<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The Ladislavci Field (oil and gas reservoirs) is located 40 km from the city of Osijek, Croatia. The oil reservoir is in structural-stratigraphic trap and Miocene rocks of the Vukovar formation (informally named as El, F1a and F1b). The shallower gas reservoir is of Pliocene age, i.e. part of the Osijek sandstones (informally named as B). The oil reservoirs consist of limestones, breccias and conglomerates, and gas is accumulated in sandstones. Using neural networks, it was possible to interpret applicability of neural algorithm in well log analyses, and using neural model, it was possible to predict reservoir without or with small number of log data. Neural networks are trained on the data from two wells (A and B), collected from the interval starting with border of Sarmatian/ Lower Pannonian (EL marker Rs7) to the well’s bottom. The inputs were data from spontaneous potential (SP) and resistivity (R16 and R64) logs. They were used for neural training and validation as well as for final prediction of lithological composition in the analysed field. The multilayer perceptron (MLP) network had been selected as the most appropriate.</p></abstract>

The Ladislavci Field (oil and gas reservoirs) is located 40 km from the city of Osijek, Croatia. The oil reservoir is in structural-stratigraphic trap and Miocene rocks of the Vukovar formation (informally named as El, F1a and F1b). The shallower gas reservoir is of Pliocene age, i.e. part of the Osijek sandstones (informally named as B). The oil reservoirs consist of limestones, breccias and conglomerates, and gas is accumulated in sandstones. Using neural networks, it was possible to interpret applicability of neural algorithm in well log analyses, and using neural model, it was possible to predict reservoir without or with small number of log data. Neural networks are trained on the data from two wells (A and B), collected from the interval starting with border of Sarmatian/ Lower Pannonian (EL marker Rs7) to the well’s bottom. The inputs were data from spontaneous potential (SP) and resistivity (R16 and R64) logs. They were used for neural training and validation as well as for final prediction of lithological composition in the analysed field. The multilayer perceptron (MLP) network had been selected as the most appropriate.

Improved neural network model of assessment for interpretation of miocene lithofacies in the Vukovar formation, Northern Croatia / Izboljšani nevronsko-mrežni model za oceno interpretacije miocenskega litofaciesa v Vukovarski formaciji v severni Hrvaški

Materials and Geoenvironment

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The Ladislavci Field (oil and gas reservoirs) is located 40 km from the city of Osijek, Croatia. The oil reservoir is in structural-stratigraphic trap and...

Improved neural network model of assessment for interpretation of miocene lithofacies in the Vukovar formation, Northern Croatia / Izboljšani nevronsko-mrežni model za oceno interpretacije miocenskega litofaciesa v Vukovarski formaciji v severni Hrvaški

Data publikacji: 18 gru 2019

Zakres stron: 145 - 156

Otrzymano: 01 maj 2018

Przyjęty: 08 cze 2018

DOI: https://doi.org/10.2478/rmzmag-2018-0029

Słowa kluczowe
neural networks, Ladislavci Field, Drava Depression, Miocene, Croatia

© 2019 Varenina Andrija, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Improved neural network model of assessment for interpretation of miocene lithofacies in the Vukovar formation, Northern Croatia / Izboljšani nevronsko-mrežni model za oceno interpretacije miocenskega litofaciesa v Vukovarski formaciji v severni Hrvaški

Data publikacji: 18 gru 2019

Zakres stron: 145 - 156

Otrzymano: 01 maj 2018

Przyjęty: 08 cze 2018

DOI: https://doi.org/10.2478/rmzmag-2018-0029

Słowa kluczoweneural networks, Ladislavci Field, Drava Depression, Miocene, Croatia

© 2019 Varenina Andrija, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
neural networks, Ladislavci Field, Drava Depression, Miocene, Croatia