Prediction of permeability in a tight gas reservoir by using three soft computing approaches: A comparative study

Abstract Permeability is the most important petrophysical property in tight gas reservoirs. Many researchers have worked on permeability measurement methods, but there is no universal method yet which can predict permeability in the whole field and in all intervals of the wells. So artificial intelligence methods have been used to predict permeability by using well log data in all field areas. In this research, Multilayer Perceptron Neural Network, Co-Active Neuro-Fuzzy Inference System and Support Vector Machine techniques have been employed to predict permeability of Mesaverde tight gas sandstones located in Washakie basin in USA. Multilayer Perceptrons are the most used neural networks in regression tasks. Co-Active Neuro-Fuzzy Inference System is a method which combines fuzzy model and neural network in a manner to produce accurate results. Support Vector Machine is a relatively new intelligence method with great capabilities in regression and classification tasks. Each method has advantages and disadvantage and here their capability in predicting permeability has been evaluated. In this study, data from three wells were used and two different dataset patterns were constructed to evaluate performances of the models in predicting permeability by using either previously seen data or unseen data. The most important aspect of this research is investigation of capability of these methods to generalize the training patterns to previously unseen data. Results showed that all methods have acceptable performance in predicting permeability but Co-Active Neuro-Fuzzy Inference System and Support Vector Machine performs so better than Multilayer Perceptron and predict permeability more accurate.

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