Formation water is produced during the whole lifetime of a hydrocarbon reservoir alongside the oil and/or gas and it represents the main part of the produced fluid. The produced formation water is ...separated during the process of dehydration. This paper deals with the formation water separation costs regarding the fields A, B and C which are located in the western part of the Sava Depression. The dehydration process regarding field A is executed in three locations, and in fields B and C, it is executed in one location. The technological system of formation water separation and the geological characteristics of the above-mentioned reservoirs is represented. A statistical analysis regarding the formation water separation costs has been made. The costs have been statistically estimated and a correlation between the costs relevant for the usual formation water separation process has also been made. The purpose of the analysis of the cost of the dehydration process is the optimization of the production system and cost control of the process.
Formation water injection is one of the constituent parts of the hydrocarbon production cycle in the secondary exploitation oil recovery phase. The formation water injection system can be divided the ...into single and central injection systems. The formation water injection costs have been described in this paper using the examples of field A (central injection system) and field B (single injection system). These are located in the western part of the Sava Depression. The reservoir rocks regarding the oil and gas fields A and B are fine to middle grained sands and quartz micaceous sandstones that belong to the geological age of Lower Pontian. The average porosity (intergranular) in field A is 15-35% and in field B it is 10-31%, depending on the depth and cementation percentage. Regarding the oil and gas fields described in this paper, a cost comparison has been made and an injection system sensitivity analysis as well as an analysis of possible injection systems' costs for optimization and rationalization.
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.
Small possible hydrocarbon gas reservoirs were analysed in the Bjelovar Subdepression in Northern Croatia. This area includes the Neogene–Quaternary, mostly clastics, sequences, reaching 3000+ metres ...in the deepest part. The shallow south-eastern part of the Drava Depression contains a subdepression characterised with several, mostly small, discovered hydrocarbon fields, where the majority are located on the northern subdepression margin. The reason is the large distance from the main depressional migration pathways and main, deep, mature source rock depocenters. However, two promising unconventional targets were discovered inside the subdepression and both were proven by drilling. The first are source rocks of Badenian, of kerogen type III in early catagenesis, where partially inefficient expulsion probably kept significant gas volumes trapped in the source rock during primary migration. Such structures are the Western Bjelovar (or Rovišće) and the Eastern Bjelovar (or Velika Ciglena) Synclines. The second promising unconventional reservoir consists of “tight” clastic lithofacies of mostly Lower Pontian located on the north-eastern margin of the subdepression. These are fine-grained sandstones with frequent alternations in siltites, silty and clayey sandstones. They are located on secondary migration pathways, but were never evaluated as regional reservoirs, although numerous drilling tests showed gas “pockets”.
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.
Geological probability calculation was applied to the Northern Croatian Neogene deposits in the western Sava Depression. Structures “A” and “B” (with sandstone hydrocarbon reservoirs inside the ...Kloštar Ivanić Formation, Upper Miocene) were analyzed using two different Probability of Success (POS) methodologies. The classical POS approach showed that, in such play and wider areas of selected two structures, the probability for discovering the new HC reservoirs is 42.18%. This is valid for minimum of 500,000 m
3
of geological reserves. The modified POS approach has been used for calculation of waterflooding efficiency in selected structures “A” and “B.” The probability value of 56.25% indicates that future flooding would result in increased recovery.
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.
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