Predicting the cementing quality in Shunbei Block based on GA-SVR algorithm

In order to accurately predict the cementing quality in the Shunbei Block of Northwest Oilfield, a cementing quality prediction model based on support vector regression (SVR) model was established by means of machine learning method, based on the analysis on the influential factors of cementing quality. Then, its penalty coefficient (C) and kernel function parameter (g) were optimized by using grid search method (GS), Bayesian optimization algorithm (BOA) and genetic algorithm (GA), so as to improve SVR prediction accuracy. Finally, the optimized model was used to calculate one certain well of Shunbei Block. The results show that compared with SVR, GS-SVR and BOA-SVR algorithm, GA-SVR algorithm has the lowest the root-mean-square error (RMSE) and mean relative error (MRE) of predicted cementing quality, which are 2.318 and 7.30%, respectively. Obviously its prediction accuracy is higher and it can be used to predict the cementing quality in the Shunbei Block. This method provides an effective means for the prediction of cementing quality and is helpful to optimize the operation scheme before the cementing, so as to improve the cementing quality.