Toward a Thorough Approach to Predicting Klinkenberg Permeability in a Tight Gas Reservoir: A Comparative Study

Authors

  • Mohammad Mobin Gafoori Persian Gulf Science and Technology Park, Bushehr, Iran
  • Reza Hajiani Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran
  • Sadegh Baziar Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran
  • Seyed Mehdi Mohaimenian Pour Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
Abstract:

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.

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Journal title

volume 4  issue 3

pages  18- 36

publication date 2015-07-01

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