Application of the bootstrap method in low-sampled Upper Miocene sandstone hydrocarbon reservoirs: a case study

2021 ◽  
pp. 1-15 ◽  
Author(s):  
Josip Ivšinović ◽  
Maria Alzira Pimenta Dinis ◽  
Tomislav Malvić ◽  
Dubravka Pleše
Keyword(s):  
Bootstrap Method ◽  
Hydrocarbon Reservoirs ◽  
Upper Miocene ◽  
The Bootstrap Method

scholarly journals Kriging with Small Number of Data Supported by Jack-Knifing, Case Study in the Sava Depression (Northern Croatia)

2018 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Rajna Rajić
Keyword(s):  
Ordinary Kriging ◽  
Cross Validation ◽  
Pannonian Basin ◽  
Hydrocarbon Reservoirs ◽  
Upper Miocene ◽  
System Data ◽  
Northern Croatia ◽  
Basin System

Presented is semivariogram and the Ordinary Kriging analyses of porosity data from the Sava Depression (Northern Croatia), as part of the Croatian part of the Pannonian Basin System. Data are taken from hydrocarbon reservoirs of the Lower Pontian (Upper Miocene) age, which belongs to the Kloštar-Ivanić Formation. Original datasets had been jack-knifed with purpose to “artificially” increased data and calculate the more reliable semivariograms. The results showed that such improvements can assist in the interpolation of more reliable maps. The both sets, made by original and jack-knifed data, need to be compared using geological recognition of non-allowed shapes (“bull-eyes”, “butterfly effects”) as well as cross-validation results. That comparison made possible to select the most appropriate porosity interpolation.

scholarly journals Measuring Child Poverty and Its Uncertainty: A Case Study of 33 European Countries

2020 ◽  
Vol 12 (19) ◽  
pp. 8204
Author(s):  
Ilaria Benedetti ◽  
Gianni Betti ◽  
Federico Crescenzi
Keyword(s):  
Social Protection ◽  
Bootstrap Method ◽  
Child Poverty ◽  
National Level ◽  
Poverty Rate ◽  
European Countries ◽  
National Estimates ◽  
Poverty Indicators ◽  
The Bootstrap Method

Over the last few years, there has been increased interest in compiling poverty indicators for children, as well as in providing uncertainty measures that are associated with point estimates. In this paper, we provide point, variance, and interval confidence estimates of the at-risk-of-poverty rate indicator for 33 European countries. Using the 2018 EU-SILC survey, we analysed the spatial distribution of poverty by providing graphical representations at the national level. Our results reveal rates of child poverty that are higher than in the national estimates for most of the countries. By considering the computation of standard errors, we used the bootstrap method thanks to its convenient properties. It is worth noting that, for some countries, such as Finland, Belgium, and Ireland, the confidence intervals do not overlap. These results suggest differences among countries not only in terms of child poverty, but also in terms of social protection and the welfare state.

scholarly journals Application of the Bootstrap Method in Data Analysis of Upper Miocene Sandstone Reservoirs of the Western Part of the Sava Depression

2020 ◽  
Author(s):  
Josip Ivšinović ◽  
Tomislav Malvić ◽  
Dubravka Pleše
Keyword(s):  
Input Data ◽  
Bootstrap Method ◽  
Reliable Data ◽  
Formation Water ◽  
Geological Data ◽  
Exploration And Production ◽  
Small Set ◽  
Case Data ◽  
The Bootstrap Method

In deep geological analysis of data, these are input data that are few and include a small set of data. In a small set of case data, it is necessary to obtain reliable data of individual geological variables from this type of data. The paper analyzes the possibility of applying the bootstrap method on variables that are important in the exploration and production of hydrocarbons. The variables analyzed were the following: porosity and total costs of disposal formation water. The case study was made on the data of reservoir "K", field "B" located in the western part of the Sava Depression. The analysis of the results showed the possibility of applying the bootstrap method in the analysis of deep geological data with the application of three different sizes of resampling dataset.

scholarly journals Kriging with a Small Number of Data Points Supported by Jack-Knifing, a Case Study in the Sava Depression (Northern Croatia)

Geosciences ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 36 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Rajna Rajić
Keyword(s):  
Ordinary Kriging ◽  
Cross Validation ◽  
Pannonian Basin ◽  
Hydrocarbon Reservoirs ◽  
Sampled Data ◽  
Upper Miocene ◽  
Data Points ◽  
Northern Croatia ◽  
Basin System

The semivariogram and the ordinary kriging analyses of porosity data from the Sava Depression (Northern Croatia), are presented relative to the Croatian part of the Pannonian Basin system. The data are taken from hydrocarbon reservoirs of the Lower Pontian (Upper Miocene) age, which belong to the Kloštar Ivanić Formation. The original datasets had been jack-knifed with the purpose of re-sampling and calculating the more reliable semivariograms. The results showed that such improvements can assist in the interpolation of more reliable maps. Both sets, made by the original and re-sampled data, need to be compared using geological recognition of isoline’s shapes (such as “bull-eye” or “butterfly” effects) as well as cross-validation results. This comparison made it possible to select the most appropriate porosity interpolation.

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

2021 ◽  
Vol 36 (5) ◽  
pp. 13-19
Author(s):  
Josip Ivšinović ◽  
Nikola Litvić
Keyword(s):  
Input Data ◽  
Bootstrap Method ◽  
Interval Estimation ◽  
Statistical Tool ◽  
Data Set ◽  
Minimum Number ◽  
Nonparametric Statistical ◽  
The Bootstrap Method ◽  
Normally Distributed

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.

scholarly journals Statistical Estimates of the Pulsar Glitch Activity

Universe ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Alessandro Montoli ◽  
Marco Antonelli ◽  
Brynmor Haskell ◽  
Pierre Pizzochero
Keyword(s):  
Linear Regression ◽  
Upper Bound ◽  
Bootstrap Method ◽  
Waiting Times ◽  
Statistical Estimates ◽  
Equal Variance ◽  
Linear Fit ◽  
The Moment ◽  
The Bootstrap Method

A common way to calculate the glitch activity of a pulsar is an ordinary linear regression of the observed cumulative glitch history. This method however is likely to underestimate the errors on the activity, as it implicitly assumes a (long-term) linear dependence between glitch sizes and waiting times, as well as equal variance, i.e., homoscedasticity, in the fit residuals, both assumptions that are not well justified from pulsar data. In this paper, we review the extrapolation of the glitch activity parameter and explore two alternatives: the relaxation of the homoscedasticity hypothesis in the linear fit and the use of the bootstrap technique. We find a larger uncertainty in the activity with respect to that obtained by ordinary linear regression, especially for those objects in which it can be significantly affected by a single glitch. We discuss how this affects the theoretical upper bound on the moment of inertia associated with the region of a neutron star containing the superfluid reservoir of angular momentum released in a stationary sequence of glitches. We find that this upper bound is less tight if one considers the uncertainty on the activity estimated with the bootstrap method and allows for models in which the superfluid reservoir is entirely in the crust.

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