Abstract

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 Klostar 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 1 st and 100 th realizations were selected for each variable in the SGS and SIS and five “indicator kriging” maps were chosen for the thicknesses cutoffs.

Highlights

  • Geostatistics is currently one of the standard geological tools applied in the exploration and development of hydrocarbon reservoirs

  • For the Kloštar Field, the stochastic approach was applied as an analytical tool to estimate three geological variables, porosity, thickness, and reservoir depth, using data collected in the same sandstone reservoir, referred to as “T”

  • The respective values are located in the western part of the Kloštar Field, which is near the deeper part of the Sava Depression

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Summary

INTRODUCTION

Geostatistics is currently one of the standard geological tools applied in the exploration and development of hydrocarbon reservoirs. Stochastic simulation tools that include the Monte Carlo algorithm represent a logical upgrade to the probabilistic approach as applied in estimating reservoir variables and hydrocarbon reserves These are deterministic methods that draw on a variogram model and kriging or cokriging as the “zero” or base realization. For the Kloštar Field, the stochastic approach was applied as an analytical tool to estimate three geological variables, porosity, thickness, and reservoir depth, using data collected in the same sandstone reservoir, referred to as “T” (a description of this reservoir is given in BALIĆ et al, 2008) This dataset is considered to be representative of present-day structural relationships (depth and thickness), and of the depositional palaeo-environment as recorded in terms of reservoir porosity distribution and thickness. These points were selected from approximately 100 well points based on criteria obtained from the latest computer analysis of well logs, including average porosity values (previously not calculated), precise, stratigraphically determined reservoir tops and bottoms, and thickness (mostly from e-log markers)

THE BASICS OF STOCHASTIC SIMULATIONS
Processing of input data and “zero” realization
Simulation
Calculation of a set of realizations
Advantages and disadvantages of SGS
RESULTS
CONCLUSIONS
Full Text
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