Determination of hydrate stability zone using electrical conductivity data of salt aqueous solution

In this contribution, the possibility of predicting the hydrate suppression temperatures of petroleum fluids from electrical conductivity data of salt aqueous solutions is investigated by developing a feed-forward artificial neural network method. The predictions of a comprehensive thermodynamic model as pseudo-experimental data of hydrate suppression temperature and electrical conductivity data from the literature for different salt aqueous solutions (NaCl, KCl, CaCl2, NaBr, KBr, K2CO3, BaCl2 and MgCl2) are used to train and develop the neural network method. The developed tool considers the changes in the electrical conductivity of a given salt aqueous solution for estimating the hydrate suppression temperature. Since measurement of the electrical conductivity for the aqueous phase is much easier than measuring the hydrate suppression temperature, such a relation can reduce the experimental costs. The results of the developed artificial neural network method for the hydrate suppression temperature along with the predictions of a previously reported general correlation for hydrate phase boundaries of petroleum fluids in the presence of distilled water are then used to determine hydrate stability zones of petroleum fluids in the presence of saline water. Predictions are found in acceptable agreement with the independent (not used in training and developing of neural network) experimental data, demonstrating the reliability of the proposed method for determination of the hydrate stability zone in the presence of salt aqueous solutions.