Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Applied Geophysics Pub Date : 2024-07-17 DOI:10.1016/j.jappgeo.2024.105453
M. Khalili , P. Göransson , J.S. Hesthaven , A. Pasanen , M. Vauhkonen , T. Lähivaara
{"title":"Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study","authors":"M. Khalili ,&nbsp;P. Göransson ,&nbsp;J.S. Hesthaven ,&nbsp;A. Pasanen ,&nbsp;M. Vauhkonen ,&nbsp;T. Lähivaara","doi":"10.1016/j.jappgeo.2024.105453","DOIUrl":null,"url":null,"abstract":"<div><p>A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic–viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams–Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water volume. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water volume estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"229 ","pages":"Article 105453"},"PeriodicalIF":2.2000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0926985124001691/pdfft?md5=601434ea8f6386330f635ccd4f7550ef&pid=1-s2.0-S0926985124001691-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926985124001691","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic–viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams–Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water volume. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water volume estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用地震数据监测多孔储层的水量:三维模拟研究
根据地震数据估算多孔储层储水量的潜在框架是神经网络。在本研究中,人造地下水储层被建模为一种耦合的多孔弹性-粘弹性介质,并使用三维非连续伽勒金方法和亚当斯-巴什福斯时间步进方案来解决基本的波传播问题。波浪问题求解器用于为基于神经网络的机器学习模型生成数据库,以估算水量。在数值示例中,我们研究了一种基于解卷积的方法,以归一化源小波的影响,以及网络对噪声水平的容忍度。我们还应用了 SHapley Additive exPlanations 方法,以便更深入地了解输入数据中对水量估算贡献最大的部分。数值结果证明了全连接神经网络估算多孔水库储水量的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
期刊最新文献
Magnetic diagnosis model for heavy metal pollution in beach sediments of Qingdao, China An improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra Deep learning-based geophysical joint inversion using partial channel drop method Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques Editorial Board
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1