Good estimation of gas compressibility factor (z-factor) of gas is an essential key in numerous gas and oil calculations. In the absence of experimental data, the iterative methods were run to estimate the z-factor. However, these methods are more complex and have a large number of factors, which require longer calculations. In addition, the accuracy of these correlations has become insufficient for the best estimations due to their limitations. The objective of this study is to test various Fuzzy Logic (FL) technique to develop a simple and robust approach. The FL has three types: Fuzzy c-means (FCM), grid partition (GP), and sub-clustering (SC) Algorithms. The proposed FL models were compared with iterative methods to test its performance and reliability to predict z-factor. Around 6500 published and unpublished data points with a wide range of z-factor and reduced temperature and pressure were collected from several fields in the Middle East used to develop FL models. It was found that the developed FL with various cluster techniques is more precise and trustful than published empirical techniques and can be used in a wide range of pseudoreduced pressure and temperature. The obtained results show that the FL with sub-cluster technique performs well with a lower average relative per cent error of 0.13% and higher accuracy (R2=1) than the other models. The technique presented in this work is robust, efficient, and accurate. It can be used to calculate the z-factor in the absence of experimental data.
Al-Gathe, Abdelrigeeb Ali; Baarimah, Salem Obeid; and Al-Khudafi, Abbas Mohamed
"Comparative Study of Different Fuzzy Models for Gas Compressibility Factor Prediction,"
Hadhramout University Journal of Natural & Applied Sciences: Vol. 18
, Article 1.
Available at: https://digitalcommons.aaru.edu.jo/huj_nas/vol18/iss1/1