Shale oil content evaluation and sweet spot prediction based on convolutional neural network-Reference-Cited by-同舟云学术

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Shale oil content evaluation and sweet spot prediction based on convolutional neural network

Published:2024-09 Issue: Volume:167 Page:106997
ISSN:0264-8172
Container-title:Marine and Petroleum Geology
language:en
Short-container-title:Marine and Petroleum Geology

Author:

Wu Yuqi,Jiang Fujie,Hu Tao,Xu Yunlong,Guo Jing,Xu Tianwu,Xing Hailong,Chen Di,Pang Hong,Chen Junqing,Zhu Chenxi

Publisher

Elsevier BV

Reference65 articles.

1. Evaluation on hydrocarbon source rocks in diferent environments and characteristics of hyarocarbon generation and expulsion in Dongpu Depression;Chen;Fault-Block Oil Gas Field,2012

2. Geochemical characteristics of the paleogene shales in the dongying depression, eastern China;Chen;Mar. Petrol. Geol.,2016

3. A data driven model for studying kerogen kinetics with application examples from Canadian sedimentary basins;Chen;Mar. Petrol. Geol.,2015

4. Calculation of petroleum masses generated and expelled from source rocks;Cooles;Org. Geochem.,1986

5. Breakthrough pressure anisotropy and intra-source migration model of crude oil in shale;Cui;Mar. Petrol. Geol.,2022

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