Experimental and Theoretical Study of Calcium Sulphate Precipitation in Porous Media Using Glass Micromodel

Experimental and Theoretical Study of Calcium Sulphate Precipitation in Porous Media Using Glass Micromodel - Mixing of two incompatible waters in water injection projects is usually associated with mineral scale formation and deposition in porous media. Deposition process dramatically affects the performance of water injection scenarios by reduction of porosity and mainly permeability of the rock. In this study, a series of experiments has been conducted to investigate the effect of different parameters on the gradual process of Calcium Sulphate precipitation. These include temperature, concentration of mixing brines, pressure, and flow rate. Due to the visual nature of the glass micromodel, a glass sandstone pattern with water-wet characteristics was used as porous medium to easily observe the scaling formation and distribution. In addition, tracing the movement of the solid particles is highly facilitated in this newly suggested experimental setup. The captured photos in microscopy scanning show that the deposition is initiated from the walls of the pores and throats and extend toward the middle space of porous medium and solid crystals look like chicken roost. For better understanding of the effect of any aforementioned parameter, the related permeability reduction curve versus injected pore volume of the brine solutions was plotted. The results indicated that as the temperature, brine concentration, and flow rate increase the scaling tendency increases as well. The pressure has a minor role on the process development. Deposition of CaSO4 manifests a functional form of permeability reduction due to the effect of different parameters. Therefore, an exponential functionality (correlation) was proposed which incorporates all physical parameters that affect the behavior of the system in dimensionless form. Reynolds number, scaling index, and deviation from equilibrium conditions are the backbone of this correlation. The adjustable exponents of the equation was determined and optimized by means of Genetic Algorithm optimization scheme. This meaningful correlation can also predict the core extracted data with reasonable accuracy.