Permeability is one of the key parameters in the characterization and modeling of the hydrocarbon reservoirs, which has an important effect on optimal production and field management. Permeability is directly obtained from the core data and indirectly from the well testing data. Determining permeability by using core is the most accurate and expensive method in the industry, also core information may only exist in some wells. Therefore, it is necessary to use methods that can determine permeability without core data. Use of hydraulic flow units (HFU) concept is one of the methods for estimate permeability. HFUs are a function of flow zone indicator (FZI), in which each flow unit has a unique FZI. Due to the fact that permeability estimation in wells without core data is one of the important issues in the oil industry, the purpose of this study is to estimate permeability in one of the southern Iranian carbonate gas fields. In this field, core data only exists in one of the wells; therefore, we used an integrated approach of HFUs and adaptive network fuzzy inference system (ANFIS) to estimate permeability. Subsequently, the values of FZI, RQI (reservoir quality index), and ϕZ (normalized porosity) were calculated for core samples, and six HFUs were identified by different methods which showed a high value of the correlation coefficient in each HFU. Based on HFU and FZI, permeability was estimated and compared with core permeability data. The average relative error between the core permeability and the estimated value is 1.83%. Eventually, based on conventional well log data and ANFIS, permeability values were estimated in un-cored wells. The average relative error between core permeability and ANFIS calculated has shown 5.21%. So it can be concluded that this method can be used in all un-cored wells by just using log data of high accuracy

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