Extended Search

- in all fields
- in the title
- in key words
- in the abstract
- in the bibliography
Artificial Neural Networks for separation of space trends on the basis of 3D seismic results
Automation, modeling and energy supply in oil and gas sector

Authors: Sergey N. SKRIPKIN (b. 1983) graduated from Gubkin Russian State University of Oil and Gas in 2006. He is a post-graduate student of the Department of Applied Mathematics and Computer-Aided Engineering of Gubkin Russian State University of Oil and Gas.
Emilia CHEN-SIN (b. 1935) graduated from Gubkin Moscow Institute of Oil. Docent (lecturer) of Gubkin Russian State University of Oil and Gas. E-mail: biblioteka@gubkin.ru

Abstract: Interpolation of petrophysical properties is an important part of 3D geological modeling. 3D seismic contains huge amount of available information about acoustic parameters, which can (and must) be used for porosity prediction. In thes paper we suggest to use artificial neural networks for extracting such information from seismic volumes. This approach does not have significant drawbacks usual for some other model (for example, linear correlation).

Index UDK: 51.001

Keywords: geological model, petrophysical properties, neural network, seismic data