Cementing Quality Prediction in the Shunbei Block Based on Genetic Algorithm and Support Vector Regression

Abstract

There are a number of factors that can affect the quality of cementing, and they constrain each other. Current cementing quality prediction methods are still in the stage of development, and it is difficult to establish an analytical model for cementing quality prediction that meets the strict requirements of cementing design. In order to accurately predict the cementing quality in the Shunbei block of the Northwest Oilfield, in this study, we established a cementing quality prediction model based on support vector regression (SVR) and optimized the penalty parameter and kernel parameter by using grid search (GS), a Bayesian optimization algorithm (BOA), and a genetic algorithm (GA), which improve the prediction accuracy of SVR. The results show that the smallest root-mean-square error and average relative error (2.318% and 7.30%, respectively) and the highest accuracy are achieved when using GA–SVR as compared to SVR, GS–SVR, and BOA–SVR. Therefore, GA–SVR is suitable for cementing quality prediction in the Shunbei block.

Keywords

• support vector regression (SVR)
• grid search (GS)
• Bayesian optimization algorithm (BOA)
• genetic algorithm (GA)