Application of the bootstrap method on a large input data set - case study western part of the Sava Depression

Abstract

The bootstrap method is a nonparametric statistical method that provides through resampling the input data set to obtain a new data set that is normally distributed. Due to various factors, deep geological data are difficult to obtain many data set, 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. The minimum number of resampling's required for a large sample to obtain a normal distribution is 1000. Interval estimation of porosity for reservoir "L" obtained by bootstrap method is 0.1875 to 0.2144 with 95% confidence level.