Use of Artificial Neural Networks for Estimating Water Content of Natural Gases

Abstract

Many thermodynamic models and correlations/charts are available that can estimate the water content of natural gases. The available methods normally have lower accuracy in regard to predicting the water content under low-temperature conditions and require further verification, because, during the development of the original predictive methods, experimental data that describe the phase equilibrium in water−hydrocarbon systems at low temperatures were not available. This is partially due to the fact that the water content of gases is indeed very low at low temperatures and high pressures, and, hence, it is generally very difficult to measure, because of, for example, adsorption problems in the sample transfer line or the analytical device. In this communication, an alternative method based on a feed-forward artificial neural network with a modified Levenberg−Marquardt algorithm is used to estimate the water content of natural gases, which assures high flexibility of the functional form for the regression. The method has been developed using recent experimental water content data, especially at low temperatures and near/inside the hydrate region. Experimental data that are not used in the development of this method have been used to examine the reliability of this method. The results are also compared with predictions of other predictive techniques. It is shown that the predictions of this method are in acceptable agreement, which demonstrates the reliability of the artificial neural network method for estimating the water content of natural gases.