Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools-Reference-Cited by-同舟云学术

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Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

Published:2024-04-25 Issue:18 Volume:9 Page:20397-20409
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Al-Darweesh Jawad¹,Aljawad Murtada Saleh¹²^ORCID,Tariq Zeeshan³,Alajmei Shabeeb¹²^ORCID,Yan Bicheng³^ORCID,Kamal Muhammad Shahzad¹²^ORCID

Affiliation:

1. College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals (KFUPM), Dharahan 31261, Saudi Arabia

2. Center for Integrative Petroleum Research, King Fahd University of Petroleum and Minerals, 31261 Dhahran, Saudi Arabia

3. Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.4c00965

Reference60 articles.

1. Integration of field, laboratory, and modeling aspects of acid fracturing: A comprehensive review

2. Technology Focus: Hydraulic Fracturing (March 2018)

3. Current opinions on foam-based hydro-fracturing in deep geological reservoirs

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