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

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 Pls 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 Pls with both high coverage and small width. (C) 2012 Elsevier Ltd. All 
rights reserved.