Oil-Water Separation in Liquid-Liquid Hydrocyclones (LLHC)–Experiment and Modeling

Abstract

Abstract The liquid-liquid Hydrocyclone (LLHC) has been widely used by the Petroleum Industry for the past several decades. A large quantity of information on the LLHC available in the literature includes experimental data, computational fluid dynamic simulations and field applications. The design of LLHCs has been based in the past mainly on empirical experience. However, no simple and overall design mechanistic model has been developed to date for the LLHC. The objective of this study is to develop a mechanistic model for the de-oiling LLHCs, and test it against available and new experimental data. This model will enable the prediction of the hydrodynamic flow behavior in the LLHC, providing a design tool for LLHC field applications. A simple mechanistic model is developed for the LLHC. The required input for the model is: LLHC geometry, fluid properties, inlet droplet size distribution and operational conditions. The model is capable of predicting the LLHC hydrodynamic flow field, namely, the axial, tangential and radial velocity distributions of the continuous-phase. The separation efficiency and migration probability are determined based on swirl intensity prediction and droplet trajectory analysis. The flow capacity, namely, the inlet-to-underflow pressure drop is predicted utilizing an energy balance analysis. An extensive experimental program has been conducted during this study, utilizing a 2″ MQ Hydroswirl hydrocyclone. The inlet flow conditions are: total flow rates between 27 to 18 gpm, oil-cut up to 10%, median droplet size distributions from 50 to 500 ìm, and inlet pressures between 60 to 90 psia. The acquired data include the flow rate, oil-cut and droplet size distribution in the inlet and in the underflow, the reject flow rate and oil concentration in the overflow and the separation efficiency. Additional data for velocity profiles were taken from the literature, especially from the Colman and Thew (1980) study. Excellent agreement is observed between the model prediction and the experimental data with respect to both separation efficiency (average absolute relative error of 3%) and pressure drop (average absolute relative error of 1.6%).