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Record W4411210845 · doi:10.3390/pr13061843

An Experimental Investigation of the Effect of Pressure and Salinity on IFT in Live Oil/Brine Systems

2025· article· en· W4411210845 on OpenAlexaff
Deniz M. Paker, Birol Dindoruk, Leslie Baksmaty, Ram R. Ratnakar, Hanin Samara, Philip Jaeger

Bibliographic record

VenueProcesses · 2025
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsBrineSalinityPetroleum engineeringEnvironmental scienceChemistryGeologyOceanography

Abstract

fetched live from OpenAlex

Residual oil saturation in reservoirs is primarily influenced by viscous and capillary forces, with interfacial tension (IFT) being a critical factor in fluid distribution due to capillary pressure. Adjusting IFT is essential for enhancing oil recovery, particularly in waterflooding, which is the most common secondary recovery technique after primary production. The salinity of injected water directly affects the IFT between crude oil and brine, making it a crucial factor in optimizing recovery. However, limited studies have examined IFT using live oil samples under actual reservoir conditions. In this study, a high-pressure, high-temperature (HPHT) drop shape analyzer was used to measure the IFT between live oil and brine under reservoir conditions. Five live oil samples and two sodium chloride (NaCl) brine concentrations (30,000 and 100,000 ppm) were tested at a reservoir temperature of 180 °F. Measurements were conducted above the bubble points of the oils, replicating undersaturated reservoir conditions. The results revealed that the impact of pressure on IFT was more complex than that of salinity. IFT generally decreased with increasing pressure but showed mixed behavior across different samples. Conversely, IFT consistently increased with higher salinity. These findings enhance the understanding of IFT behavior under reservoir conditions, supporting improved reservoir simulations and oil recovery strategies.

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 score0.345
Threshold uncertainty score0.200

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 head0.005
GPT teacher head0.247
Teacher spread0.241 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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