Automatic Electrofacies Classification from Well Logs Using Multivariate Statistical Techniques

다변량 통계 기법을 이용한 물리검층 자료로부터의 암석물리학상 결정

  • Lim Jong-Se (Research Institute of Engineering Science, Seoul National University) ;
  • Kim Jungwhan (Technical Department, Korea National Oil Company) ;
  • Kang Joo-Myung (Sch. of Urban, Civil & Geosystem Eng., Seoul National University)
  • 임종세 (서울대학교 공학연구소) ;
  • 김정환 (한국석유공사 기술실) ;
  • 강주명 (서울대학교 지구환경시스템공학부)
  • Published : 1998.11.01

Abstract

A systematic methodology is developed for the prediction of the lithology using electrofacies classification from wireline log data. Multivariate statistical techniques are adopted to segment well log measurements and group the segments into electrofacies types. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the quality and efficiency of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification works well with reliability to the core and cutting data. This methodology for electrofacies determination can be used to define reservoir characterization which is helpful to the reservoir management.