Gas compressibility factor plays an important role in reservoir engineering applications. A lot of techniques have been proposed to predict Z-factor. Standing-Katz (S-K) Z-factor chart is the most common and popular among them and is being used since 1941. Many correlations have been proposed after S-K chart to regenerate and increase its range in an accurate manner. Some of these models are direct models such as Papp Correlation, Shell Oil Company Correlation, and Beggs and Brill Correlation, others are indirect correlations such as Hall-Yarborough and Dranchuk-Abu-Kassem Correlation.
In this study, five different artificial intelligence techniques are implemented to predict Z-factor. These techniques are neural network, radial basis function network, fuzzy logic, functional network, and support vector machine. To build and test these techniques, Standing-Katz charts data was used in which about 70% of the data was used for training and 30% for testing.
Results from this work show that artificial intelligence techniques can predict Z-factor with low error such as Neural network, Radial basis function, Fuzzy logic, and Support vector machine. Neural network is the best technique among others in predicting Z-factor.
This work will help in selecting the best artificial intelligence technique for predicting Z-factor.
Compressibility factor is the ratio between actual volume to ideal volume. Its value indicates how much the real gas deviates from the ideal gas behavior at a certain pressure and temperature. Z-factor values are mainly used in reservoir engineering (Xiang 2005). Accurate calculation of Z-factor affects determination of other properties for the case of gas condensate and gas reservoirs. The Standing-Katz charts have been used as a standard to calculate Z-factor in petroleum engineering since 1942. These charts were developed using the concept of pseudo-reduced properties, reduced temperature, Tr, and reduced pressure, Pr.
A lot of research have been conducted after Standing-Katz charts trying to fit these charts and to extend them. There are several correlations available to predict Z-factor [Standing and Katz 1942, Papay 1968, Beggs and Brill 1979, Burnett 1979, Papp 1979, Hall and Yarborough 1973, Mahmoud 2013, Lateef 2013, Elechi et. al 2015].
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