Experimental study and implementation of supervised machine learning algorithm to predict the flowability of two-phase water-oil in pipeline

Abstract

In the middle-stream petroleum industry, the concurrent flow of oil and water in pipes is a typical occurrence. Several factors have been reported to influence the flowability of two-phase water-oil in pipeline. However, the non-linear relationship between these factors and the flowability of the two-phase water-oil is not yet known. The understanding of these relationships could help in developing models that might be employed in predicting flow behavior. In this study, two-phase water-oil flowability was experimentally investigated and modeled using supervised machine learning algorithms. The performance of each of the models was optimized by evaluating the model with the best-hidden neuron. For the Levenberg-Marquardt backpropagation, Bayesian regularization backpropagation, and scaled conjugate gradient backpropagation networks, the best topologies of 4-8-4, 4-9-4, and 4-10-4 were obtained, and coefficient of determination (R2) of 0.998, 0.999, 0.996 respectively. The predicted kinematic viscosity, dynamic viscosity, pressure drop, and power consumption were consistent with the observed values. The sensitivity analysis revealed that the temperature has the most significant effect on the predicted output. The robustness of using the supervised machine learning technique in modeling the flowability of two-phase water-crude oil mixes in pipelines based on the relationship between the predictors and the targeted variables was demonstrated.