Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes-Reference-Cited by-同舟云学术

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Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

Published:2023 Issue: Volume:11 Page:8761-8772
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Song Jianguo¹^ORCID,Ntibahanana Munezero¹^ORCID,Luemba Moise¹,Tondozi Keto²,Imani Gloire³^ORCID

Affiliation:

1. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong, China

2. Faculté de Sciences, Université de Kinshasa, Kinshasa, DR, Congo

3. School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong, China

Funder

National Science Foundation of China

Major Scientific and Technical Projects of China National Petroleum Corporation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10025713.pdf?arnumber=10025713

Reference49 articles.

1. Geostatistical inversion of prestack seismic data for the joint estimation of facies and impedances using stochastic sampling from Gaussian mixture posterior distributions

2. Seismic identification of tight-oil reservoirs by using 3D rock-physics templates

3. Seismic estimation of reservoir properties with Bayesian evidential analysis

4. Narrow‐band spectral analysis and thin‐bed tuning

5. Estimation of microfracture porosity in deep carbonate reservoirs based on 3D rock-physics templates

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation;Earth and Space Science;2024-06

2. Prestack Porosity Direct Inversion Based on Logging Constraints;Springer Series in Geomechanics and Geoengineering;2024

3. Porosity prediction in tight sandstone reservoirs based on a one–dimensional convolutional neural network–gated recurrent unit model;Applied Geophysics;2023-12-14

4. Porosity prediction from prestack seismic data via deep learning: incorporating a low-frequency porosity model;Journal of Geophysics and Engineering;2023-09-01

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