{"title":"基于三维地震属性和井资料标定的三维三维岩相识别与建模","authors":"S. Roy, Kalyan Saikia","doi":"10.2118/194599-MS","DOIUrl":null,"url":null,"abstract":"\n Seismic attributes play an important role during reservoir characterization and three-dimensional (3D) lithofacies modeling by providing indirect insight of the subsurface. Using seismic attributes for such studies has always been challenging because it is difficult to determine a realistic relationship between hard data points (i.e., well information) and a 3D volume of seismic attributes. However, a probability-based approach for 3D seismic attribute calibration with well data provides better results of lithofacies modeling and spatial distribution of reservoir properties. This paper presents a probability-based seismic attribute calibration technique that has been described for 3D lithofacies modeling and distribution. This approach helps in subsurface reservoir characterization and provides a realistic lithofacies distribution model. This approach also helps reduce uncertainty of lithofacies prediction compared to conventional methods of simply using geostatistical algorithms.","PeriodicalId":11150,"journal":{"name":"Day 2 Wed, April 10, 2019","volume":"179 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Three-Dimensional 3D Lithofacies Identification and Modeling Using 3D Seismic Attribute and Well Data Calibration\",\"authors\":\"S. Roy, Kalyan Saikia\",\"doi\":\"10.2118/194599-MS\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Seismic attributes play an important role during reservoir characterization and three-dimensional (3D) lithofacies modeling by providing indirect insight of the subsurface. Using seismic attributes for such studies has always been challenging because it is difficult to determine a realistic relationship between hard data points (i.e., well information) and a 3D volume of seismic attributes. However, a probability-based approach for 3D seismic attribute calibration with well data provides better results of lithofacies modeling and spatial distribution of reservoir properties. This paper presents a probability-based seismic attribute calibration technique that has been described for 3D lithofacies modeling and distribution. This approach helps in subsurface reservoir characterization and provides a realistic lithofacies distribution model. This approach also helps reduce uncertainty of lithofacies prediction compared to conventional methods of simply using geostatistical algorithms.\",\"PeriodicalId\":11150,\"journal\":{\"name\":\"Day 2 Wed, April 10, 2019\",\"volume\":\"179 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 2 Wed, April 10, 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/194599-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 2 Wed, April 10, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/194599-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Three-Dimensional 3D Lithofacies Identification and Modeling Using 3D Seismic Attribute and Well Data Calibration
Seismic attributes play an important role during reservoir characterization and three-dimensional (3D) lithofacies modeling by providing indirect insight of the subsurface. Using seismic attributes for such studies has always been challenging because it is difficult to determine a realistic relationship between hard data points (i.e., well information) and a 3D volume of seismic attributes. However, a probability-based approach for 3D seismic attribute calibration with well data provides better results of lithofacies modeling and spatial distribution of reservoir properties. This paper presents a probability-based seismic attribute calibration technique that has been described for 3D lithofacies modeling and distribution. This approach helps in subsurface reservoir characterization and provides a realistic lithofacies distribution model. This approach also helps reduce uncertainty of lithofacies prediction compared to conventional methods of simply using geostatistical algorithms.