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Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network

Author

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  • Emad A. Al-Khdheeawi

    (Department of Petroleum Engineering, Curtin University, Kensington 6151, Australia
    Petroleum Technology Department, University of Technology, Baghdad 10066, Iraq)

  • Doaa Saleh Mahdi

    (Petroleum Technology Department, University of Technology, Baghdad 10066, Iraq)

Abstract

Apparent viscosity is of one of the main rheological properties of drilling fluid. Monitoring apparent viscosity during drilling operations is very important to prevent various drilling problems and improve well cleaning efficiency. Apparent viscosity can be measured in the laboratory using rheometer or viscometer devices. However, this laboratory measurement is a time-consuming operation. Thus, in this paper, we have developed a new empirical correlation and a new artificial neural network model to predict the apparent viscosity of drilling fluid as a function of two simple and fast measurements of drilling mud (i.e., March funnel viscosity and mud density). 142 experimental measurements for different drilling mud samples have been used to develop the new correlation. The calculated apparent viscosity from the developed correlation and neural network model has been compared with the measured apparent viscosity from the laboratory. The results show that the developed correlation and neural network model predict the apparent viscosity with very good accuracy. The new correlation and neural network models predict the apparent viscosity with a correlation coefficient ( R ) of 98.8% and 98.1% and an average absolute error (AAE) of 8.6% and 10.9%, respectively, compared to the R of 89.2% and AAE of 20.3% if the literature correlations are used. Thus, we conclude that the newly developed correlation and artificial neural network (ANN) models are preferable to predict the apparent viscosity of drilling fluid.

Suggested Citation

  • Emad A. Al-Khdheeawi & Doaa Saleh Mahdi, 2019. "Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network," Energies, MDPI, vol. 12(16), pages 1-10, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3067-:d:256102
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    References listed on IDEAS

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    1. Si Le Van & Bo Hyun Chon, 2016. "Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application," Energies, MDPI, vol. 9(12), pages 1-20, December.
    2. Si Le Van & Bo Hyun Chon, 2017. "Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO 2 Performance," Energies, MDPI, vol. 10(7), pages 1-20, June.
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    Cited by:

    1. Salaheldin Elkatatny, 2019. "Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks," Sustainability, MDPI, vol. 11(18), pages 1-18, September.
    2. Mohamed Ezzat & Daniel Vogler & Martin O. Saar & Benjamin M. Adams, 2021. "Simulating Plasma Formation in Pores under Short Electric Pulses for Plasma Pulse Geo Drilling (PPGD)," Energies, MDPI, vol. 14(16), pages 1-23, August.

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