Synthesis and Experimental-Modelling Evaluation of Nanoparticles Movements by Novel Surfactant on Water Injection: An Approach on Mechanical Formation Damage Control and Pore Size Distribution

Water injection is used as a widespread IOR/EOR method and promising formation damages (especially mechanical ones) is a crucial challenge in the near-wellbore of injection wells. The magnesium oxide (MgO) NanoParticles (NPs) considered in the article underwater flooding experiment tests to monitor the promising mechanical formation damage (size exclusion) in lab mechanistic scale include micro-scale classical deep bed filtration model, permeability, and pore size distribution. The averaged upper-scale equations were constructed on the water injection basis on the presence of NPs. The model validation to adjust the equation of state was obtained based on fluid samples from the laboratory and simulation tests. The permeability decline (up to 50% initial permeability) was important when the optimum value of capturing the probability coefficient (pa) is 0.7 mismatched on the conventional simulation results. Pore size distribution in each simulation time step based on retention concentrations determined in the sandstone samples. Formation damage analyses on the presence of NPs showed that modification of the static reservoir models has excellent potential regarding porosity and permeability maps, in large-scale simulation. This study displays an improved approach to NPs’ movement through a porous medium which will act as a benchmark for future waterflooding EOR projects in sandstone oil reservoirs or similar basins all over the world.