English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The increasing importance of CO 2  in reservoir engineering demands more accurate models for predicting interfacial tension (IFT). We investigated the use of neural networks to predict the IFT of ternary systems CO 2 /CH 4 /Water-Brine in a wide range of reservoir conditions, since the existing correlations do not capture all of the system's mechanisms. We introduced an optimization associated with k-fold cross validation to find optimal architecture and ensure an unbiased model. The average absolute relative error obtained is 1.33% (water) and 1.99% (brine) considerably more accurate than the best empirical models tested: 9.87% (water) and 19.11% (brine). Molecular dynamics simulation performed confirms the high superficial activity of CO 2 .

Neural network protocol to predict interfacial tension for CO 2 /CH 4 /Water-Brine ternary systems under reservoir temperature and pressure ranges