Gas compressibility factor is an important thermodynamic property introduced to modify the ideal gas law to account for the real gas behavior. Precise determination of this property is the top priority for many aspects of chemical and petroleum engineering applications. After the publication of the results of the experiments conducted by Standing and Katz in 1942, their proposed chart has become an industry standard, and since then many authors have tried to develop new models by fitting their parameters to that chart. Lack of sufficient accuracy, especially near specific boundaries, as expected for applications like in numerical reservoir simulators, and the iterative nature of procedures are the most common problems of these suggested models. To defeat such problems, in this research a novel type of model is developed on the basis of fuzzy inference system. The error analysis of the generated model compared with the widely accepted Dranchuk and Abou-Kassem correlation, the artificial neural network model proposed by Kamyab and colleagues and several other well-known models demonstrate that fuzzy logic can reach much higher precisions than most of the current methods.