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ABSTRACT
The presence of water in the natural gas causes numerous operating problems. Hence, in order to have satisfactory operational conditions in the gas production units, we should determine the accurate amount of water content of natural gas. This study aimed to develop a low parameter predictive tool based on the least square support vector machine for estimating water content of natural gas as a function of temperature and pressure under a wide range of conditions. Results obtained from the suggested model indicated its lower deviation than other existence correlations and confirmed its acceptable predictive capability. In addition, the obtained values of R-squared and mean relative error were 1.00 and 0.24%, respectively. This tool is simple to use and can be of help to gas engineers to determine an accurate approximation of water content of natural gas.