深度学习在南川页岩气含气量预测中的应用

doi:10.11720/wtyht.2021.1507

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Abstract

Gas content is one of the main parameters to evaluate whether shale gas can be enriched to obtain high-yield.The higher the gas content,the more favorable for shale gas wells to obtain high-yield.Traditional gas content seismic prediction methods are based on single-attribute,multi-attribute linear fitting or simple neural networks,and have low accuracy.The gas content prediction method is based on deep neural network.Through optimizing seismic attributes,optimizing the solution method and choosing the appropriate number of hidden layers,the number of neurons,and the number of iterations,a prediction model can be established to predict the gas content of shale,thus effectively improving the prediction accuracy of shale gas content and providing support for the geological evaluation of shale gas research areas and the deployment of shale gas horizontal wells.

Key words： deep learning shale gas shale gas content seismic prediction non-linear

Received: 03 November 2020 Published: 27 July 2021

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	Articles by authors
	Yong ZHANG
	Xiao-Dong MA
	Yan-Jing LI
	Jing-Shun CAI

Cite this article:

Yong ZHANG,Xiao-Dong MA,Yan-Jing LI, et al. The application of deep learning to the shale gas content prediction in Nanchuan(South Sichuan)[J]. Geophysical and Geochemical Exploration, 2021, 45(3): 569-575.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2021.1507 OR https://www.wutanyuhuatan.com/EN/Y2021/V45/I3/569

Structure of deep neural network

Structural characteristics map of Nanchuan area

Comprehensive histogram of the first section of Wufeng-Longmaxi Formation in Well A

Attribute number optimization diagram

Superimposed graph of actual measured curve and predicted curve

The gas content prediction profile of different types of prediction methods

The average gas content and structure superposition map of high-quality shale section

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