APPLICATION OF THE BOOTSTRAP METHOD ON A LARGE INPUT DATA SET - CASE STUDY ON THE WESTERN PART OF THE SAVA DEPRESSION

Abstract

The bootstrap method is a nonparametric statistical method that through the resampling of an input data set provides the ability to obtain a new data set that is normally distributed. Due to various factors, it is difficult to obtain many data sets for deep geological data, and in most cases, they are not normally distributed. Therefore, it is necessary to introduce a statistical tool that will enable obtaining a set with which statistical analyses can be done. The bootstrap method was applied to field “A”, reservoir “L” located in the western part of the Sava Depression. It was applied to the geological variable of porosity on a set of 25 data points. The minimum number of resamplings required for a large sample to obtain a normal distribution is 1000. Interval estimation of porosity for reservoir “L” obtained by the bootstrap method is 0.1875 to 0.2144 with a 95% confidence level.

Keywords

• bootstrap
• porosity
• large data set
• normality tests
• Sava Depression