At
harsh conditions of high pressure high temperature (HPHT), polymers
undergo thermal degradation leading to serious loss in fluid rheological
and filtration properties. Nanoparticles are the most promising additives
proposed to address this challenge. The stability of nanofluids is
perused from various facets including rheological and filtration properties,
shale stability, and zeta potential. The presence of nanoparticles
could amazingly reduce the filtration at high temperatures even by
95%, and it also had a conspicuous effect on shale stability, thermal
conductivity, and zeta potential. Experimental data were fit to rheological
models to determine the best models describing the behavior of the
nanosystem. It was clarified that the Sisko and Mizhari–Berk
models enjoy the highest accuracy among the others. Moreover, a correlation
is developed relating the viscosity of nanofluid to shear rate, temperature,
and nanoparticles’ concentration. The model exposed high accuracy
regarding a high value of average correlation factor, which was 0.994.
Asphaltene precipitation and consequent deposition may result in several operational problems ranging from the wellbore to transmission lines. Despite several studies, stability conditions of the asphaltene in crude oil are still a challenging issue and a potential area of investigation. Refractive Index (RI) is a parameter indicative of the region at which asphaltene becomes stable. In this study, a Committee Machine Intelligent System (CMIS) is incorporated to predict the RI of different crude oils through the existing SARA fractions experimental data. The CMIS itself utilizes different artificial neural networks: Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Least Squares Support Vector Machine (LSSVM). By comparing the results of each artificial neural network with the final output, it was demonstrated that the CMIS increases the generalization capability of the utilized artificial network. The results were compared with two well‐known classical correlations. It was proven that the proposed intelligent system outperforms the classical correlations. At the end, outlier detection was performed to identify data which deviate from the bulk of the data points and obtain the applicability domain of the CMIS model.
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