{"title":"在碳酸氢盐池中建立一个地下中子流模型,其地震数据的物理性质","authors":"Tabah Fatchur Rubiyana, Paul Hutabarat","doi":"10.31258/jkfi.18.3.179-190","DOIUrl":null,"url":null,"abstract":"Physical properties are often applied to oil and gas exploration analysis using seismic data. However, in reality, none of the physical properties (attributes) of seismic data can describe the entire type of lithology of a subsurface layer. It takes a combination of various physical properties (multi-attributes) and other data to map the lithological distribution of a subsurface layer. One of the seismic attributes that can be used in describing the condition of subsurface lithology is acoustic impedance (AI). Acoustic impedance can provide information in the form of rock lithology in a layer. This information can be interpreted by inversion. Inversions performed on acoustic impedance obtain the results of cross-sectional distribution of acoustic impedance that shows lithology. As existing lithological conditions, correlations to other physical properties can be modeled. Combination of the physical property used is called multi-attribute. Multi-attribute methods can predict and model the porosity of rocks from seismic attributes. The application of this method is used to describe lateral distribution and porosity mapping (neutron porosity). The results of the study using the multi-attribute seismic method applied to the LMGS Field seismic data obtained a distribution map of neutron porosity. Neutron porosity values obtained to show a hydrocarbon reservoir range from 0.05 to 0.2 on the fraction scale.","PeriodicalId":403286,"journal":{"name":"Komunikasi Fisika Indonesia","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MEMBANGUN MODEL NEUTRON POROSITY BAWAH PERMUKAAN DENGAN PROPERTI FISIK DATA SEISMIK PADA RESERVOIR KARBONAT\",\"authors\":\"Tabah Fatchur Rubiyana, Paul Hutabarat\",\"doi\":\"10.31258/jkfi.18.3.179-190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Physical properties are often applied to oil and gas exploration analysis using seismic data. However, in reality, none of the physical properties (attributes) of seismic data can describe the entire type of lithology of a subsurface layer. It takes a combination of various physical properties (multi-attributes) and other data to map the lithological distribution of a subsurface layer. One of the seismic attributes that can be used in describing the condition of subsurface lithology is acoustic impedance (AI). Acoustic impedance can provide information in the form of rock lithology in a layer. This information can be interpreted by inversion. Inversions performed on acoustic impedance obtain the results of cross-sectional distribution of acoustic impedance that shows lithology. As existing lithological conditions, correlations to other physical properties can be modeled. Combination of the physical property used is called multi-attribute. Multi-attribute methods can predict and model the porosity of rocks from seismic attributes. The application of this method is used to describe lateral distribution and porosity mapping (neutron porosity). The results of the study using the multi-attribute seismic method applied to the LMGS Field seismic data obtained a distribution map of neutron porosity. Neutron porosity values obtained to show a hydrocarbon reservoir range from 0.05 to 0.2 on the fraction scale.\",\"PeriodicalId\":403286,\"journal\":{\"name\":\"Komunikasi Fisika Indonesia\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Komunikasi Fisika Indonesia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31258/jkfi.18.3.179-190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Komunikasi Fisika Indonesia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31258/jkfi.18.3.179-190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MEMBANGUN MODEL NEUTRON POROSITY BAWAH PERMUKAAN DENGAN PROPERTI FISIK DATA SEISMIK PADA RESERVOIR KARBONAT
Physical properties are often applied to oil and gas exploration analysis using seismic data. However, in reality, none of the physical properties (attributes) of seismic data can describe the entire type of lithology of a subsurface layer. It takes a combination of various physical properties (multi-attributes) and other data to map the lithological distribution of a subsurface layer. One of the seismic attributes that can be used in describing the condition of subsurface lithology is acoustic impedance (AI). Acoustic impedance can provide information in the form of rock lithology in a layer. This information can be interpreted by inversion. Inversions performed on acoustic impedance obtain the results of cross-sectional distribution of acoustic impedance that shows lithology. As existing lithological conditions, correlations to other physical properties can be modeled. Combination of the physical property used is called multi-attribute. Multi-attribute methods can predict and model the porosity of rocks from seismic attributes. The application of this method is used to describe lateral distribution and porosity mapping (neutron porosity). The results of the study using the multi-attribute seismic method applied to the LMGS Field seismic data obtained a distribution map of neutron porosity. Neutron porosity values obtained to show a hydrocarbon reservoir range from 0.05 to 0.2 on the fraction scale.