JACIII Vol.20 No.7 pp. 1103-1111
doi: 10.20965/jaciii.2016.p1103


Intelligent Nadaboost-ELM Modeling Method for Formation Drillability Using Well Logging Data

Chao Gan*, Weihua Cao*,†, Min Wu*, Xin Chen*, Chengda Lu*, Yule Hu**, and Guojun Wen***

*School of Automation, China University of Geosciences
Wuhan 430074, China

**Faculty of Engineering, China University of Geosciences
Wuhan 430074, China

***School of Mechanical Engineering & Electronic Information, China University of Geosciences
Wuhan 430074, China

Corresponding author

July 5, 2016
September 27, 2016
December 20, 2016
formation drillability, correlation analysis, extreme learning machine, improved Adaboost algorithm, deep area
Drillability is a precondition for drilling-trajectory planning and intelligent control, as well as an important foundation for achieving safety, high quality, and efficiency in deep drilling. A formation drillability modeling method based on the Nadaboost-ELM algorithm is proposed. First, well logging parameters are chosen as inputs of the extreme learning machine (ELM) model, whose output is the formation drillability. Then, the models are trained as weak learners using the improved Adaboost algorithm. Finally, the weak learners are combined into a strong learner. The proposed modeling method is used for regression and prediction. In the regression aspect, the comparison results indicate that the proposed method has higher accuracy than methods in other studies, e.g., multiple regression (MR), grey model (GM), particle-swarm optimization back-propagation (PSO-BP), etc. In the prediction aspect, the results show that the proposed method is better than other prediction methods, e.g., MR, GM, BP, PSO-BP, ELM, and Adaboost-ELM at improving the model’s prediction accuracy, which provides a foundation for intelligent geological drilling.
Cite this article as:
C. Gan, W. Cao, M. Wu, X. Chen, C. Lu, Y. Hu, and G. Wen, “Intelligent Nadaboost-ELM Modeling Method for Formation Drillability Using Well Logging Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.7, pp. 1103-1111, 2016.
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