Artificial intelligence method for predicting the maximum stress of an off-center casing under non-uniform ground stress with support vector machine

The situation of an off-center casing under non-uniform ground stress can occur in the process of drilling a salt-gypsum formation, and the related casing stress calculation has not yet been solved analytically. In addition, the experimental equipment in many cases cannot meet the actual conditions and the experimental cost is very high. These comprehensive factors cause the existing casing design to not meet the actual conditions and cause casing deformation, affecting the drilling operation in Tarim oil field. The finite element method is the only effective method to solve this problem at present, but the re-modelling process is time-consuming because of the changes in the parameters, such as the cement properties, casing centrality, and the casing size. In this article, an artificial intelligence method based on support vector machine (SVM) to predict the maximum stress of an off-center casing under non-uniform ground stress has been proposed. After a program based on a radial basis function (RBF)-support vector regression (SVR) (ε-SVR) model was established and validated, we constructed a data sample with a capacity of 120 by using the finite element method, which could meet the demand of the nine-factor ε-SVR model to predict the maximum stress of the casing. The results showed that the artificial intelligence prediction method proposed in this manuscript had satisfactory prediction accuracy and could be effectively used to predict the maximum stress of an off-center casing under complex downhole conditions. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at 10.1007/s11431-019-1694-4 and is accessible for authorized users.