Because of the wide use of water injection for enhanced oil recovery in the oil fields in order to displace oil into the production well, many reservoirs experience scale deposition problem. Scale formation leads to block production tubing and other facilities through the path of production and also significant reductions in productivity. One of the most common methods for preventing or lowering the amount of scale formation is applying the scale inhibitors. In this work, Silica Nanoparticles are used as a scale inhibitor.Conductivity is used as a property of the fluid that shows the amount of ion in the solution that leads us to predict the amount of scale formed in the solution. Finally an optimum amount of Silica Nanoparticles could reduce the rate of conductivity decreasing of the solution and consequently lower the scale deposition which is the aim of this challenging subject in the oil industry.

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Obtaining accurate relative permeability curves from coreflood experiments is imperative for characterizing a reservoir and estimating its production capability. As the relative permeabilities are not directly measurable, they are inferred from the measured data utilizing some mathematical model of the physical process. This procedure is referred to as inverse modelling. The inverse problem is to obtain estimates of the relative permeability functions using the data measured during displacement experiments. It concerns on the unsteady state relative permeabilities that are obtained from waterflood and suspension of silica nanoparticle flood experiments. For each experiment, recovery and pressure drop data were collected and used in a coreflood simulator. The coreflood simulator used in this study is the Sendra. As the relative permeability is a function of saturation, the authors required the estimates of the entire function. The results show that it is possible to determine one set of relative permeability curves that reconcile several nanosilica flooding experiments simultaneously using the history match method.

Filtration volume of drilling fluid is directly associated with the amount of formation damage in hydrocarbon reservoirs. Many different additives are added to the drilling fluid in order to minimize the filtration volume. Nanoparticles have been utilized recently to improve the filtration properties of drilling fluids. Up to now, no model has yet been presented to investigate the effect of nanoparticles on filtration properties of drilling fluids. The impact of various nanoparticles is investigated in this study. Artificial neural network is used as a powerful tool to develop a novel approach to predict the effect of various nanoparticles on filtration volume. Model evaluation is performed by calculating the statistical parameters. The obtained results by the model and the experimental results are in an excellent agreement with average absolute relative error of 2.6636%, correlation coefficient (R 2) of 0.9928, and mean square error of 0.4797 for overall data. The statistical results showed that the proposed model is able to predict the amount of filtration volume with high precision. Furthermore, the sensitivity analysis on the input parameters demonstrated that nanoparticle concentration has the highest effect on filtration volume and should be considered by researchers during process optimization.

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