particular the dispersive mixing mechanism. We have performed a series of topdown solvent injection experiments under varying conditions, utilizing a CT scanner to monitor fluid movements. All of the displacements we have observed are gravity-unstable in the early stages, and characterized by viscous fingering of the solvent into the 5,500 cP oil. After solvent breakthrough, the displacements become stable, dominated by a single solvent finger which has many of the features of a VAPEX solvent chamber. The "mixing parameter" we infer for these experiments using the Butler/Mokrys analytic model is higher than that reported for Hele-Shaw VAPEX experiments. An analysis of localized fluid velocities in the experiments using numerical simulation shows that the enhanced mixing parameter can be understood as a consequence of convective dispersion in the porous medium. By adjusting the amount of physical dispersion, the simulations can match breakthrough time, post-breakthrough oil rates, and the general character of the fingering. A novel type of "quasi-pore scale" simulation grid appears to provide advantages in simulating the unstable period at the beginning of the displacements. Introduction Compared with steam-based processes such as Steam Assisted Gravity Drainage (SAGD) for recovery of heavy oil, solventbased processes offer the possibility of reduced energy consumption and greenhouse gas production. However, they are mechanistically complex, and questions remain regarding their expected performance. To date, no field data are publicly available to answer these questions. One solvent-based process that has been proposed is the Vapour Extraction (VAPEX) process(1, 2). This solvent analogue of SAGD utilizes gravity as the driving agent, and solvent dilution of the heavy oil as the mobilization mechanism. The concept of the process is illustrated in Figure 1. A practical, solvent-based recovery process will depend for its success on the interplay of a number of phenomena. Some of the most important of these are: diffusion/dispersion, viscous fingering, capillary-driven mixing (in the case of a gaseous solvent), and the effects of reservoir heterogeneity. The first three are accessible for study in the laboratory, and understanding their interplay at the laboratory scale is a first step toward predicting their effects in a field process. Numerical simulation is required both to extrapolate laboratory experience to the field scale, and to incorporate the effects of reservoir heterogeneity. The study described in this paper addresses the phenomena of diffusion/dispersion and viscous fingering based on a series of laboratory experiments, combined with numerical simulation. Our initial experiments utilized a liquid solvent; therefore capillary mixing effects were absent. Future work will extend the results to gaseous solvents.

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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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.228
Teacher spread0.216 · 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

Citations33
Published2003
Admission routes1
Has abstractyes

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