An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network

The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters. Therefore, to quantitatively evaluate the relative importance of model parameters on the production forecasti...

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Bibliographic Details

Published in:	Petroleum Science
Main Author:	Liang Xue
Other Authors:	Shuai Xu, Jie Nie, Ji Qin, Jiangxia Han, Yue-Tian Liu, Qinzhuo Liao
Format:	Article
Language:	English
Published:	Elsevier BV 2024
Subjects:	Reservoir Engineering and Simulation Methods Hydrocarbon exploration and reservoir analysis Hydraulic Fracturing and Reservoir Analysis shale gas production global sensitivity analysis convolutional neural networks Sobol indices surrogate models production forecasting reservoir thickness well length parameter ranking
Online Access:	https://doi.org/10.1016/j.petsci.2024.02.010
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