Institute of Petroleum Engineering

Centre for Gas Hydrate Research Publications



Abstract 163

Methane and Water Phase Equilibria in the Presence of Single and Mixed Electrolyte Solutions Using the Cubic-Plus-Association Equation of State

Haghighi, H., Chapoy, A., and Tohidi, B.
Oil & Gas Science and Technology- Revue de l'IFP , 64 141-154 (2009).

Methane and water phase equilibria in the presence of single and mixed electrolyte solutions using the Cubic-Plus-Association equation of state - Methane gas hydrates have been widely touted as a potential new source of energy. Methane hydrate has been found to form in various rocks or sediments given suitable pressures, temperatures, and supplies of water and methane. However, natural subsurface environments exhibit significant variations in formation water chemistry, and these changes create local shifts in the phase boundary. Furthermore, formation water produced with reservoir fluids contains various quantities of salts, which inhibit hydrate formation. Therefore, it is essential to gain a better understanding of the effect of aqueous electrolyte solutions on gas hydrate stability conditions. In this communication, we report new experimental dissociation data for methane simple hydrates in presence of aqueous solutions containing different concentrations of NaCl, KCl and MgCl2. The new data were generated by a reliable fixed-volume, isochoric, step-heating technique. The accuracy and reliability of the experimental measurements are demonstrated by comparing measurements with the literature data. A thermodynamic approach in which the Cubic-Plus-Association equation of state is combined with a modified Debye-Hückel electrostatic term is employed to model the phase equilibria. The hydrate-forming conditions are modeled by the solid solution theory of van der Waals and Platteeuw. To model hydrate phase equilibria in porous media, the effect of capillary pressure has been taken into account. Predictions of the developed model are validated against independent experimental data and the data generated in this work. A good agreement between predictions and experimental data is observed, supporting the reliability of the developed model.

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