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Application of Artificial Neural Networks in Identification of Geological Formations on the Basis of Well Logging Data – A Comparison of Computational Environments’ Efficiency

  • Marcin ZychEmail author
  • Gabriel Stachura
  • Robert Hanus
  • Norbert P. Szabó
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 548)

Abstract

The paper presents the application of artificial neural networks in lithology identification on the basis of well logging data. The problem is very important considering petroleum geophysics as it allows to find sweet spots -potential deposits of hydrocarbons (oil and gas). The use of advanced statistical methods such as artificial neural networks is expected to improve geological interpretation of geophysical data. Moreover, such methods are capable of dealing with big data sets since well logging provides more and more information about petrophysical (e.g. porosity, density, resistivity, natural gamma radiation, sonic wave propagation) and chemical rock properties (mineral content and element abundance). Therefore, the analyzed data comprises around 56000 records. Two different computational environments has been used in order to examine their efficiency in terms of accuracy of a lithological classification. Computation was done in R software, which is an open source environment, and STATISTICA v. 13 which is a commercial one. As an input, logging data from three boreholes drilled in the Baltic Basin, North Poland were used. The results show that R offers more possibilities of modification of a net. However, STATISTICA provides more user-friendly interface and better accuracy of lithology identification.

Keywords

Artificial neural network Well logging Lithological classification 

Notes

Acknowledgements

Data was allowed by POGC Warsaw, Poland for the MWSSSG Polskie Technologie dla Gazu Łupkowego project (2013–2017).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.AGH – University of Science and TechnologyKrakówPoland
  2. 2.Rzeszów University of TechnologyRzeszówPoland
  3. 3.University of MiskolcMiskolc-EgyetemvárosHungary

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