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Water injection process is the oldest and high efficient enhanced oil recovery technique. Despite its immense effect on oil recovery, scale deposition is the most common problem which is the result of the difference between injected and formation water ion concentration. In this study, performance of silica nanoparticles on scale formation inhibition is investigated by measuring the conductivity of solutions containing different concentrations of nanoparticles. An optimum amount of silica nanoparticles could increase the amount of conductivity and consequently lowering the scale deposition.…mehr

Produktbeschreibung
Water injection process is the oldest and high efficient enhanced oil recovery technique. Despite its immense effect on oil recovery, scale deposition is the most common problem which is the result of the difference between injected and formation water ion concentration. In this study, performance of silica nanoparticles on scale formation inhibition is investigated by measuring the conductivity of solutions containing different concentrations of nanoparticles. An optimum amount of silica nanoparticles could increase the amount of conductivity and consequently lowering the scale deposition. Since predicting the performance of kinds of nanoparticles on scale precipitation inhibition has been an incentive for most researchers, great efforts have been made in this study to monitor the performance of silica nanoparticles. For this purpose, different artificial intelligence tools were used to correlate the obtained experimental data. Comparison between the results shows the eye-catching performance of the proposed smart models.
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Autorenporträt
Amin Daryasafar received B.S. and M.S. degree in petroleum reservoir engineering from Petroleum University of Technology (PUT), in Iran. His research interests are Enhanced Oil Recovery (EOR), Application of Nano Technology in Petroleum Engineering and Biotechnology. He has published more than 20 papers in international journals and conferences.