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A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2022年第12期23卷 1027-1046页

作者： Fei LV Jia YU Jun ZHANG Peng YU Da-wei TONG Bin-ping WU State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin 300350China

Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.

关键词： drilling efficiency Prediction Earth-rock excavation Stacking-based ensemble learning Improved cuckoo search optimization(ICSO)algorithm Comprehensive effects of various factors Hyper-parameter optimization

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To Improve drilling efficiency by Multi-objective Optimization of Operational drilling Parameters in the Complex Geological drilling Process

To Improve Drilling Efficiency by Multi-objective Optimizati...

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第37届中国控制会议

作者： Chao Gan Weihua Cao Kangzhi Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Electronic Engineering Chiba University

Operational drilling parameters optimization is necessary for increase drilling efficiency in complex geological drilling process. There are four key objectives to evaluate the drilling efficiency, including drilling cost, rate of penetration（ROP）, drill bit life, and drill bit’s specific energy. In this paper, we proposed a multi-objective optimization method to improve drilling efficiency in the complex geological drilling process considering all the four key objectives. Firstly, the characteristics of complex geological drilling process and optimization problems are analyzed to find the vital process parameters and problems for drilling efficiency optimization. Then, a multi-objective optimization model which combines the four key objectives is established.Finally, an improved nondominated sorting genetic algorithm（improved NSGA-II） is used to optimize the operational drilling parameters include weight on bit（WOB） and rotational speed（RS） to make drilling efficiency improved. The real case results demonstrate that our method increases the drilling efficiency in four key objectives and saves the simulation time. The proposed method provides the foundation for intelligent optimization control in complex geological drilling process.

关键词： Operational drilling Parameters Multi-objective Optimization Complex Geological drilling Process drilling efficiency Improved NSGA-II

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