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Record W4232674657 · doi:10.2118/2009-108

Numerical Simulation and Economic Evaluation of Hybrid Solvent Processes

2009· article· en· W4232674657 on OpenAlexaboutno aff
T. Frauenfeld, C. Jossy, J. Ivory

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsSolventComputer scienceComputer simulationChemistrySimulationOrganic chemistry

Abstract

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Abstract Solvent-based processes for recovery of heavy oil and bitumen have potential application to a variety of reservoir situations. Potential processes range from SAGD to VAPEX, with a range of hybrid processes in between. Over 50 lab-scale and 80 field scale simulations were run to determine optimum operating points for various hybrid processes. The results showed that steam-butane simulations yielded two "sweet spots? where the cost objective function was lower than that for SAGD. Economic analysis was done based on a set of field scale simulations. This analysis showed that a hybrid solvent process for an Athabasca reservoir was an alternative to SAGD. The analysis may be extended to other reservoir types as needed. Introduction SAGD is the main commercial technology used for in-situ recovery of Athabasca bitumen. Due to the increasing costs for energy (natural gas) and the increasing restrictions on fresh water usage, VAPEX (Ref. 1) has been proposed. The VAPEX process may be augmented by adding heat. Heating will reduce the oil viscosity sufficiently to produce a large increase in oil rate. The heat will serve to speed the diffusion of solvent into the oil. The heat will also serve to initiate communication between the injector and the producer. Heat may be injected by using vaporized solvent or steam. Because of the low latent heat capacity of solvent, it is expedient to heat the solvent by co-injection of steam. The result is a Hybrid Solvent process (Figure 1). This process may be operated at any set of steam and solvent rates between pure SAGD and pure VAPEX. Detailed experimental, modelling and economic studies were done to determine an optimum point or points for this process. Numerical 2D field-scale simulations were used to compare VAPEX, SAGD and Hybrid solvent processes for an Athabasca bitumen reservoir. The comparisons considered propane, n-butane and n-pentane as solvents, and considered effects of steam rate, solvent rate, pressure and steam sub-cool setting of the production well. The results are displayed in more detail in the following figures. Scaled Laboratory Models for Heavy Oil Recovery The Scaling Theory The numerical simulations were based on experiments done at Alberta Research Council to model the Steam-Solvent Hybrid process. Figure 2 shows a photo of the experimental apparatus. Figure 3 shows a diagram of the experimental model. The scaling criteria used for ARC lab model experiments on thermal processes are the Pujol and Boberg scaling criteria (Ref. 2). This set of scaling criteria matches the ratios of gravity, viscous forces, conductive and convective heat transfer, and diffusion, at the expense of incorrectly scaling pressure drop vs. capillary forces, and dispersion vs. diffusion. This scaling method is acceptable for SAGD, where thermal conduction is the rate-controlling step. The scaling will be less certain for VAPEX and hybrid solvent processes, where diffusion and dispersion play major roles in controlling process rates. Heat transfer, diffusion and dispersion are all important in hybrid solvent processes. These values must be determined experimentally. Experimental values of diffusion as a function of temperature are not yet available.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.264
Teacher spread0.240 · 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 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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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