Predictive Modeling of Holdup in Horizontal Wateroil Flow Using a Neural Network Approach

Abstract

In this work, the application of an artificial neural network (ANN) is proposed to develop a predicting model for the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe. For this, the surface velocities of each fluid and the differential pressure in the pipeline are used as input parameters of the multilayer artificial neural network with backpropagation, while the holdup of the fluids is used as the output parameter for the training. A set of 56 experimental data was obtained in the LabPetro-CEPETRO-UNICAMP laboratory. The best performing results for the predictive model show a mean absolute error (AAPE) of 3.01% and a coefficient of determination 2 of 0.9964 using 15 neurons in the hidden layer of the network and the TanSig transfer function.

Modelo predictivo para la identificación de la fracción volumétrica en flujo bifásico

“…Studies for training a neural network from flow characteristics and a sensor based on electrical capacitance as inputs, to obtain the holdup of water of two-phase oil-water flow were developed by [17,18]. In a horizontal pipe, [19] analyzed flow patterns and data generated from photographs and signals generated by an optical probe using neural networks.…”