Neural networks in petroleum geology as interpretation tools

Malvić, Tomislav and Velić, Josipa and Horváth, Janina and Cvetković, Marko (2010) Neural networks in petroleum geology as interpretation tools. Central European Geology, 53 (1). pp. 97-115. ISSN 1788-2281


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Abstract 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.

Item Type: Article
Subjects: Q Science / természettudomány > QE Geology / földtudományok
Depositing User: xBarbara xBodnár
Date Deposited: 20 Jun 2017 07:48
Last Modified: 04 Apr 2023 12:54

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