Stochastic simulations of dependent geological variables in sandstone reservoirs of Neogene age: A case study of Kloštar Field, Sava Depression

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Kristina Novak Zelenika
Tomislav Malvić


The research presented herein is the first attempt to perform geostatistical simulations on three geologic variables, porosity, thickness, and depth to reservoir, in the Croatian Pannonian basin. The data were collected from a reservoir of Lower Pontian age in Kloštar Field, located in the western part of the Sava Depression.

All three variables were analyzed using sequential Gaussian simulations (SGS). Information regarding present-day depth, thickness, and locations of areas with higher porosity values were used to reconstruct paleo-depositional environments and the distribution of different lithotypes, ranging from medium-grained, to mostly clean sandstones and to pure, basin marls.   Estimates of present-day thickness and depth can help to define areas of gross tectonic displacement and the role of major faults that have been mapped in the field. However, since mapping of the raw data (including porosities) does not allow the reconstruction of paleo-depositional environments, sequential indicator simulations (SIS) were applied as a secondary analytical tool.  For this purpose, several cutoff values for thickness were defined in an effort to distinguish the orientation of depositional channels (main and transitional). This was accomplished by transforming porosities to indicator values (0 and 1) and by applying a non-linear “indicator kriging” technique as the “zero” map for obtaining numerous indicator realizations by SIS.

In the SGS and the SIS approaches, the simulations encompassed 100 realizations. A representative realization was then selected using purely statistical criteria, i.e., two realizations were almost always chosen in accordance with the order of calculation.  The 1st and 100th realizations were selected for each variable in the SGS and SIS and five “indicator kriging” maps were chosen for the thicknesses cutoffs.


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