TY - JOUR ID - 36017 TI - 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 JO - Geopersia JA - GEOPE LA - en SN - 2228-7817 AU - Rastegarnia, Mahdi AU - Kadkhodaie-Ilkhchi, Ali AD - Petroleum Engineering Department, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran AD - Geology Department, Faculty of Natural Science, University of Tabriz, Tabriz, Iran Y1 - 2013 PY - 2013 VL - 3 IS - 2 SP - 87 EP - 97 KW - Cheshmeh Khush oilfield KW - Flow zone indicator KW - Permeability KW - Stoneley wave velocity KW - Well log data DO - 10.22059/jgeope.2013.36017 N2 - Accurate permeability estimation has always been a concern in determining flow units, assigning appropriate capillary pressure andrelative permeability curves to reservoir rock types, geological modeling, and dynamic simulation.Acoustic method can be used as analternative and effective tool for permeability determination. In this study, a four-step approach is proposed for permeability estimationfrom acoustic data. The steps include estimation of the Stoneley wave slowness from conventional logs using a support vector machineneural 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 comparisonis made between the ST-FZI permeability with those derived from CMR log and core analysis. The results of this study show thatacoustic method in conjunction with robust SVM neural network can be considered as an accurate tool for permeability estimation inthe mixed clastic-carbonate reservoirs with complex pore type systems. UR - https://geopersia.ut.ac.ir/article_36017.html L1 - https://geopersia.ut.ac.ir/article_36017_09f9448dccf9a38e762255e98f81ff50.pdf ER -