Bedding-parallel fracture density prediction using graph convolutional network in continental shale oil reservoirs: A case study in Mahu Sag, Junggar basin, China-Reference-Cited by-同舟云学术

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Bedding-parallel fracture density prediction using graph convolutional network in continental shale oil reservoirs: A case study in Mahu Sag, Junggar basin, China

Published:2024-09 Issue: Volume:167 Page:106992
ISSN:0264-8172
Container-title:Marine and Petroleum Geology
language:en
Short-container-title:Marine and Petroleum Geology

Author:

Lu Guoqing,Zeng Lianbo,Liu Guoping,Chen Xiaoxuan,Ostadhassan Mehdi,Du Xiaoyu,Chen Yangkang

Publisher

Elsevier BV

Reference60 articles.

1. Fractured zones detection using conventional petrophysical logs by differentiation method and its correlation with image logs;Aghli;J. Petrol. Sci. Eng.,2016

2. Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: case studies from the Appalachian basin, USA;Bhattacharya;J. Petrol. Sci. Eng.,2018

3. Random forests;Breiman;Maching Learning,2001

4. Evaluation of machine learning methods for lithology classification using geophysical data;Bressan;Comput. Geosci.,2020

5. The relationship between recall and precision;Buckland;J. Am. Soc. Inf. Sci.,1994

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