A novel Shan and Chen type Lattice Boltzmann two phase method to study the capillary pressure curves of an oil water pair in a porous media

In this study, an immiscible oil-water two phase flow in a typical porous media was modeled using the well-known Lattice Boltzmann method. A set of flow tests for modeling an oil-water two phase flow in the porous media were conducted to generate the capillary pressure curves for two distinctive initial conditions, namely, water and oil dispersed conditions in two domains of different resolutions. Based on the obtained results, the general trend of these curves has an acceptable agreement with the usual trend of these curves in hydrocarbon reservoirs and the capillary data are independent of the initial conditions. Also, the results showed the effect of grid resolution on capillary data which are validated quantitatively by proposing a new approach using Purcell's equation. One can see that they are compatible with the geometrical characteristics of the porous media as well as the conditions governing the tests. Finally, another set of tests for oil water pairs of higher viscosity ratio up to 4.4 was performed in a low porosity heterogeneous porous media and the viscous coupling effect on capillary data, due to viscosity ratio, was studied to strengthen the model validation. Keywords: Immiscible two phase flow, Lattice Boltzmann method, Shan and Chen model, Capillary pressure curves, Purcell's equatio

Assessing the dynamic viscosity of Na–K–Ca–Cl–H2O aqueous solutions at high-pressure and high-temperature conditions

Most industrial areas, especially oilfield operations and geothermal reservoirs, deal with varying viscosities in multicomponent electrolyte solutions. An accurate estimate of this property as a function of pressure, temperature, and varying salt concentrations is highly desirable. Although a number of empirical correlations have already been developed, they are still limited to single electrolyte solutions and can only operate over specified temperature and pressure ranges. In this study, a highly accurate model based on an adaptive network-based fuzzy inference system was developed, mainly devoted to dynamic viscosity prediction in aqueous multicomponent chloride solutions. Crisp input data were transformed into fuzzy sets employing the subtractive clustering algorithm with an effective radius optimized by a hybrid of genetic algorithm and particle swarm optimization technique. Comparing the model with thousands of experimental data concluded in squared correlation coefficient (R2 ) of 0.9986 and an average absolute error of 1.59%. The developed model was also found to outperform a number of empirical correlations that are employed for the viscosity determination of single electrolyte solutions