基于支持向量机的水驱油物理模拟建模方法研究

Meijuan Gao, Jingwen Tian, Hao Zhou
{"title":"基于支持向量机的水驱油物理模拟建模方法研究","authors":"Meijuan Gao, Jingwen Tian, Hao Zhou","doi":"10.1109/GSIS.2007.4443448","DOIUrl":null,"url":null,"abstract":"An actual physical simulation model was constructed to simulate the course of water displacing oil Under certain physical property parameter conditions, we simulated the water injection well and the oil withdrawal well on the physical simulation model, and continuous measured online the oil and water content of different areas of physical simulation model in three-dimensional spaces using the 512 routes resistivity measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is complicated and nonlinear, the support vector machine (SVM) was used to establish the water displacing remaining oil model, in order to obtain the distributing situation of remaining oil in various complicated conditions. We proposed a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters. The experimental results show that this method is feasible and effective.","PeriodicalId":445155,"journal":{"name":"2007 IEEE International Conference on Grey Systems and Intelligent Services","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on modeling method of water displacing oil physical simulation based on support vector machine\",\"authors\":\"Meijuan Gao, Jingwen Tian, Hao Zhou\",\"doi\":\"10.1109/GSIS.2007.4443448\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An actual physical simulation model was constructed to simulate the course of water displacing oil Under certain physical property parameter conditions, we simulated the water injection well and the oil withdrawal well on the physical simulation model, and continuous measured online the oil and water content of different areas of physical simulation model in three-dimensional spaces using the 512 routes resistivity measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is complicated and nonlinear, the support vector machine (SVM) was used to establish the water displacing remaining oil model, in order to obtain the distributing situation of remaining oil in various complicated conditions. We proposed a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters. The experimental results show that this method is feasible and effective.\",\"PeriodicalId\":445155,\"journal\":{\"name\":\"2007 IEEE International Conference on Grey Systems and Intelligent Services\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Grey Systems and Intelligent Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSIS.2007.4443448\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Grey Systems and Intelligent Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2007.4443448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在一定物性参数条件下,对物理模拟模型上的注水井和采油井进行了模拟,并利用512路电阻率测量电路在三维空间连续在线测量了物理模拟模型不同区域的含油量和含油量,获得了大量模拟样本。针对剩余油与水驱油各参数之间关系复杂、非线性的问题,利用支持向量机(SVM)建立了水驱剩余油模型,得到了各种复杂条件下剩余油的分布情况。提出了一种自适应参数调整迭代算法来确定支持向量机参数。实验结果表明,该方法是可行和有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Research on modeling method of water displacing oil physical simulation based on support vector machine
An actual physical simulation model was constructed to simulate the course of water displacing oil Under certain physical property parameter conditions, we simulated the water injection well and the oil withdrawal well on the physical simulation model, and continuous measured online the oil and water content of different areas of physical simulation model in three-dimensional spaces using the 512 routes resistivity measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is complicated and nonlinear, the support vector machine (SVM) was used to establish the water displacing remaining oil model, in order to obtain the distributing situation of remaining oil in various complicated conditions. We proposed a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters. The experimental results show that this method is feasible and effective.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Research of sludge compost maturity degree modeling method based on classify support vector machine for sewage treatment On the properties of weakening operator Countermeasure and cause analysis on urban construction investment and financing Grey association degree analyses and arrangement of targets’ value A rough set based GDSS approach to integrate multi-type preference information
×
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