Prediction of Riser Base Pressure in a Multiphase Pipeline-Riser System Using Artificial Neural Networks

  • A.B. Ehinmowo Department of Chemical & Petroleum Engineering, University of Lagos, Nigeria
  • S.A. Bishop Mathematics Department, Covenant University, Ogun, Nigeria
  • N.M. Jacob
Keywords: ANN, multiphase flow, pipeline-riser, riser base pressure, slug flow


In the multiphase flow of oil and gas in pipeline-riser systems, reliable pressure measurements and monitoring is of
utmost importance for flow assurance. These measurements are usually obtained using remote pressure
measuring gauges and other devices. They are employed in the automatic slug flow control technique. However,
these devices are quite expensive and often require calibration at intervals to guarantee accuracy and precision.
There is therefore, the need for suitable alternatives. In this study, a feed-forward back propagation artificial
neural network (ANN) for predicting riser base pressure in offshore pipeline riser systems is presented. A total of
16,870 experimental data sets were used to develop the ANN model. The results show near perfect predictions
with an average mean square error of 0.00207197 and regression correlation coefficient, R values as high as
0.99919. The models obtained from this work can be pivotal to the development of data driven control of slug in
pipeline-riser systems.


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How to Cite
Ehinmowo, A., Bishop, S., & Jacob, N. (2018). Prediction of Riser Base Pressure in a Multiphase Pipeline-Riser System Using Artificial Neural Networks. Journal of Engineering Research, 23(2), 25-34. Retrieved from