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Production processes optimization through machine learning methods based on geophysical monitoring data
A.V. Osipov 1, E.S. Pleshakova 1, S.T. Gataullin 1

1 MIREA – Russian Technological University,
119454, Russia, Moscow, Vernadsky Ave. 78

 PDF, 3737 kB

DOI: 10.18287/2412-6179-CO-1373

Pages: 633-642.

Full text of article: English language.

Abstract:
The purpose of the article is to create an effective method for low-delay monitoring of the operating state of a drill string and a drill bit without interfering with the proper drilling process. For the drilling process to be continuously controlled, an experimental setup that operates by utilizing the phase-metric method of control was developed. Any movement of the bit causes a change in the electrical characteristics of the probing signal. To obtain a stable signal from a bit immersion depth of up to 250 m, a frequency of probing electrical signals of 166 Hz and an amplitude of up to 500 V were used; the sampling rate of an analog-to-digital converter (ADC) was 10101 Hz. To identify the state of the drill string and the bit based on graphs of time-dependences of changes in the probing signal electrical characteristics, the present authors investigated a number of deep learning methods. Based on the results of the study, a series of capsular neural network methods ( CapsNet ) was chosen. The authors developed modifications of 2D-CapsNet: windowed Fourier transform (WFT) - 2D-CapsNet and frequency slice wavelet transform (FSWT) - 2D-CapsNet. Both of these methods showed a 99% accuracy in determining the transition between two layers of rocks with different properties, which is 2–3% higher than the currently used measurement-while-drilling (MWD) and logging-while-drilling (LWD) rock surveys. Both of these methods unambiguously reveal self-oscillations in the drill string. When determining a fully serviceable bit in the case of self-oscillations, the (FSWT) - 2D-CapsNet method showed an accuracy of 99%.

Keywords:
robotics, artificial intelligence, neural networks, engineering, CapsNet, geophysical monitoring, drilling optimization.

Citation:
Osipov AV, Pleshakova ES, Gataullin ST. Production processes optimization through machine learning methods based on geophysical monitoring data. Computer Optics 2024; 48(4): 633-642. DOI: 10.18287/2412-6179-CO-1373.

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