Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning-Reference-Cited by-同舟云学术

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Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

Published:2022-10-09 Issue: Volume: Page:
ISSN:0072-1050
Container-title:Geological Journal
language:en
Short-container-title:Geological Journal

Author:

Wang Zhao¹^ORCID,Tang Hongming¹^ORCID,Hou Yawei²,Shi Hongfu²,Li Jingling²,Yang Tao¹,Feng Yutian¹,Meng Wang³⁴

Affiliation:

1. School of Geoscience and Technology Southwest Petroleum University Chengdu China

2. Tianjin Branch of China National Offshore Oil Co., Ltd. Tianjin China

3. School of Chemistry and Chemical Engineering Chongqing University of Science and Technology Chongqing China

4. Key Laboratory of Shale Gas Exploration, Ministry of Natural Resources Chongqing Institute of Geology and Mineral Resources Chongqing China

Publisher

Wiley

Subject

Geology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/gj.4604

Reference61 articles.

1. Incorporating capillary pressure, pore throat aperture radii, height above free-water table, and Winland r35 values on Pickett plots

2. Early Cenozoic two-phase extension and late Cenozoic thermal subsidence and inversion of the Bohai Basin, northern China

3. Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

4. Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

Cited by 3 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. Analysis of damage mechanisms and controlling factors of fine particle migration in unconsolidated sandstone reservoirs based on reservoir classification;Energy Science & Engineering;2024-06-27

2. Probabilistic classification of surrounding rock mass of tunnels based on fully Bayesian Gaussian process classification (fB‐GPC) with consideration of influencing factors selection;Geological Journal;2024-01-29

3. Application of artificial intelligence in geotechnical and geohazard investigations;Geological Journal;2023-05-23

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