The correct selection of the value of p is a complex and iterative procedure that requires experience in the interpretation of the obtained interpolated maps. Inverse Distance Weighting is a method applied to the porosities of the K and L hydrocarbon reservoirs discovered in the Neogene (Lower Pontian) subsurface sandstones in northern Croatia (Pannonian Basin System). They represent small and large data samples. Also, a standard statistical analysis of the data was made, followed by a qualitative–quantitative analysis of the maps, based on the selection of different values for the power distance exponent (p-value) for the K and L reservoir maps. According to the qualitative analysis, for a small data set, the p-value could be set at 1 or 2, giving the optimal result, while for a large data set, a p value of 3 or 4 could be applied. For quantitative analysis, in the case of a small data set, p = 2 is recommended, resulting in a root mean square error value of 0.03458, a mean absolute error of 0.02013 and a median absolute deviation of 0.00546. In contrast, a p-value of 3 or 4 is selected as appropriate for a large data set, with root mean square errors of 0.02435 and 0.02437, mean square errors of 0.01582 and 0.01509 and median absolute deviations 0.00896 and 0.00444. Eventually for a small data set, it is recommended to use a p-value of 2, and for a large data set, a p-value of 3 or 4.

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.

Interpolation is a procedure that depends on the spatial and/or statistical properties of the analysed variable(s). It is a particularly challenging task for small datasets, such as in those with less than 20 points of data. This problem is common in subsurface geological mapping, i.e., in cases where the data is taken solely from wells. Successful solutions of such mapping problems depend on interpolation methods designed primarily for small datasets and the datasets themselves. Here, we compare two methods, Inverse Distance Weighting and the Modified Shepard’s Method, and apply them to three variables (porosity, permeability, and thickness) measured in the Neogene sandstone hydrocarbon reservoirs (northern Croatia). The results show that cross-validation itself will not provide appropriate map selection, but, in combination with geometrical features, it can help experts eliminate the solutions with low-probable structures/shapes. The Golden Software licensed program Surfer 15 was used for the interpolations in this study.

The interpolation of small datasets is challenging problem regarding the selection of interpolation methods and type of datasets. Here, for such analysis, the analysed data was taken in two hydrocarbon fields (“A” and “B”), located in the western part of the Sava Depression (in Northern Croatia). The selected reservoirs “L” (in the “A” Field) and “K” (“B”) are of Lower Pontian (Upper Miocene) age and belong to the Kloštar-Ivanić Formation. Due to strong tectonics, there are numerous tectonic blocks, each sampled with only a few wells. We selected two variables for interpolation—reservoirs permeabilities and injected volumes of field water. The following interpolation methods are described, compared and applied: Nearest Neighbourhood, Natural Neighbour (for the first time in the Sava Depression) and Inverse Distance Weighting. The last one has been recommended as the most appropriate in this study. Also, the presented research can be repeated in similar clastic environments at the same level hydrocarbon of exploration.

Here are analysed data taken in two hydrocarbon fields ("A" and "B"), located in the western part of Sava Depression (North Croatia). They are in the secondary phase of production. The selected reservoirs "L" (in the “A” Field) and "K" (“B”) are of the Lower Pontian (Upper Miocene) age and belong to Kloštar-Ivanić Formation. Due to strong tectonics, there are numerous tectonic block, relatively rarely sampled with well and laboratory tests. Here are selected two variables for interpolation - reservoirs permeabilities and the injected volumes of field water. The following interpolation methods are described, compared and applied: Nearest Neighbourhood, Natural Neighbour (the first time in the Sava Depression) and Inverse Distance Weighting. The last one has been proven as the most appropriate for datasets with size lower than 20 points.

The authors analyse the process of water re-injection in the hydrocarbon reservoirs/fields in the Upper Miocene sandstone reservoirs, located in the western part of the Sava Depression (Croatia). Namely, this is the “A” field with “L” reservoir that currently produces hydrocarbons using a secondary recovery method, i.e., water injection (in fact, re-injection of the field waters). Three regional reservoir variables were analysed: Porosity, permeability and injected water volumes. The quantity of data was small for porosity reservoir “L” and included 25 points; for permeability and injected volumes of water, 10 points each were measured. This study defined selection of mapping algorithms among methods designed for small datasets (fewer than 20 points). Namely, those are inverse distance weighting and nearest and natural neighbourhood. Results were tested using cross-validation and isoline shape recognition, and the inverse distance weighting method is described as the most appropriate approach for mapping permeability and injected volumes in reservoir “L”. Obtained maps made possible the application of the modified geological probability calculation as a tool for prediction of success for future injection (with probability of 0.56). Consequently, it was possible to plan future injection more efficiently, with smaller injected volumes and higher hydrocarbon recovery. Prevention of useless injection, decreasing number of injection wells, saving energy and funds invested in such processes lead to lower environmental impact during the hydrocarbon production.