Application of Least Squares –Support Vector Machines (LS –SVM) For Function Approximations in Steam-Assisted Gravity Drainage (SAGD): Part I –Steam Chamber Volumes

Mohamed Tamer
Supervisor: Prof. Zhangxing (John) Chen and Prof. Saber Elmabrouk


In this presentation,  two LS –SVM regression models are constructed to predict SAGD steam chamber volumes with two different sample sizes. It is shown that the larger the sample size, the more accurate the prediction for LS –SVM models for function approximations.