Unsupervised-learning based self-organizing neural network using multi-component seismic data: Application to Xujiahe tight-sand gas reservoir in China-Reference-Cited by-同舟云学术

Unsupervised-learning based self-organizing neural network using multi-component seismic data: Application to Xujiahe tight-sand gas reservoir in China

Published:2022-02 Issue: Volume:209 Page:109964
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Zhang Kai,Lin Niantian^ORCID,Tian Gaopeng,Yang Jiuqiang,Wang Deying,Jin Zhiwei

Funder

National Natural Science Foundation of China

Shandong Province Natural Science Foundation

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference63 articles.

1. Neural networks in geophysical applications;Baan;Geophysics,2000

2. Unsupervised machine learning applications for seismic facies classification;Chopra;Unconvent. Resourc. Technol. Conf.,2019

3. Characteristics of gas pool in the second member of Xujiahe formation in Xinchang structure, west Sichuan basin;Deng;Nat. Gas. Ind.,2008

4. Multiscale modeling of compartmentalized reservoirs using a hybrid clustering-based non-local approach;Esmaeilzadeh;J. Petrol. Sci. Environ.,2019

5. A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs;Esmaeilzadeh,2019

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