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Record W2027627894 · doi:10.2118/157830-pa

Investigation of Emulsion Flow in Steam-Assisted Gravity Drainage

2013· article· en· W2027627894 on OpenAlexafffund
C. C. Ezeuko, J.. Wang, Ian D. Gates

Bibliographic record

VenueSPE Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSteam-assisted gravity drainageEmulsionPetroleum engineeringAsphaltMultiphase flowSteam injectionViscosityPorous mediumPermeability (electromagnetism)Oil viscosityFlow (mathematics)Oil sandsMaterials scienceEnvironmental scienceChemical engineeringGeologyPorosityGeotechnical engineeringChemistryMechanicsEngineeringComposite material

Abstract

fetched live from OpenAlex

Summary We present a numerical simulation approach that allows incorporation of emulsion modeling into steam-assisted gravity-drainage (SAGD) simulations with commercial reservoir simulators by means of a two-stage pseudochemical reaction. Numerical simulation results show excellent agreement with experimental data for low-pressure SAGD, accounting for approximately 24% deficiency in simulated oil recovery, compared with experimental data. Incorporating viscosity alteration, multiphase effect, and enthalpy of emulsification appears sufficient for effective representation of in-situ emulsion physics during SAGD in very-high-permeability systems. We observed that multiphase effects appear to dominate the viscosity effect of emulsion flow under SAGD conditions of heavy-oil (bitumen) recovery. Results also show that in-situ emulsification may play a vital role within the reservoir during SAGD, increasing bitumen mobility and thereby decreasing cumulative steam/oil ratio (cSOR). Results from this work extend understanding of SAGD by examining its performance in the presence of in-situ emulsification and associated flow of emulsion with bitumen in porous media.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.311

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)0.0000.000

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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 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

Citations28
Published2013
Admission routes2
Has abstractyes

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