Predicting field production rates for waterflooding using a machine learning-based proxy model-Reference-Cited by-同舟云学术

Predicting field production rates for waterflooding using a machine learning-based proxy model

Published:2020-11 Issue: Volume:194 Page:107574
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Zhong Zhi,Sun Alexander Y.,Wang Yanyong,Ren Bo

Funder

Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference65 articles.

1. Significance of capillary forces during low-rate waterflooding;Aghaeifar;Energy Fuels,2019

2. Effect of pore-scale heterogeneity and capillary-viscous fingering on commingled waterflood oil recovery in stratified porous media;Al-Shalabi;J. Pet. Eng.,2016

3. New insights of low salinity water flooding in low permeability-low porosity clay rich sandstone reservoir;Alhuraishawy,2018

4. Mineral dissolution and fine migration effect on oil recovery factor by low-salinity water flooding in low-permeability sandstone reservoir;Alhuraishawy;Fuel,2018

5. The role of capillary-pressure in improving the numerical simulation of multi-phase flow in porous media;Alzayer,2017

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