Lithofacies identification of shale reservoirs using a tree augmented Bayesian network: A case study of the lower Silurian Longmaxi formation in the changning block, South Sichuan basin, China

Author:

Zhao ZhenduoORCID,Su Siyuan,Shan Xuanlong,Li Xuesong,Zhang Jiahao,Jing Cui,Ren Hongjia,Li Ang,Yang Qin,Xing Jian

Publisher

Elsevier BV

Reference59 articles.

1. Automated lithology classification from drill core images using convolutional neural networks;Alzubaidi;J. Petrol. Sci. Eng.,2020

2. Data-driven machine learning approach to predict mineralogy of organic-rich shales: an example from Qusaiba Shale, Rub’ al Khali Basin, Saudi Arabia;Am;Mar. Petrol. Geol.,2022

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

4. Noble gas isotopic variations and geological implication of Longmaxi shale gas in Sichuan Basin, China;Cao;Mar. Petrol. Geol.,2018

5. Lithofacies types and reservoirs of paleogene fine-grained sedimentary rocks dongying sag, Bohai Bay basin, China;Chen;Petrol. Explor. Dev.,2016

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