气候变化领域集成服务门户(CCPortal): Shale Anisotropy Model Building Based on Deep Neural Networks

CCPortal

DOI	10.1029/2019JB019042
	Shale Anisotropy Model Building Based on Deep Neural Networks
	You N.; Li Y.E.; Cheng A.
发表日期	2020
ISSN	21699313
卷号	125 期号:2
英文摘要	Seismic anisotropy parameters are essential in the processing and interpretation of modern array data with multicomponent, long offsets and wide azimuth acquisitions. Traditional well logs do not measure anisotropy in a vertical well and thus cannot provide the needed information. Conventional calibration-based as well as recent inversion-based rock physics modeling methods involve tuning parameters and subjective choices that are largely empirical and difficult to generalize. Here we present a machine learning approach to alleviate these problems. Since it is impossible to collect massive labeled field well log data, we generate paired synthetic data of features (porosity, density, vertical (Formula presented.) and (Formula presented.) wave velocities, (Formula presented.) wave and shear moduli) and labels (bulk and shear moduli of rock matrices and aspect ratio of ellipsoidal cracks). By tuning hyperparameters we obtain an optimal fully connected neural network with four hidden layers that fits well with the synthetic data. The neural network is applied to published laboratory measurements and field well log data from a Chinese well and a U.S. well without any modification. We show that anisotropy models estimated by the deep neural network agree well with the inversion results and with the laboratory measurements. The neural network optimized by extensive training based on massive synthetic data removes the subjectivity in parameter selection, generalizes to different geological environments, and has the potential to provide real-time anisotropy estimation while logging. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	anisotropy; deep neural network; Hudson-Cheng model; machine learning; shale
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187967
作者单位	Department of Civil and Environmental Engineering, National University of Singapore, Singapore
推荐引用方式 GB/T 7714	You N.,Li Y.E.,Cheng A.. Shale Anisotropy Model Building Based on Deep Neural Networks[J],2020,125(2).
APA	You N.,Li Y.E.,&Cheng A..(2020).Shale Anisotropy Model Building Based on Deep Neural Networks.Journal of Geophysical Research: Solid Earth,125(2).
MLA	You N.,et al."Shale Anisotropy Model Building Based on Deep Neural Networks".Journal of Geophysical Research: Solid Earth 125.2(2020).