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.

Keywords:
machine learning, neural network, Upstream Oil & Gas, fuzzy logic, Artificial Intelligence, regression, radial basis function network, functional network, University, predict z-factor
Subjects:
Information Management and Systems, Artificial intelligence
Copyright 2018, Society of Petroleum Engineers
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