- Title
- Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques
- Creator
- Liao, Xiufeng; Khandelwal, Manoj; Yang, Haiqing; Koopialipoor, Mohammadreza; Murlidhar, Bhatawdekar
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/172031
- Identifier
- vital:14439
- Identifier
-
https://doi.org/10.1007/s00366-019-00711-6
- Identifier
- ISBN:0177-0667
- 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.
- Relation
- Engineering with Computers Vol. 36, no. 2 (Apr 2020), p. 499-510
- Rights
- Copyright © 2020 Springer Nature Switzerland AG. Part of Springer Nature.
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- 0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 0802 Computation Theory and Mathematics; Compressive strength feature; ROP; Optimization; ABC; TBM penetration rate; Strength prediction; Neural-networks; Shear-strength; Algorithm; Performance; Model; Simulation; Beams
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