1. Identifying Flow Units Using an Artificial Neural Network Approach Optimized by the Imperialist Competitive Algorithm

Seyyed Hossein Hosseini Bidgoli; Ghasem Zargar; Mohammad Ali Riahi

Volume 3, Issue 3 , Summer 2014, , Pages 11-25

http://dx.doi.org/10.22050/ijogst.2014.6618

Abstract
  The spatial distribution of petrophysical properties within the reservoirs is one of the most important factors in reservoir characterization. Flow units are the continuous body over a specific reservoir volume within which the geological and petrophysical properties are the same. Accordingly, an accurate ...  Read More

2. The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks

Ali Khazaei; Hossein Parhizgar; Mohammad Reza Dehghani

Volume 3, Issue 3 , Summer 2014, , Pages 47-61

http://dx.doi.org/10.22050/ijogst.2014.6621

Abstract
  In this work, artificial neural network (ANN) has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures ...  Read More

3. Development of an Intelligent System to Synthesize Petrophysical Well Logs

Morteza Nouri Taleghani; Sadegh Saffarzadeh; Mina Karimi Khaledi; Ghasem Zargar

Volume 2, Issue 3 , Summer 2013, , Pages 11-24

http://dx.doi.org/10.22050/ijogst.2013.3641

Abstract
  Porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. It is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. Log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon ...  Read More