Permeability estimation from the joint use of stoneley wave velocity and support vector machine neural networks: a case study of the Cheshmeh Khush Field, South Iran

Rastegarnia, Mahdi; Kadkhodaie-Ilkhchi, Ali

doi:10.22059/jgeope.2013.36017

Permeability estimation from the joint use of stoneley wave velocity and support vector machine neural networks: a case study of the Cheshmeh Khush Field, South Iran

Document Type : Research Paper

Authors

Mahdi Rastegarnia ¹

Ali Kadkhodaie-Ilkhchi ²

¹ Petroleum Engineering Department, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

² Geology Department, Faculty of Natural Science, University of Tabriz, Tabriz, Iran

10.22059/jgeope.2013.36017

Abstract

Accurate permeability estimation has always been a concern in determining flow units, assigning appropriate capillary pressure and
relative permeability curves to reservoir rock types, geological modeling, and dynamic simulation.Acoustic method can be used as an
alternative and effective tool for permeability determination. In this study, a four-step approach is proposed for permeability estimation
from acoustic data. The steps include estimation of the Stoneley wave slowness from conventional logs using a support vector machine
neural network, determination of the Stoneley wave slowness in non-permeable zones, calculation of the Stoneley permeability index,
and calculation of the Stoneley-Flow Zone Index (ST-FZI) permeability using the index matching factor (IMF). Finally, a comparison
is made between the ST-FZI permeability with those derived from CMR log and core analysis. The results of this study show that
acoustic method in conjunction with robust SVM neural network can be considered as an accurate tool for permeability estimation in
the mixed clastic-carbonate reservoirs with complex pore type systems.

Keywords

Cheshmeh Khush oilfield

Flow zone indicator

Permeability

Stoneley wave velocity

Well log data