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Record W2043450261 · doi:10.2118/165467-ms

Experimental and Numerical Study of VAPEX at Elevated Temperatures

2013· article· en· W2043450261 on OpenAlexaff
Parnian Haghighat, Brij Maini, Jalal Abedi

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2013
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPetroleum engineeringAsphaltPropaneWork (physics)Materials scienceEnvironmental scienceGeotechnical engineeringThermodynamicsGeologyEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Incorporating some heat injection along with the solvent injection appears to be the most viable option for improving the drainage rate of VAPEX in extra-heavy oil formations. The obvious question then concerns the magnitude of temperature increase needed to make the drainage rate economical. The objective of this work was to examine the effect of temperature on VAPEX performance. VAPEX experiments under different operating conditions were conducted in a high-pressure physical model. Physical model was packed with 250 Darcy sand and saturated with Athabasca bitumen (Mackay River oil). Injecting propane at 0.817 MPa, temperatures from 40 to 60ºC were tested to investigate possible improvement to oil production rates. Experimental results were numerically simulated with a commercial compositional simulator, Computer Modelling Group's (CMG) GEM. CMG's WinProp module, along with available experimental data, was employed to model the phase behavior and properties of the propane / Athabasca bitumen system. By history matching the experimental production data, results were extended to wider ranges of temperature and VAPEX performance at elevated temperatures was investigated. According to the results, substantial improvement in the performance of VAPEX in reservoirs containing this type of oil would require increasing the reservoir temperature above 60ºC.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

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)0.0010.000

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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.

Study designBench or experimental
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

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

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