Amir Heydari, Keivan Shayesteh, Ladan Kamalzadeh


A great number of petroleum engineering calculations require knowledge of natural gas hydrate formation conditions. Ideally, natural gas hydrate formation conditions are determined experimentally in the laboratory, but these data are not always available. Correlations are consequently used to determine values for natural gas hydrate formation conditions. In this paper, an alternate tool, i.e. the artificial neural network (ANN) technique has been applied for estimation of temperature for gas hydrate formation.
ANN was applied to the 167 raw data in the range of 32-74 , 50-4200 psia and 0.554-1 for temperature, pressure and specific gravity, respectively. To check the ANN model, the samples were divided into two groups. One of them contained 149 samples and was used to train the network and the remaining 18 samples were used as the test sets. For the training of the different networks, the standard feed forward back propagation algorithm was used and several types of structures were tested to obtain the most suitable network for the prediction of solubility. To check the reproducibility of the results, each of the networks studied was trained three times. Finally the best ANN structure was determined as 7-5-1.
In comparison of performance analysis of ANN, the relative error (RE) was studied and maximum error found 3.035 percent and R2 value was equal to 0.9941. To ensure, the results of ANN was compared with the results of Sloan model. To sum up ANN shows the better results in comparison with it. So it can be concluded that ANN provides a good method in predicting temperature at witch hydrates formation occurs.

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