Abstract
The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model’s superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity.
Highlights
The increasing demand for oil and coal as energy and the technological and environmental concerns associated with its production and consumption have drawn attention toward natural gas
Critical insight into the behavior of natural gas is important to the reservoir and the chemical engineering calculations that deal with gas as one of the main phases
The reliability of any intelligent model is dependent on the data bank that has been utilized for the purpose of model training and testing
Summary
The increasing demand for oil and coal as energy and the technological and environmental concerns associated with its production and consumption have drawn attention toward natural gas. When utilizing EOS, the reservoir characteristics are being employed These equations come from the following form when the gas compressibility factor is the target PVT parameter (McCain, 2017): Z3 + a × Z2 + b × Z + c = 0. We have already developed two intelligent models for predicting natural gas compressibility factor using the same data bank (Kamari et al, 2013; Shateri et al, 2015). They are a black box, and their usage generally needs software. The performance of previously published well-known correlations and EOSs was investigated and compared to the proposed model
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