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Record W2949491960 · doi:10.2118/193702-pa

Simultaneous Occurrence of Miscible and Immiscible Displacement Processes During Solvent Displacement of Heavy Oil: A Parametric Analysis Using Visual Capillary-Tube Experiments

2019· article· en· W2949491960 on OpenAlexafffund
Yu Shi, Tayfun Babadagli

Bibliographic record

VenueSPE Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapillary actionMass transferViscosityImbibitionSolventDisplacement (psychology)DiffusionMixing (physics)WettingChemistryDilutionMaterials scienceEnhanced oil recoverySurface tensionChemical engineeringThermodynamicsChromatographyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Summary Oil/solvent mixing is essential during solvent-injection applications to reduce the viscosity of oil, but mass transfer by diffusion becomes slower because the oil becomes heavier. Thus, an interface exists between the oil and solvent at certain times, being stronger in the beginning of the process. This results in an immiscible displacement controlled by the capillary forces while mixing is in progress. It is of practical and fundamental importance to determine the mechanisms responsible for the displacement of heavy oil and the behavior of solvents (acting as both immiscible and miscible displacement agents) because it could be advantageous to accelerate the dilution of heavy oil in many circumstances, including heterogeneous (fractured, layered, wormholed) systems. This is a complex process consisting of multiple pore phases (oil, solvent, their mixtures, and aqueous and vapor phases) at the same time, while different mechanisms such as capillary imbibition, miscible interaction (diffusion and convection), and gravity also act simultaneously. To investigate this complex phenomenon for different oil/solvent systems, a novel experimental method was used. The underlying mechanisms that dominate the solvent displacement process were comprehensively identified. The movement and evolution of interfaces among different fluid phases in glass capillary tubes were observed and recorded. Oil samples with different viscosities were used to examine the effects of oil viscosity on the mass transfer accelerated by imbibition transfer. The effects of temperature, wettability, and boundary conditions on the interaction of miscible fluid pairs were also studied. Pentane, heptane, and decane were used as the solvent phases. Advanced photographic techniques using ultraviolet (UV) light and dyed fluids were applied to better track the flow of different phases in the mixing zone. The experiments demonstrated a slowly smearing interface between the solvent and viscous oil. A unique natural convection was induced, with the combined effect of gravity, diffusion (mixing), and capillarity all contributing to the recovery of heavy oil. On the basis of the saturation method, boundary condition, and the Bond number, four different motion modes of mixing zone and interfaces of miscible fluids in the capillary tube were revealed and categorized to identify the degree of interface development (immiscible flooding). Also, the mixing zone, mass flux, and flow behavior were quantified using dimensionless parameters. The results indicate that priority may be given to a solvent with a high interfacial tension (IFT) for the solvent-based oil-recovery technique because of a strong imbibition and further enhancement of the dilution and displacement processes under conditions of a similar viscosity ratio. The data provided will be useful for the accuracy of modeling studies, especially for complex geologies where oil/solvent interaction is critically difficult to develop in order for mixing to occur.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.279
Teacher spread0.270 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

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