Applying a deep-learning surrogate model to simulate and compare achievable oil recovery by different waterflood scenarios-Reference-Cited by-同舟云学术

Applying a deep-learning surrogate model to simulate and compare achievable oil recovery by different waterflood scenarios

Published:2023-10-04 Issue: Volume: Page:1-19
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Singh Vishal¹,Ruwali Nabindra¹,Pandey Rakesh Kumar¹^ORCID,Vaferi Behzad²³^ORCID,Wood David A.⁴

Affiliation:

1. School of Engineering and Technology, DIT University, Dehradun, India

2. Department of Chemical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

3. Department of Advanced Calculations, Chemical, Petroleum, and Polymer Engineering Research Center, Shiraz Branch, Islamic Azad University, Shiraz, Iran

4. DWA Energy Limited, Lincoln, United Kingdom

Publisher

Informa UK Limited

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2023.2264902

Reference52 articles.

1. Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

2. Modeling the CO2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks

3. Estimating the density of deep eutectic solvents applying supervised machine learning techniques

4. Prediction of absorption and stripping factors in natural gas processing industries using feedforward artificial neural network

5. Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media

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