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Record W2894743489 · doi:10.2118/183894-pa

Water Ion Interactions at Crude-Oil/Water Interface and Their Implications for Smart Waterflooding in Carbonates

2018· article· en· W2894743489 on OpenAlexafffund
Subhash Ayirala, Ali Al‐Yousef, Zuoli Li, Zhenghe Xu

Bibliographic record

VenueSPE Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsSurface tensionEnhanced oil recoveryCoalescence (physics)Capillary actionPetroleum engineeringBrineChemical engineeringMaterials scienceChemistryGeologyComposite materialOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Summary Smart waterflooding (SWF) through tailoring of injection-water salinity and ionic composition is receiving favorable attention in the industry for both improved and enhanced oil recovery (EOR) in carbonate reservoirs. Surface/intermolecular forces, thin-film dynamics, and capillary/adhesion forces at rock/fluid interfaces govern crude-oil liberation from pores. On the other hand, stability and rigidity of oil/water interfaces control the destabilization of interfacial film to promote coalescence between released oil droplets and to improve the oil-phase connectivity. As a result, the dynamics of oil recovery in smart waterflood is caused by the combined effect of favorable interactions occurring at both oil/brine and oil/brine/rock interfaces across the thin film. Most of the laboratory studies reported so far have been focused on only studying the interactions at rock/fluid interfaces. However, the other important aspect of characterizing water ion interactions at the crude oil/water interface and their impact on film stability and oil-droplet coalescence remains largely unexplored. A detailed experimental investigation was conducted to understand the effects of different water ions at the crude-oil/water interface by using several instruments such as Langmuir trough, interfacial shear rheometer, Attension tensiometer, and coalescence time-measurement apparatus. The reservoir crude oil and four different water recipes with varying salinities and individual ion concentrations were used. Interfacial tension (IFT), interface pressures, compression energy, interfacial viscous and elastic moduli, oil-droplet crumpling ratio, and coalescence time between crude-oil droplets are the major experimental data measured. The IFTs are found to be the largest for deionized (DI) water, followed by the 10-times-reduced-salinity seawater and 10-times-reduced-salinity seawater enriched with sulfates. Interfacial pressures gradually increased with compressing surface area for all the brines and DI water. The compression energy (integration of interfacial pressure over the surface-area change) is the highest for DI water, followed by the lower-salinity brine containing sulfate ions, indicating rigid interfaces. The transition times of interfacial layer to become elastic-dominant from viscous-dominant structures are found to be much shorter for brines enriched with sulfates, once again confirming the rigidity of interface. The crumpling ratios (oil drop wrinkles when contracted) are also higher with the two recipes of DI water and sulfates-only brine to indicate the same trend and to confirm elastic rigid skin at the interface. The coalescence time between oil droplets was the least in brines containing sufficient amounts of magnesium and calcium ions, while the highest in DI water and sulfate-rich brine, respectively. These results, therefore, showed a good correlation of coalescence times with the rigidity of oil/water interface, as interpreted from different measurement techniques. This study, thereby, integrates consistent results obtained from different measurement techniques at the crude-oil/water interface to demonstrate the importance of both salinity and certain ions, such as magnesium and calcium, on crude-oil-droplets coalescence, and to improve oil-phase connectivity in smart waterflood.

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 score0.019
Threshold uncertainty score0.354

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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations47
Published2018
Admission routes2
Has abstractyes

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