NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment

Authors:
Ronay Ak;Yanfu Li;Valeria Vitelli;Enrico Zio;Enrique LóPez Droguett;Carlos Magno Couto Jacinto
Affiliations:
Chair on Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, ícole Centrale Paris, Grande Voie des Vignes, Chítenay-Malabry 92290, ...;Chair on Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, ícole Centrale Paris, Grande Voie des Vignes, Chítenay-Malabry 92290, ...;Chair on Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, ícole Centrale Paris, Grande Voie des Vignes, Chítenay-Malabry 92290, ...;Chair on Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, ícole Centrale Paris, Grande Voie des Vignes, Chítenay-Malabry 92290, ...;Center for Risk Analysis and Environmental Modeling, Federal University of Pernambuco, Recife, Brazil;Petrobras Research Center, CENPES, Petrobras, Rio de Janeiro, Brazil
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.