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Prediction of shear wave velocity based on a statistical rock-physics model and Bayesian theory
Zhang, Bing1,2; Jin, Shuanggen2,3; Liu, Cai4; Guo, Zhiqi4; Liu, Xiwu5
2020-12-01
Source PublicationJOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
ISSN0920-4105
Volume195Pages:12
AbstractShear wave velocity (V-s) is essential for amplitude-variation-with-offset (AVO) analysis and reservoir characterization. However, V-s is unavailable in many well logs due to the cost or the absence of technology for old wells. A common method is to estimate V-s from other measurements through their relationships, but has a large uncertainty. In this study, a statistical method is proposed to predict V-s of wells. Firstly, a statistical rock-physics model is built for the relationship between logging curves and V-s, which is realized by initializing key petrophysical parameters of the Xu-White model by the distributions instead of constants. The distributions come from prior information, which is a knowledge or experience of research area. Secondly, the key petrophysical parameters are calculated in Bayesian inversion framework by comparing the modeled compression wave velocity (V-p) with real data. Then, V-s is estimated based on these parameters and the rock-physics model. The real data test shows that our statistical method gets accurate V-s prediction, whose mean square error is about 0.002. Besides, the correlation coefficient between estimation and real data is about 0.97. The result is better than common methods. Moreover, statistics of the prediction, such as a confidence interval, can be provided by the statistical method. The real velocities are in the 95% confidence interval of the estimation. The estimated values and statistics of well velocities will offer more valuable information for the following processes of reservoir characterization.
KeywordS-wave velocity prediction Statistical model Xu-White model Bayesian inversion
Funding OrganizationNational Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China ; National Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China ; National Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China ; National Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China
DOI10.1016/j.petrol.2020.107710
WOS KeywordWELL LOG DATA ; ELASTIC PROPERTIES ; RESERVOIR ; SHALE ; POROSITY
Language英语
Funding ProjectNational Key Research and Development Program of China Project[2018YFC0603502] ; Jiangsu Province Distinguished Professor Project, China[R2018T20]
Funding OrganizationNational Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China ; National Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China ; National Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China ; National Key Research and Development Program of China Project ; National Key Research and Development Program of China Project ; Jiangsu Province Distinguished Professor Project, China ; Jiangsu Province Distinguished Professor Project, China
WOS Research AreaEnergy & Fuels ; Engineering
WOS SubjectEnergy & Fuels ; Engineering, Petroleum
WOS IDWOS:000586002900098
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/80247
Collection中国科学院国家天文台
Corresponding AuthorJin, Shuanggen
Affiliation1.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Joint Int Res Lab Climate & Environm Change ILCE, Key Lab Meteorol Disaster,Minist Educ KLME, Nanjing 210044, Peoples R China
2.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
3.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
4.Jilin Univ, Coll GeoExplorat Sci & Technol, Changchun 130012, Peoples R China
5.SinoPEC Petr Explorat & Prod Res Inst, Beijing 100083, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Bing,Jin, Shuanggen,Liu, Cai,et al. Prediction of shear wave velocity based on a statistical rock-physics model and Bayesian theory[J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,2020,195:12.
APA Zhang, Bing,Jin, Shuanggen,Liu, Cai,Guo, Zhiqi,&Liu, Xiwu.(2020).Prediction of shear wave velocity based on a statistical rock-physics model and Bayesian theory.JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,195,12.
MLA Zhang, Bing,et al."Prediction of shear wave velocity based on a statistical rock-physics model and Bayesian theory".JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 195(2020):12.
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