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Record W3010647323 · doi:10.2118/199909-ms

Effects of Oil Viscosity and Injection Velocity on Imbibition Displacement in Sandstones

2020· article· en· W3010647323 on OpenAlexaff
Danial Arab, Apostolos Kantzas, Steven L. Bryant

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImbibitionCapillary actionViscosityResidual oilSaturation (graph theory)Water injection (oil production)Capillary numberPetroleum engineeringViscous fingeringCapillary pressureChemistryGeotechnical engineeringMaterials scienceGeologyMechanicsPorous mediumComposite materialPorosityPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract Water flooding has been applied for more than seventy years in both conventional and unconventional heavy oil reservoirs. Although it is generally accepted that the mechanisms of water flooding in heavy oil systems are totally different than that of light oil reservoirs, there is not a systematic study to specifically investigate water flooding in heavy oil systems. This article presents the findings of core flooding experiments in water-wet systems and gives some insights on the interplay between capillary and viscous forces in imbibition displacement processes. Seven different oils of various viscosities, ranging from 1 to 15,000 mPa.s at 25 °C, were used in nineteen core flooding experiments where injection velocity was changed from 0.7 to 24.3 ft/D (2.5 × 10−6 m/s to 86.0 × 10−6 m/s). An in-line densitometer was used to precisely determine breakthrough time. Capillary forces and instability analysis were used to quantify the balance between viscous and capillary forces. On physical grounds, the capillary number, which is the relative magnitude of viscous and capillary forces during a displacement, should be the first-order influence on residual oil saturation. However, Abrams (Abrams, 1975) showed that accounting for the viscosity ratio improves the correlation to residual oil saturation for oil viscosity below 37 mPa.s. Our observations extend the range of oil viscosity to 15,000 mPa.s and when combined with 178 datasets from the literature indicate that viscosity ratio has much more influence than capillary number on residual oil saturation. Standard models such as Buckley-Leverett theory predict that oil recovery at water breakthrough depends only on phase mobilities. However, our observations indicate that flow velocity also influences breakthrough oil recovery. At oil to water viscosity ratios smaller than 20, breakthrough oil recovery monotonically increases with increasing injection velocity. For intermediate viscosity ratios (20 < μo < 160), breakthrough oil recovery increases with decreasing injection velocity. At higher values of viscosity ratios, breakthrough oil recovery is almost independent of injection velocity. In these cases, late time oil recovery remarkably increases with decreasing injection velocity. This effect is more pronounced in more viscous oil systems suggesting the importance of imbibition in these systems. Our observations prove that water flooding, if applied at the most optimized mode that is a strong function of oil viscosity, can still be a very efficient EOR technique.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.004
GPT teacher head0.199
Teacher spread0.194 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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