TY - JOUR ID - 10365 TI - Toward a Thorough Approach to Predicting Klinkenberg Permeability in a Tight Gas Reservoir: A Comparative Study JO - Iranian Journal of Oil and Gas Science and Technology JA - IJOGST LA - en SN - 2345-2412 AU - Baziar, Sadegh AU - Gafoori, Mohammad Mobin AU - Mohaimenian Pour, Seyed Mehdi AU - Bidhendi, Majid Nabi AU - Hajiani, Reza AD - Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran AD - Persian Gulf Science and Technology Park, Bushehr, Iran AD - Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran AD - Institute of Geophysics, University of Tehran, Tehran, Iran Y1 - 2015 PY - 2015 VL - 4 IS - 3 SP - 18 EP - 36 KW - Klinkenberg Permeability KW - Tight Gas Reservoir KW - Multiple linear regression KW - General Regression Neural Network KW - Support Vector Machine DO - 10.22050/ijogst.2015.10365 N2 - Klinkenberg permeability is an important parameter in tight gas reservoirs. There are conventional methods for determining it, but these methods depend on core permeability. Cores are few in number, but well logs are usually accessible for all wells and provide continuous information. In this regard, regression methods have been used to achieve reliable relations between log readings and Klinkenberg permeability. In this work, multiple linear regression, tree boost, general regression neural network, and support vector machines have been used to predict the Klinkenberg permeability of Mesaverde tight gas sandstones located in Washakie basin. The results show that all the four methods have the acceptable capability to predict Klinkenberg permeability, but support vector machine models exhibit better results. The errors of models were measured by calculating three error indexes, namely the correlation coefficient, the average absolute error, and the standard error of the mean. The analyses of errors show that support vector machine models perform better than the other models, but there are some exceptions. Support vector machine is a relatively new intelligence method with great capabilities in regression and classification tasks. Herein, support vector machine was used to predict the Klinkenberg permeability of a tight gas reservoir and the performances of four regression techniques were compared. UR - https://ijogst.put.ac.ir/article_10365.html L1 - https://ijogst.put.ac.ir/article_10365_5ee6e39bea9f84fecee5850c926577ce.pdf ER -