Henadz Zaitsau, Valeri Shumilyak, Alexander Konyushenko
{"title":"基于岩性测井、测井、地震数据和现有机器学习和分类方法的概率面模型生成","authors":"Henadz Zaitsau, Valeri Shumilyak, Alexander Konyushenko","doi":"10.2118/206547-ms","DOIUrl":null,"url":null,"abstract":"\n The main topic of an article is machine learning and classification (neural net) use for prognostic lithological model creation. Moreover, research preceding stages such as attribute analysis, seismic inversion, seismogeological modeling and briefly the results of lithological and petrophysical investigations are described in this art","PeriodicalId":11177,"journal":{"name":"Day 4 Fri, October 15, 2021","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generation of a Probabilistic Facial Model on the Basis of Lithology Logs, Well Logs, Seismic Data and Existing Methods of Machine Learning and Classification\",\"authors\":\"Henadz Zaitsau, Valeri Shumilyak, Alexander Konyushenko\",\"doi\":\"10.2118/206547-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The main topic of an article is machine learning and classification (neural net) use for prognostic lithological model creation. Moreover, research preceding stages such as attribute analysis, seismic inversion, seismogeological modeling and briefly the results of lithological and petrophysical investigations are described in this art\",\"PeriodicalId\":11177,\"journal\":{\"name\":\"Day 4 Fri, October 15, 2021\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 4 Fri, October 15, 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/206547-ms\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 4 Fri, October 15, 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/206547-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generation of a Probabilistic Facial Model on the Basis of Lithology Logs, Well Logs, Seismic Data and Existing Methods of Machine Learning and Classification
The main topic of an article is machine learning and classification (neural net) use for prognostic lithological model creation. Moreover, research preceding stages such as attribute analysis, seismic inversion, seismogeological modeling and briefly the results of lithological and petrophysical investigations are described in this art