An Prediction Method for Determining the Maximum von Mises Stress in Casing Based on SVM

In order to predict the maximum stress of uncentered casing under non-uniform in-situ stress and improve the safety of casing, a prediction method of casing’s maximum von Mises stress based on artificial intelligence SVM is studied. First, the key factors affecting the maximum stress of casing are determined, including non-uniform geologic stress, elastic modulus and Poisson's ratio of cement sheath, eccentricity of casing, etc. Then the "experimental" samples of casing stress are constructed by using ANSYS software. Finally the \varepsilon \rm - SVR model is established to realize the prediction of casing’s maximum stress. Through self-learning, the SVM regression method based on RBF kernel achieves good accuracy for training samples. For the five test samples, the average relative error is only 1.32%, which means that this method can meet the needs of engineering application. In particular, this method can be used to quickly solve the maximum stress of uncentered casing under non-uniform in-situ stress.The research results provide theoretical basis for site safety construction.