The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs

The authors introduce a new implementation of the neural network (ANN), genetic programming neural network (GPNN), and neuro-fuzzy (NF) technology in petroleum engineering. An intelligent framework is developed for calculating the amount of permeability reduction by asphaltene precipitation in Iranian crude oil reservoirs over a wide pressure, temperature, and solvent mole fraction range. Theoretical results and practical experience indicate that a feed-forward network can approximate a wide class of function relationships very well. In this work, a conventional feed-forward multilayer ANN, GPNN, and NF approach have been proposed to predict the amount of permeability reduction. The accuracy of the method is evaluated by predicting the amount of permeability reduction of various reservoir fluids not used in the development of the models. One of the ways in modeling such systems is using intelligent techniques, which need information about the systems, so, based on some intelligent learning methods, it can provide a suitable model. Furthermore, the performance of the model is compared with the performance of a simple model for permeability reduction prediction, a new correlation, and experimental data. Results of this comparison show that the proposed GPNN method first and then NF method is superior both in accuracy and generality, over the other models.