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http://dx.doi.org/10.6109/jkiice.2021.25.8.1032

Comparative Study of the Supervised Learning Model for Rate of Penetration Prediction Using Drilling Efficiency Parameters  

Han, Dong-Kwon (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University)
Sung, Yu-Jeong (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University)
Yang, Yun-Jeong (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University)
Kwon, Sun-Il (Department of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.8, 2021 , pp. 1032-1038 More about this Journal
Abstract
Rate of penetration(ROP) is one of the important variables for maximizing the drilling performance. In order to maximize drilling efficiency, it is necessary to increase the drilling speed, and real-time ROP prediction is important so that the driller can identify problems during drilling. The ROP has a high correlation with the drillstring rotational speed, weight on bit, and flow rate. In this paper, the ROP was predicted using a data-driven supervised learning model trained from the drilling efficiency parameters. As a result of comparison through the performance evaluation metrics of the regression model, the root mean square error(RMSE) of the RF model was 4.20 and the mean absolute percentage error(MAPE) was 9.08%, confirming the best predictive performance. The proposed method can be used as a base model for ROP prediction when constructing a real-time drilling operation guide system.
Keywords
ROP; Drillstring rotational speed; Weight on bit; Drilling efficiency parameters; Supervised learning;
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