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Neural Computing and Applications

Erschienen in:

01.06.2012 | Original Article

An intelligent approach to evaluate drilling performance

verfasst von: A. Bhatnagar, Manoj Khandelwal

Erschienen in: Neural Computing and Applications | Ausgabe 4/2012

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Abstract

In this paper, an attempt has been made to predict the rate of penetration (ROP) of rocks by incorporating thrust, revolutions per minute (rpm), flushing media and compressive strength of rocks using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 4-7-1 architecture was trained using 472 experimental data sets of sandstone, limestone, rock phosphate, dolomite, marble and quartz-chlorite-schist rocks. A total of 146 new data sets were used for the testing and comparison of the ROP by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of ROP. The coefficient of determination by ANN was 0.985, while coefficient of determination was 0.629 for rate of penetration. The mean absolute error (MAE) for rate of penetration by ANN was 0.3547, whereas MAE by MVRA was 1.7499.

Vorheriger Artikel Wavelet-based adaptive robust control for a class of MIMO uncertain nonlinear systems

Nächster Artikel A new artificial neural network ensemble based on feature selection and class recoding

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Titel: An intelligent approach to evaluate drilling performance
verfasst von: A. Bhatnagar
Manoj Khandelwal
Publikationsdatum: 01.06.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0457-6

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