How Machine Learning Is Replacing Conventional Interpretation

D. Sacrey, R. Roden
{"title":"How Machine Learning Is Replacing Conventional Interpretation","authors":"D. Sacrey, R. Roden","doi":"10.3997/2214-4609.201803011","DOIUrl":null,"url":null,"abstract":"This presentation shows severaed classification process in successful case histories of the sample-basully finding hydrocarbons and delineating reservoir limits. This type of machine learning is especially good for thin bed exploration as it allows for stratigraphic pattern recognition below conventional seismic tuning.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"First EAGE/PESGB Workshop Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201803011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This presentation shows severaed classification process in successful case histories of the sample-basully finding hydrocarbons and delineating reservoir limits. This type of machine learning is especially good for thin bed exploration as it allows for stratigraphic pattern recognition below conventional seismic tuning.
机器学习如何取代传统的口译
本报告展示了样品成功案例历史中的几种分类过程——主要是寻找碳氢化合物和圈定储层界限。这种类型的机器学习尤其适用于薄层勘探,因为它允许在常规地震调谐下进行地层模式识别。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信