Bidirectional Long Short-term Neural Network Based on the Attention Mechanism of the Residual Neural Network (ResNet–BiLSTM–Attention) Predicts Porosity through Well Logging Parameters-Reference-Cited by-同舟云学术

Bidirectional Long Short-term Neural Network Based on the Attention Mechanism of the Residual Neural Network (ResNet–BiLSTM–Attention) Predicts Porosity through Well Logging Parameters

Published:2023-06-21 Issue:26 Volume:8 Page:24083-24092
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Sun Youzhuang¹^ORCID,Zhang Junhua¹,Yu Zhengjun²,Zhang Yongan³,Liu Zhen¹^ORCID

Affiliation:

1. College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China

2. Shengli Oilfield Geophysical Research Institute, Dongying 257000, China

3. College of Computer Science, China University of Petroleum, Qingdao 266555, China

Funder

National Natural Science Foundation of China

National fund for studying abroad

China University of Petroleum

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c03247

Reference34 articles.

1. Experimental Investigation about Gas Transport in Tight Shales: An Improved Relationship between Gas Slippage and Petrophysical Properties

2. Geological Factors Influencing Reservoir Performance of the Hartzog Draw Field, Wyoming

3. Pore pressure prediction and modeling using well-logging data in one of the gas fields in south of Iran

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