Univrsity of Tehran Press Geopersia 2228-7817 3 2 2013 12 01 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 87 97 36017 10.22059/jgeope.2013.36017 EN Mahdi Rastegarnia Petroleum Engineering Department, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran Ali Kadkhodaie-Ilkhchi Geology Department, Faculty of Natural Science, University of Tabriz, Tabriz, Iran Journal Article 2013 06 27 Accurate permeability estimation has always been a concern in determining flow units, assigning appropriate capillary pressure and<br />relative permeability curves to reservoir rock types, geological modeling, and dynamic simulation.Acoustic method can be used as an<br />alternative and effective tool for permeability determination. In this study, a four-step approach is proposed for permeability estimation<br />from acoustic data. The steps include estimation of the Stoneley wave slowness from conventional logs using a support vector machine<br />neural network, determination of the Stoneley wave slowness in non-permeable zones, calculation of the Stoneley permeability index,<br />and calculation of the Stoneley-Flow Zone Index (ST-FZI) permeability using the index matching factor (IMF). Finally, a comparison<br />is made between the ST-FZI permeability with those derived from CMR log and core analysis. The results of this study show that<br />acoustic method in conjunction with robust SVM neural network can be considered as an accurate tool for permeability estimation in<br />the mixed clastic-carbonate reservoirs with complex pore type systems. https://geopersia.ut.ac.ir/article_36017_09f9448dccf9a38e762255e98f81ff50.pdf