Development and Validation of Multi-Purpose Geostatistical Model with Modified Kriging Method

수정된 Kriging법을 응용한 다목적지구통계모델의 개발 및 타당성 검토

  • Kim, In-Kee (Department of Mineral and Petroleum Engineering, Hanyang University) ;
  • Sung, Won-Mo (Department of Mineral and Petroleum Engineering, Hanyang University) ;
  • Jung, Moon-Young (Department of Mineral and Energy Resources Engineering, Semyung University)
  • 김인기 (한양대학교 공과대학 자원공학과) ;
  • 성원모 (한양대학교 공과대학 자원공학과) ;
  • 정문영 (세명대학교 자원공학과)
  • Received : 1993.01.06
  • Published : 1993.04.30

Abstract

In modem petroleum reservoir engineering, the characterization of reservoir heterogeneities is very important to accurately understand and predict reservoir production performance. Formation evaluation for the description of reservoir is generally conducted by performing the analysis of well logging, core testing, and well testing. However, the measured data points by well logging or core testing are in general very sparse and hence reservoir properties should be interpolated and extrapolated from measured points to uncharacterized areas. In assigning the data for the unknown points, simple averaging technique is not feasible as optimum estimation method since this method does not account the spatial relationship between the data points. The main goal of this work is to develop PC-version of multi-purpose geostatistical model in which several stages are systematically proceeded. In the development of model, the simulator employs a automatic selection of semivariogram function such as exponential or spherical model with the best values of $R^2$. The simulator also implements a special algorithm for the fitting of semivariogram function to experimental sernivariogram. The special algorithm such as trial and error scheme is devised since this method is much more reliable and stable than Gauss-Newton method. The simulator has been tested under stringent conditions and found to be stable. Finally, the validity and the applicability of the developed model have been studied against some existing actual field data.

Keywords