Computer and Information Science, ISSN 1913-8989, Vol. 1, No. 1, February 2008

The main goal of the presented work is to analyse the performance of the Multi-Layer Perceptron (MLP) neural network for flow regime classification based on sets of simulated Electrical Capacitance Tomography (ECT) data. Normalised ECT data have been used to separately train several MLPs employing various commonly used back-propagation learning algorithms, namely the Levenberg-Marquardt (LM), Quasi-Newton (QN) and Resilient-Backpropagation (RP), to classify the gas-oil flow regimes. The performances of the MLPs have been analysed based on their correct classification percentage (CCP). The results demonstrate the feasibility of using MLP, and the superiority of LM algorithm for flow regime classification based on ECT data. 1. Introduction Knowledge of flow regimes is essential particularly for investigation involving multi-component flows such as in oil production. Many types of flow regime could interchangeably form along the pipeline during oil transportation. Since pipelines are normally concealed, a special technique is required in order to obtain information regarding flow regimes form in a pipeline.