{"title":"多基因遗传规划在储层弹性模量估算中的应用","authors":"Nuo Li, Hao Chen, Jian-qiang Han","doi":"10.1109/SPAWDA.2019.8681879","DOIUrl":null,"url":null,"abstract":"Based on the Multigene genetic programming (MGGP) method, the static elastic moduli (Young’s moduli) of reservoir sandstones are estimated with the bulk densities, porosities and P-wave velocities data. A analytical expression for static moduli is obtained by evolution according to the method, and the static moduli are calculated by the expression. Then the calculation results are compared with those obtained by experimental and empirical methods. It shows that MGGP method is more accurate than the traditional empirical relation between dynamic and static parameters. Furthermore, MGGP requires less input parameters. For example, S-wave velocities are not necessary. So the method reduce the error induced by the inaccurate or missing of S-wave velocities.","PeriodicalId":304940,"journal":{"name":"2019 Symposium on Piezoelectrcity,Acoustic Waves and Device Applications (SPAWDA)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Application of Multigene Genetic Programming for Estimating Elastic Modulus of Reservoir Rocks\",\"authors\":\"Nuo Li, Hao Chen, Jian-qiang Han\",\"doi\":\"10.1109/SPAWDA.2019.8681879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the Multigene genetic programming (MGGP) method, the static elastic moduli (Young’s moduli) of reservoir sandstones are estimated with the bulk densities, porosities and P-wave velocities data. A analytical expression for static moduli is obtained by evolution according to the method, and the static moduli are calculated by the expression. Then the calculation results are compared with those obtained by experimental and empirical methods. It shows that MGGP method is more accurate than the traditional empirical relation between dynamic and static parameters. Furthermore, MGGP requires less input parameters. For example, S-wave velocities are not necessary. So the method reduce the error induced by the inaccurate or missing of S-wave velocities.\",\"PeriodicalId\":304940,\"journal\":{\"name\":\"2019 Symposium on Piezoelectrcity,Acoustic Waves and Device Applications (SPAWDA)\",\"volume\":\"133 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Symposium on Piezoelectrcity,Acoustic Waves and Device Applications (SPAWDA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPAWDA.2019.8681879\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Symposium on Piezoelectrcity,Acoustic Waves and Device Applications (SPAWDA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAWDA.2019.8681879","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Multigene Genetic Programming for Estimating Elastic Modulus of Reservoir Rocks
Based on the Multigene genetic programming (MGGP) method, the static elastic moduli (Young’s moduli) of reservoir sandstones are estimated with the bulk densities, porosities and P-wave velocities data. A analytical expression for static moduli is obtained by evolution according to the method, and the static moduli are calculated by the expression. Then the calculation results are compared with those obtained by experimental and empirical methods. It shows that MGGP method is more accurate than the traditional empirical relation between dynamic and static parameters. Furthermore, MGGP requires less input parameters. For example, S-wave velocities are not necessary. So the method reduce the error induced by the inaccurate or missing of S-wave velocities.