Modeling Rheological Properties of Oil Well Cement Slurries Using Artificial Neural Networks
Publication: Journal of Materials in Civil Engineering
Volume 23, Issue 12
Abstract
An artificial neural network (ANN) model was developed to predict the rheological properties of oil well cement slurries. The slurries were prepared by using Class G oil well cement with a water-cement ratio () of 0.44 and incorporating three different chemical admixtures, including a new-generation polycarboxylate-based high-range water-reducing admixture (PCH), polycarboxylate-based midrange water-reducing admixture (PCM), and lingosulphonate-based midrange water-reducing admixture (LSM). The rheological properties were investigated at different temperatures in the range of 23 to 60°C by using an advanced shear-stress/shear-strain controlled rheometer. A back-propagation neural network was designed and trained by using the experimental flow curves. The shear rate, dosage of admixture, and test temperature were considered as input parameters, and the measured shear stress was the output parameter. The trained ANN was not only capable of accurately predicting the shear flow used for its training, but could also effectively predict the rheological properties of new slurries designed within the range of input parameters of the experimental database with an absolute error of 3.43, 3.17, and 2.82% for slurries incorporating PCH, PCM, and LSM, respectively. The flow curves developed by using the ANN model allowed the prediction of the Bingham parameters (yield stress and plastic viscosity) of the slurries with adequate accuracy.
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References
American National Standards Institute, New York/American Petroleum Institute (ANSI/API). (2005). “Recommended practice for testing well cements.” RP 10B-2, Washington, DC, 171.
Bruni, C., Forcellese, A., Gabrielli, F., and Simoncini, M. (2006). “Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques.” J. Material Processing Technology, 177(1-3), 323–326.
Demuth, H., Beale, M., and Hagan, M. (2008). Neural network tool box 6 for use with MATLAB R2008a, Math Works, Natick, MA, 5.2–5.72.
El-Chabib, H., and Nehdi, M. (2005). “Neural network modelling of properties of cement-based materials demystified.” Adv. Cem. Res., 17(3), 91–102.
El-Chabib, H., Nehdi, M., and Sonebi, M. (2003). “Artificial intelligence model for flowable concrete mixtures used in underwater construction and repair.” ACI Mater. J., 100(2), 165–173.
Guillot, D. (2006). “Rheology of well cement slurries.” Well cementing, E. B. Nelson and D. Guillot, eds., Schlumberger, Sugar Land, TX, 93–142.
Nehdi, M., and Al-Martini, S. (2007). “Effect of chemical admixtures on rheology of cement pastes at high temperature.” J. ASTM Int., 4(3), 17.
Nehdi, M., El-Chabib, H., and El-Naggar, M. (2001). “Predicting the performance of self-compacting concrete mixtures using artificial neural networks.” ACI Mater. J., 98(5), 394–401.
Nehdi, M., and Rahman, M. A. (2004). “Estimating rheological properties of cement pastes using various rheological models for different test geometry, gap and surface friction.” Cem. Concr. Res., 34(11), 1993–2007.
Nelson, E. B., Michaux, M., and Drochon, B. (2006). “Chemistry and characterization of portland cement.” Well cementing, E. B. Nelson and D. Guillot, eds., Schlumberger, Sugar Land, TX, 23–48.
Orban, J., Parcevaux, P., and Guillot, D. (1986). “Influence of shear history on the rheological properties of oil well cement slurries.” 8th Int. Congress on the Chemistry of Cement, Vol. 6, Abla Gráfica Editora, Rio de Janiero, Brazil, 243–247.
Ramachandran, V. S., Malhotra, V. M., Jolicoeur, C., and Spiratos, N. (1997). Superplasticizers: Properties and applications in concrete, Materials Technology Laboratory, CANMET, Natural Resources Canada, Ottawa, 43–150.
Ravi, K. M., and Sutton, D. L. (1990). “New rheological correlation for cement slurries as a function of temperature.” SPE 20499, 65th Annual Technical Conf. and Exhibition of the Society of Petroleum Engineers, Society of Petroleum Engineers, Richardson, TX, 455–462.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning internal representation by error propagation.” Parallel distributed processing, Vol. 1, MIT Press, Cambridge, MA, 318–362.
Saak, W. A. (2000). “Characterization and modeling of the rheology of cement paste: With application toward self-flowing materials.” Ph.D. thesis, Northwestern Univ., Evanston, IL, 283.
Shahriar, A., and Nehdi, M. (2010). “Effect of chemical admixture on rheology of oil well cement slurries.” Const. Mater., in press.
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Published In
Journal of Materials in Civil Engineering
Volume 23 • Issue 12 • December 2011
Pages: 1703 - 1710
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Nov 1, 2010
Accepted: May 26, 2011
Published online: May 28, 2011
Published in print: Dec 1, 2011
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