Determination of the density of water emulsions in the crude oil dehydration process
Bahadori, A, Zendehboudi, S, Zahedi, G & Jamili, A 2015, 'Determination of the density of water emulsions in the crude oil dehydration process', Petroleum Science and Technology, vol. 33, no. 3, pp. 327-324.
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The salt elimination from produced crude oil is an important stage in oilfield processing and it is considered as a compulsory requirement in the oil industry. In most cases, salt is found a dissolved component in the brine phase in contact with oil. Different compositions of various salts might exist in the brine; however, sodium chloride (NaCl) has the highest fraction in the solution. The water existing in the crude oil is seen as very small drops dispersed in the bulk of oil. In the current study, a simple predictive strategy for density determination of aqueous salty solution in crude oil as a function of salinity (in vol% of sodium chloride concentration), temperature, and pressure is proposed through combination of an Arrhenius-type asymptotic exponential function and the relationship introduced by Spivey et al. (2004). The developed method predicts the amount of salt in the crude oil for temperatures up to 373 K, sodium chloride concentrations up to 250,000 ppm (25% by volume), and maximum pressure of 200 MPa, upon availability of the required input data. Estimations obtained from the proposed approach are found to be in very good agreement with the reported data in the literature so that the absolute error percentage varies in the range of 0.003–1.681%. The technique introduced in this research appears to provide reliable value for the oil engineers to attain a fast estimation of the salt content in the crude oil at various operating conditions without conducting laboratory tests. It is believed that the approach would be user-friendly without complicated computations for chemical and petroleum engineers and researchers. It can be also combined with existing thermodynamic software packages, resulting in an accurate and fast predictive model for practical applications.