A comprehensive modeling in predicting the effect of various nanoparticles on filtration volume of water-based drilling fluids

Abstract

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 (R2) 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.