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 (w/c) 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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Shahriar et al. (2011) studied this question.