A novel rate of penetration prediction model with identified condition for the complex geological drilling process

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. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The paper presents an online hybrid prediction model based on drilling data for high-accuracy prediction of rate of penetration (ROP). The method utilizes mutual information analysis, combines K-nearest neighbor algorithm and dynamic time warping, establishes ROP prediction model with support vector regression method, and obtains hyperparameters using hybrid bat algorithm and nondominated sorting genetic algorithm II.