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Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques

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Abstract

One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations.

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Acknowledgements

The authors would like to express their sincere appreciation to reviewers because of their valuable comments that increased quality of our paper. The financial support from the fundamental research funds for the Natural Science Fund of China (nos. 51879016) and the National Key R&D Program of China, no. 2018YFC1505504 is greatly appreciated.

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Authors and Affiliations

  1. Construction Project Quality Supervision Station, Hechuan, Chongqing, 401520, China

    Xiufeng Liao

  2. School of Science, Engineering and Information Technology, Federation University Australia, PO Box 663, Ballarat, VIC 3353, Australia

    Manoj Khandelwal

  3. School of Civil Engineering, Chongqing University, Chongqing, 400045, China

    Haiqing Yang

  4. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Mohammadreza Koopialipoor

  5. Geotropik-Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Bhatawdekar Ramesh Murlidhar

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  1. Xiufeng Liao
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  2. Manoj Khandelwal
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  4. Mohammadreza Koopialipoor
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  5. Bhatawdekar Ramesh Murlidhar
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Correspondence to Haiqing Yang.

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Liao, X., Khandelwal, M., Yang, H. et al. Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Engineering with Computers 36, 499–510 (2020). https://doi.org/10.1007/s00366-019-00711-6

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