Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems

• Main Authors: Aref Hashemi Fath, Farshid Madanifar, Masood Abbasi
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2020-03-01
• Series: Petroleum
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405656118301020

Exact determination of pressure-volume-temperature (PVT) properties of the reservoir oils is necessary for reservoir calculations, reservoir performance prediction, and the design of optimal production conditions. The objective of this study is to develop intelligent and reliable models based on multilayer perceptron (MLP) and radial basis function (RBF) neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure, reservoir temperature, oil gravity (API), and gas specific gravity. These models were developed and tested using a total of 710 experimental data sets representing the samples of crude oil from various geographical locations around the world. Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses. The results indicated that the proposed models outperform the considered empirical correlations, providing a strong agreement between predicted and experimental values, However, the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model. Keywords: Solution gas oil ratio, Multilayer perceptron, Radial basis function, Empirical correlation