A model ranking and uncertainty propagation approach for improving confidence in solids transport model predictions

Abstract

The transport of solid particles in pipelines is of interest in the petroleum industry, and is needed to increase flow efficiency in the pipe and prevent pipeline damage due to the particles’ accumulation. To achieve this goal, the velocity of the carrier fluid in the pipe needs to exceed the threshold velocity. Many solids transport models are available for predicting the threshold velocity, but for the same input condition, the predictions of these models may vary by orders of magnitude, and information regarding the confidence of the models’ predictions is not readily available. To resolve these issues, this paper presents a model evaluation and uncertainty propagation approach that uses a novel combination of data clustering, model parameter fine-tuning, model screening and ranking, model uncertainty quantification, and Monte Carlo simulation methods. The inputs are the experimental database for solids transport, a set of solids transport models, and the input condition(s) where the models’ predictions are needed. The outputs of the methodology include the models’ rankings, and the envelopes of the models’ predictions to within a predetermined confidence level. By propagating the uncertainties of the models, experimental data, and input conditions, the highest-ranked models produce velocity envelopes at the 90% confidence level that cover the experimentally-observed values for 92% of the cases; while using the prediction of an individual model does not provide any information regarding the prediction confidence.