The Kiskunhalas-NE (KIHA-NE) fractured hydrocarbon reservoir is part of the structurally rather complex crystalline basement of the Great Hungarian Plain. In the course of petrologic and thermometric examinations various rock types of the investigated area have been classified and characterized. There are four basic lithological units in the area. In the lowest structural position orthogneiss is common, which according to its petrographic features is assumed to be identical to the orthogneiss body of the adjacent Jánoshalma (JH) basement high (metamorphic peak temperature T < 580 °C according to Zachar and M. Tóth 2004). The next rock unit upward is the highly mylonitized variety of the orthogneiss with textural features suggesting deformation in an extensional stress regime. In the higher section of the mylonite zone graphitic gneiss mylonite is characteristic, with a peak metamorphic T of 410±45 °C. The lithology in the shallowest position of the area is a graphitic carbonate phyllite, with a T of 375 ± 15 °C. Estimation of the deformation temperature for both mylonitic rocks results in approximately Tdef ∼ 455 °C. All data together suggest that between the top (graphitic carbonate phyllite) and the bottom (orthogneiss) of the ideal rock column there is about 200 °C peak metamorphic temperature deviation. The two extreme metamorphic blocks probably became juxtaposed along an extensional fault zone in the basement at approximately 15 km depth.
Over the years many studies have been conducted to understand the delta system of the Algyő field, many of them dealing with the Ap-13 reservoir. In the present study, therefore, several papers have been reviewed and analyzed to provide the basis for a more detailed description of this reservoir.
A macro-scale sedimentological model was developed using Markov analysis. Golden Software's Surfer 8.1 and Strater were used to construct the maps and to define the vertical sedimentological facies of the A-993 borehole. The mega-scale sedimentological 3-D model and the petrophysical parameters of 144 boreholes were analyzed using the Rockware RockWorks 15 and SPSS software.
It is concluded, when comparing the vertical section of the A-993 borehole (from the core description) with the sand content from the 3-D model at similar depth, considering the results of the embedded Markov model and the 3-D sedimentological model, that the reservoir is a deep-marine sand body, with a sand content less than 55% and siltstone content of around 30%. It is characterized by the features of proximal middle fan systems.
Authors:Barbara Szabó, Tivadar M. Tóth and Félix Schubert
Volcanic successions of the Kecel Basalt Formation (KBF) occur in the southern part of the Pannonian Basin. As a result of periodic submarine eruptions, the basaltic and pyroclastic rock horizons were intercalated with layers of the Late Miocene Endrod Marl Formation, which is regarded as one of the most important hydrocarbon source rocks in the area. The KBF was discovered through almost 30 wells between 2,200 and 2,900 meters of depth. Due to the high fracture porosity, some parts of the formation show good reservoir characteristics and act as important migration pathways of hydrocarbon-bearing fluids. Since the reservoir is presumably fracture-controlled, this study concentrates on the evolution of fractures crosscutting the rock body. Based on textural and mineralogical features, four distinct vein types can be distinguished, of which the first three types are discussed in this paper. Beside calcite, quartz, feldspar, and chlorite, the veins are cemented by various zeolite minerals. The vertical dimension of the dominant zeolite zone indicates the burial-diagenetic type of zeolite zonation and suggests subsidence of the subaqueous basalt after formation.
Seismic data integration in reservoir modeling workflows is the one of the fastest-growing fields in the Earth Sciences. The actual geostatistical methods (co-kriging, stochastic simulation) can use seismic data as a secondary variable if there is a well-determined linear correlation between well log data and seismic attribute. Seismic interpreters must often increase this correlation. The application of multi-attributes via neural network may help in this case. A neural network type, called multi-layer perceptron, and its application in 3D porosity distribution prediction in a Hungarian natural gas reservoir, are described in this paper.
reservoir. The main goal of this study is to find a method which can reduce the huge number of realizations for hydrocarbonreservoirs in a way that the remaining ones retain the information of spatial uncertainty for the reservoir’s flow behavior as
Wireline logging surveys are routinely used for the reconnaissance and quantitative characterization of multi-mineral hydrocarbon structures. The interpretation of well-logging data, however, is quite a challenging task, because the conventionally used local inversion procedure becomes either an underdetermined or a slightly overdetermined problem that may result in poor parameter estimation. In order to determine the petrophysical model composed of several parameters, such as specific volumes of matrix components, water saturation, primary and secondary porosity and numerous zone-parameters, in a more reliable way a new inversion methodology is required. We suggest a joint inversion technique for the estimation of model parameters of multi-mineral rocks that inverts data acquired from a larger depth interval (hydrocarbon zone). The inverse problem is formulated assuming homogeneous intervals within the zone to get a highly overdetermined inversion procedure. The interval inversion method has been applied to shaly sandy hydrocarbon reservoirs, in this study, that is used for the estimation of petrophysical parameters of complex reservoirs. Numerical results with synthetic and field data demonstrate the feasibility of the inversion method in investigating carbonate and metamorphic structures.
Authors:Tomislav Malvić, Josipa Velić, Janina Horváth and Marko Cvetković
Three examples of the use of neural networks in analyses of geologic data from hydrocarbon reservoirs are presented. All networks are trained with data originating from clastic reservoirs of Neogene age located in the Croatian part of the Pannonian Basin. Training always included similar reservoir variables, i.e. electric logs (resistivity, spontaneous potential) and lithology determined from cores or logs and described as sandstone or marl, with categorical values in intervals. Selected variables also include hydrocarbon saturation, also represented by a categorical variable, average reservoir porosity calculated from interpreted well logs, and seismic attributes. In all three neural models some of the mentioned inputs were used for analyzing data collected from three different oil fields in the Croatian part of the Pannonian Basin. It is shown that selection of geologically and physically linked variables play a key role in the process of network training, validating and processing. The aim of this study was to establish relationships between log-derived data, core data, and seismic attributes. Three case studies are described in this paper to illustrate the use of neural network prediction of sandstone-marl facies (Case Study # 1, Okoli Field), prediction of carbonate breccia porosity (Case Study # 2, Beničanci Field), and prediction of lithology and saturation (Case Study # 3, Kloštar Field). The results of these studies indicate that this method is capable of providing better understanding of some clastic Neogene reservoirs in the Croatian part of the Pannonian Basin.