Relevance Based Transfer Learning for Reservoir Parameters Prediction with Logs

R. Shao, L. Xiao, G. Liao
{"title":"Relevance Based Transfer Learning for Reservoir Parameters Prediction with Logs","authors":"R. Shao, L. Xiao, G. Liao","doi":"10.3997/2214-4609.202112716","DOIUrl":null,"url":null,"abstract":"Traditional methods for reservoir parameters prediction with logs are based on petrophysics knowledge, such as volumetric models and response functions (Darwin and Julian, 2007). The advantage of those methods is that the relationship between the logs and reservoir parameters is clear, there is a theoretical basis, and it can be explained; the disadvantage is that the response functions can only be constructed for the known physical relationship, and the unknown physical relationship may be ignored. Whereas using neural network to predict reservoir parameters, we can map the relationship between logs and reservoir parameters as long as building a suitable model and have a large number of training data. With the help of neural network, we can map the unknown physical relationship without much geological expertise. The existing research of reservoir parameter prediction neural network with logs only focuses on one kind of reservoir parameter prediction modeling, ignoring the relationship between reservoir parameters. In this paper, relevance transfer learning is introduced, which using the knowledge of petrophysics to improve the performance of neural network reservoir parameters prediction.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"82nd EAGE Annual Conference & Exhibition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202112716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Traditional methods for reservoir parameters prediction with logs are based on petrophysics knowledge, such as volumetric models and response functions (Darwin and Julian, 2007). The advantage of those methods is that the relationship between the logs and reservoir parameters is clear, there is a theoretical basis, and it can be explained; the disadvantage is that the response functions can only be constructed for the known physical relationship, and the unknown physical relationship may be ignored. Whereas using neural network to predict reservoir parameters, we can map the relationship between logs and reservoir parameters as long as building a suitable model and have a large number of training data. With the help of neural network, we can map the unknown physical relationship without much geological expertise. The existing research of reservoir parameter prediction neural network with logs only focuses on one kind of reservoir parameter prediction modeling, ignoring the relationship between reservoir parameters. In this paper, relevance transfer learning is introduced, which using the knowledge of petrophysics to improve the performance of neural network reservoir parameters prediction.
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
基于关联迁移学习的测井储层参数预测
利用测井资料预测储层参数的传统方法是基于岩石物理学知识,如体积模型和响应函数(Darwin and Julian, 2007)。这些方法的优点是测井曲线与储层参数之间的关系清晰,有理论依据,可以解释;缺点是只能针对已知的物理关系构造响应函数,而可能忽略未知的物理关系。而利用神经网络进行储层参数预测,只要建立合适的模型,并有大量的训练数据,就可以映射出测井曲线与储层参数之间的关系。在神经网络的帮助下,我们可以在没有太多地质专业知识的情况下绘制未知的物理关系。现有的利用测井资料进行储层参数预测的神经网络研究只集中在一种储层参数预测建模上,忽略了储层参数之间的关系。本文介绍了关联迁移学习方法,利用岩石物理学知识提高神经网络储层参数预测的性能。
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
自引率
0.00%
发文量
0
期刊最新文献
GOMCRUST - The crustal-scale extension of the 2004 BP velocity model for long-offset OBN acquisition setting Prestack data attenuation compensation based on inversion Complex Near-surface Velocity Modeling via U-net Integrated high-resolution model building: a case study from the Sultanate of Oman Inverse Hessian estimation in least-squares migration using chains of operators
×
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