{"title":"碳酸盐岩储层成像测井解释的先进技术:工作流程、地质控制因素、动态数据集成","authors":"G. Buongiovanni, R. Berto, M. Pirrone, G. Trombin","doi":"10.3997/2214-4609.201903300","DOIUrl":null,"url":null,"abstract":"Summary The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level and a secondary porosity quantification. A further characterization of the facies can be established by means of image processing techniques which are mainly aimed to quantify the different components of secondary porosity. We adopt the Watershed Transform (WT) approach which is based on digital image segmentation processes. The WT-based algorithm provides a robust quantification of the secondary porosity contributions to total porosity in terms of connected vugs, isolated vugs, fractures and matrix contribution rate. Finally, image log-facies classification and quantitative porosity partition can be integrated with production logging and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale. The presented methodology can perform an advanced automatic interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and not efficient standard analyses when the number of wells to be handled is large. The added value from this data-driven image log analysis is demonstrated through selected case studies coming from wells in carbonate reservoirs with high heterogeneity.","PeriodicalId":427666,"journal":{"name":"Third EAGE Borehole Geology Workshop","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"“Advanced techniques for image log interpretation in carbonate reservoirs: workflow, geological control factors, dynamic data integration”\",\"authors\":\"G. Buongiovanni, R. Berto, M. Pirrone, G. Trombin\",\"doi\":\"10.3997/2214-4609.201903300\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level and a secondary porosity quantification. A further characterization of the facies can be established by means of image processing techniques which are mainly aimed to quantify the different components of secondary porosity. We adopt the Watershed Transform (WT) approach which is based on digital image segmentation processes. The WT-based algorithm provides a robust quantification of the secondary porosity contributions to total porosity in terms of connected vugs, isolated vugs, fractures and matrix contribution rate. Finally, image log-facies classification and quantitative porosity partition can be integrated with production logging and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale. The presented methodology can perform an advanced automatic interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and not efficient standard analyses when the number of wells to be handled is large. The added value from this data-driven image log analysis is demonstrated through selected case studies coming from wells in carbonate reservoirs with high heterogeneity.\",\"PeriodicalId\":427666,\"journal\":{\"name\":\"Third EAGE Borehole Geology Workshop\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third EAGE Borehole Geology Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.201903300\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third EAGE Borehole Geology Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201903300","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
“Advanced techniques for image log interpretation in carbonate reservoirs: workflow, geological control factors, dynamic data integration”
Summary The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level and a secondary porosity quantification. A further characterization of the facies can be established by means of image processing techniques which are mainly aimed to quantify the different components of secondary porosity. We adopt the Watershed Transform (WT) approach which is based on digital image segmentation processes. The WT-based algorithm provides a robust quantification of the secondary porosity contributions to total porosity in terms of connected vugs, isolated vugs, fractures and matrix contribution rate. Finally, image log-facies classification and quantitative porosity partition can be integrated with production logging and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale. The presented methodology can perform an advanced automatic interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and not efficient standard analyses when the number of wells to be handled is large. The added value from this data-driven image log analysis is demonstrated through selected case studies coming from wells in carbonate reservoirs with high heterogeneity.