The Prediction of Wax Precipitation by Neural Network and Genetic Algorithm and Comparison With a Multisolid Model in Crude Oil Systems

Abstract

This article introduce a new implementation of the neural network and genetic programming neural network technology in petroleum engineering. An intelligent framework is developed for calculating the amount of wax precipitation in petroleum mixtures over a wide temperature range. Theoretical results and practical experience indicate that feedforward networks can approximate a wide class of function relationships very well. In this work, a conventional feedforward multilayer neural network and genetic programming neural network (GPNN) approach have been proposed to predict the amount of wax precipitation. The introduced model can predict wax precipitation through neural network and genetic algorithmic techniques. The accuracy of the method is evaluated by predicting the amount of wax precipitation of various reservoir fluids not used in the development of the models. Furthermore, the performance of the model is compared with the performance of multisolid model for wax precipitation prediction and experimental data. Results of this comparison show that the proposed method is superior, both in accuracy and generality, over the other models.

Keywords:

• genetic algorithm
• intelligent modeling
• multisolid modeling
• neural network
• wax precipitation