Physicochemical characterization of crude oil fractions by artificial neural networks

This paper presents a novel approach to the problem of characterization of petroleum fractions. An artificial neural network consisting of a three-layer perceptron is used to predict volume and weight yields, viscosity, specific gravity and sulphur content. The network was trained using assay data relative to crude oils from central Libya and south-west Iran. After training, the predictive capabilities of the perceptron were tested on systems not included in the learning set. In addition, a comparison was made with the estimates provided by a widespread crude-oil evaluation procedure. The results obtained indicate that accuracies can be achieved that are even better than those derived from current estimation methods.