The use of iron oxide nanoparticles in electromagnetic heating application has been proven through several investigations to increase the electromagnetic absorption properties of reservoir, then, significantly increase the generated temperature. A factor that affects the temperature build-up is nanoparticles transport mechanism. When the nanoparticles carrier fluid is in contact with oil, it is possible for mass transfer to occur. Thus, nanoparticles can diffuse from its carrier fluid to oil in the reservoir. This mechanism is advantageous in increasing the oil temperature because it reduces the heat loss. In this study, we investigate the diffusion mechanism of iron oxide nanoparticles from brine to oil. We used 1% NaCl solution as the brine and nanoparticles carrier. The experiments are done with variation of the type of oil, nanoparticles concentration and sonication time. Heavy and asphaltic oil creates barrier for nanoparticles to diffuse, therefore, diffusion of nanoparticles from brine to oil is very small. Increase in nanoparticles concentration and sonication time provides more driving force for nanoparticles to move to oil. However, it is not always as ideal as those conditions, interaction among nanoparticles, such as aggregation creates various unpredictable results in the experiments.

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Electromagnetic heating has been recently introduced as an effective technology to enhance production of heavy and extra-heavy oil reservoir. Several investigations using metal oxide nanoparticles have successfully proven to increase the effectiveness of heating process. Nanoparticles act as thermal-conducting agent during the heating process. Thus, heat distribution along the reservoir strongly depends on the nanoparticles distribution (concentration profile along the reservoir). In this study, we develop a mathematical model to characterize the concentration distribution of nanoparticles along the reservoir during injection phase. The model is developed using material balance and fluid flow in porous media. Several empirical correlations are also adopted to describe the adsorption phenomenon during the nanoparticles flow in the reservoir. Using coreflood experiment data of iron oxide nanoparticles, the model is simulated and fitted to look for several constants and confirm the minimum error. From the simulation results, the model matched with tracer data and had small squared error with nanoparticles data.

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