Abstract: The drilling rate is related to drilling operation parameters,drilling fluid performance and drilling tool assembly.Accurate prediction of ROP can effectively calculate drilling costs and drilling time,thereby guiding the design of drilling process parameters,optimizing drilling parameters,rationally arranging drilling rigs and drilling staff,and providing a basis for drilling designers.Combined with current machine learning and big data processing,a drilling rate prediction model based on integrated learning is established by using the historical drilling data of Tuha oilfield in western China.The ensemble members include k-nearest neighbour (KNN),support vector ma-chine (SVM),decision tree (DT),random forest (RF).Seven feature influencing factors are input,including well depth,bit pressure,pump pressure,density,viscosity,pump flow rate,and rotary speed.The goodness of fit used as the evaluation method of ROP prediction,and the results show that the prediction output of the ensemble learning model is higher than that of any single model.Taking well 7-13 as an example,the prediction effect reaches more than 0.93.In addition,this study also explores the combination of different ensemble members.Combined with time cost and goodness of fit,it is found that the optimal combination is KNN+SVR+RF.The goodness of fit is in wells 7-13,8-17,and 4-10 reaches 0.937 8,0.918 7 and 0.912 4.Finally,taking SVR as an example,the fitting accuracy of the optimized single model is still lower than any group of combined models.Further investigation reveals that both the diversity and high accuracy of ensemble members are required to obtain an effective integrated model.These observations demonstrate that the proposed model offers a promising alternative solution for ROP prediction.

Key words: Average principle, Integrated model, Parameter optimization, Regression prediction, ROP