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Graphene is a flexible and transparent conductor which can be used in varied material-apparatus applications, counting solar cells, phones, touch panels, and light-emitting diodes (LED). In this study, to begin with, Graphene aqueous-based nanofluid is processed at separate mass fractions of 0.1 − 0.45 W%. Hence, thermal conductivity of these units was detected at separate temperatures of 25 − 50°C aside the KD2-Pro appliance. Similarly, Rheological behavior at noticed temperatures, for 12.23 and 122.3 S^-1 shear rates, was detected aside the DV2EXTRA-Pro appliance. To shorten the expense of research, neural network designs and fuzzy system were trained to discover addition thermal conductivities and viscosities for unalike temperatures and mass fractions. Purpose of this study is to broaden Fuzzy system and ANN algorithms to predict the TC/VIS therefore it predicts the targeted-input dataset as factual as practicable. Hence, the numerical research was accomplished and related aside Levenberg Marquardt and Orthogonal Distance Regression models of Artificial Neural Networks, and Recursive Least Squares Fuzzy system. To train, 14400 data were placed. To test, 2160 ones, to train-control 2160 ones, and to train-output 10080 ones. Conclusions of comparison between algorithms and Fuzzy, exhibited Fuzzy system was fitted on the three-dimensional data more corrected than LM/ODR designs that leads to a better prediction.

In oil and gas industry, prior prediction of certain properties is needed ahead of exploration and facility design. Viscosity and gas/oil ratio (GOR) are among those properties described through curves with their values varying over a specific range of reservoir pressures. However, the usual single point prediction approach could result into curves that are inconsistent, exhibiting scattered behavior as compared to the real curves. Support Vector Regressors and Functional Networks are explored in this paper to solve this problem. Inputs into the developed models include hydrocarbon and non-hydrocarbon crude oil compositions and other strongly correlating reservoir parameters. Graphical plots and statistical error measures, including root mean square error and average absolute percent relative error, have been used to evaluate the performance of the models. A comparative study is performed between the two techniques and with the conventional feed forward artificial neural networks. Most importantly, the predicted curves are consistent with the shapes of the physical curves of the mentioned oil properties, preserving the need of such curves for interpolation and ensuring conformity of the predicted curves with the conventional properties.