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Record W2488924613 · doi:10.2118/144546-ms

Mechanics of Heavy Oil and Bitumen Recovery by Hot Solvent Injection

2011· article· en· W2488924613 on OpenAlexafffund
Varun Pathak, Tayfun Babadagli, Neil Edmunds

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsLaricina Energy (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphalteneVapoursPropaneSolventAsphaltOil in placePorous mediumMaterials sciencePetroleum engineeringButaneViscous fingeringLight crude oilChemical engineeringPorosityPetroleumAnalytical Chemistry (journal)ChemistryComposite materialChromatographyGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In our earlier works (Pathak et al., 2010; 2011), we presented the initial results for heavy oil and bitumen recovery using heated solvent vapours. The heavy oil and bitumen saturated sand pack samples of different heights were exposed to heated vapours of butane or propane at a constant temperature and pressure for an extended duration of time. The produced oil was analyzed for recovery, asphaltene content, viscosity, composition and refractive index. Recovery was found to be very sensitive to temperature and pressure. The current work was undertaken to better understand the physics of the process and to explain the observations of the earlier experiments using additional experiments on tighter samples of different sizes, numerical simulation and visualization experiments. The effects of temperature and pressure on the recovery were studied using a commercial reservoir simulator. Propane and butane were used as solvents. Asphaltene precipitation was also modeled. A qualitative history match with the experiments on different porous media types was achieved by mainly considering the permeability reduction due to asphaltene precipitation, pore plugging, the extent of interaction between solvent and oil phase, and the parameters like model height, vertical permeability and gravity. To investigate the phenomenon further, visualization experiments were performed. 2-D Hele-Shaw models were constructed by joining two plexiglass sheets from three sides, leaving some space in between to accommodate oil. The models were saturated with heavy-oil and left open from one side and were exposed to different types of solvents from this side. The setup was continuously monitored to observe fluid fronts and asphaltene precipitation. Using this analysis, the mechanics of the process was clarified from the effect of solvent type on the recovery process. The optimum operating temperature for the hot solvent process and the dominant mechanisms were identified.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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 designNot applicable
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

Citations20
Published2011
Admission routes2
Has abstractyes

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