A physics-constrained data-driven workflow for predicting bottom hole pressure using a hybrid model of artificial neural network and particle swarm optimization-Reference-Cited by-同舟云学术

A physics-constrained data-driven workflow for predicting bottom hole pressure using a hybrid model of artificial neural network and particle swarm optimization

Published:2023-05 Issue: Volume:224 Page:211625
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Zhu Zhaopeng,Liu Zihao,Song Xianzhi^ORCID,Zhu Shuo,Zhou Mengmeng,Li Gensheng,Duan Shiming,Ma Baodong^ORCID,Ye Shanlin,Zhang Rui^ORCID

Funder

China National Petroleum Corporation

Science Foundation of China University of Petroleum, Beijing

National Key Research and Development Program of China

Ministry of Science and Technology of the People's Republic of China

China University of Petroleum, Beijing

Publisher

Elsevier BV

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