Method for predicting the properties of crude oils by the application of neural networks

A method for predicting the properties of crude oils by the application of neural networks articulated in phases and characterized by determining the T2 NMR relaxation curve of an unknown crude oil and converting it to a logarithmic relaxation curve; selecting the values of the logarithmic relaxation curve lying on a characterization grid; entering the selected values as input data for a multilayer neural network of the back propagation type, trained and optimized by means of genetic algorithms; predicting, by means of the trained and optimized neural network, the physico-chemical factors of the unknown crude oil. The method comprises a training and optimization process of the multilayer neural network of the back propagation type. The method thus defined allows the most representative physico-chemical factors of crude oils to be predicted rapidly and without onerous laboratory structures, or alternatively the distillation curve of crude oils with an acceptable approximation degree.