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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Air-foam flooding is an effective technology to enhance oil recovery in heavy oil production. In this study, Back Propagation (BP) neural network model is built and optimized to predict the recovery effect of heavy oil with the injection parameters data of the oxygen-reduced air-foam flooding obtained through experiments. The study shows that the prediction results of the BP neural network is quite accurate, and the average absolute relative error of prediction results is less than 0.21%. This also illustrates the feasibility of using the BP network prediction model in predicting heavy oil recovery effect.

Application of BP neural network to prediction of recovery effect of air-foam flooding in heavy oil