A novel rate of penetration prediction model with identified condition for the complex geological drilling process. (April 2021)

• Record Type: Journal Article
• Authors: Zhou, Yang; Chen, Xin; Zhao, Haibin; Wu, Min; Cao, Weihua; Zhang, Yongchun; Liu, Haibo
• Is Part Of: Journal of process control. Volume 100(2021)
• Page Start: 30
• Page End: 40
• Publication Date: 2021-04
• Subjects: Rate of penetration (ROP) -- Drilling conditions -- Support vector regression (SVR) -- Hybrid bat algorithm (HBA) -- Nondominated sorting genetic algorithm II (NSGA-II); Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.02.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: The accurate prediction of rate of penetration (ROP) has a crucial role in improving efficiency and minimizing cost in geological drilling process. Considering the drilling characteristics of strong nonlinearity, complexity, multiple variables and drilling conditions in drilling process, an online hybrid prediction model based on the drilling data is developed to achieve high accuracy prediction of the ROP. First, mutual information analysis is used to determine the appropriate model inputs. Then, k-nearest neighbor algorithm and dynamic time warping (KNN–DTW) are combined to identify drilling condition. After that, ROP prediction model is established by support vector regression (SVR) method. The hyperparameters of SVR method are obtained by hybrid bat algorithm (HBA) and nondominated sorting genetic algorithm II (NSGA-II) based on the identified drilling condition. Finally, a modified sliding window method is developed to update the prediction model to deal with complex and variable drilling process. The simulation results show that our method has higher accuracy than other methods, and our method can identify the drilling condition and provide guidance for the drilling operation. Highlights: Modeling of rate of penetration in complex geological drilling process using machine learning techniques. Drilling condition identification by KNN–DTW method for drilling process. Optimal hyperparameters are determined according to identified drilling condition. Modified sliding window method is developed to update the hybrid prediction model. The developed prediction model lays a foundation for the optimization of drilling process.