Artificial neural network model prediction of bitumen/light oil mixture viscosity under reservoir temperature and pressure conditions as a superior alternative to empirical models

Ronald Ssebadduka, Nam Nguyen Hai Le, Ronald Nguele, Olalekan Alade, Yuichi Sugai

Research output: Contribution to journalArticlepeer-review

Abstract

Herein, we show the prediction of the viscosity of a binary mixture of bitumen and light oil using a feedforward neural network with backpropagation model, as compared to empirical models such as the reworked van der Wijk model (RVDM), modified van der Wijk model (MVDM), and Al-Besharah. The accuracy of the ANN was based on all of the samples, while that of the empirical models was analyzed based on experimental results obtained from rheological studies of three binary mixtures of light oil (API 32) and bitumen (API 7.39). The classical Mehrotra–Svrcek model to predict the viscosity of bitumen under temperature and pressure, which estimated bitumen results with an %AAD of 3.86, was used along with either the RVDM or the MVDM to estimate the viscosity of the bitumen and light oil under reservoir temperature and pressure conditions. When both the experimental and literature data were used for comparison to an artificial neural network (ANN) model, the MVDM, RVDM and Al-Besharah had higher R2 values.

Original languageEnglish
Article number8520
JournalEnergies
Volume14
Issue number24
DOIs
Publication statusPublished - Dec 1 2021

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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