- 期刊
- OpenAccess
Modeling Rheological Properties Of Oil Wel Cement Slurries Using Multiple Regression Analysis And Artificial Neural Networks
並列摘要
Artificial neural networks (ANN) and multiple regression analysis (MRA) were used to predict the rheological properties of oil well cement slurries. The slurries were prepared using class G oil well cement with a water-cement mass ratio (w/c) of 0.44, and incorporating a new generation polycarboxylate-based high-range water reducing admixture (PCH), polycarboxlate-based mid-range water reducing admixture (PCM), and lignosulphonate-based mid-range water reducing admixture (LSM). The rheological properties were investigated at different temperatures in the range of 23 to 60ºC using an advanced shear-stress/shear-strain controlled rheometer. Experimental data thus obtained were used to develop predictive models based on backpropagation artificial neural networks and multiple regression analysis. It was found that both ANN and MRA depicted good agreement with the experimental data, with ANN achieving more accurate predictions. The developed models could 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%, in the case of ANN and 4.83, 6.32, and 5.05%, in the case of MRA, for slurries incorporating PCH, PCM, and LSM, respectively. The flow curves developed using ANN and MRA allowed predicting the Bingham parameters (yield stress and plastic viscosity) of the oil well slurries with adequate accuracy.