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Rao, K. U. M.; Bhatnagar, Anupam; Misra, Biswapriya B.

doi:10.1023/a:1013864423562

Cited by 22 publications

(4 citation statements)

References 5 publications

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“…Torsional vibrations lead to the development of longitudinal vibrations. According to the results of the analysis of bit cutters, failures are mainly associated with polycrystalline wear, abrasive wear and cutter failure [25], [26]. The most common wear mechanism for PDC bits in rock drilling is abrasion.…”

Section: A Problems Of Efficient Drillingmentioning

confidence: 99%

Machine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimization

et al. 2023

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The purpose of the article is to create an effective method to monitor the state of the drill string and the bit without interfering with the drilling process itself in low-time delay mode. For continuous monitoring of the well drilling process, an experimental setup was developed that operates on the basis of the use of the phase-metric method of control. 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; sampling rate (analog-to-digital converter) ADC -10101 Hz. To identify the state of the drill string and the bit according to the graphs of dependences of changes in the electrical characteristics of the probing signal on time, the authors of the article investigated a number of deep learning methods, based on the results of the research, a line of capsule neural network (CapsNet) methods was selected. The authors have developed two modifications of 1D-CapsNet and Windowed Fourier Transform (WFT) -2D-CapsNet. To identify the transition between two rock layers with different properties, WFT-2D-CapsNet showed an accuracy of 99%, which is 2-3% higher than the results of modern rock studies based on measurement-while-drilling (MWD) and logging-while-drilling (LWD) methods. The WFT-2D-CapsNet method unambiguously detects selfoscillations in the drill string and detects the good condition of the bit with an accuracy of 99%.

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Section: A Problems Of Efficient Drillingmentioning

confidence: 99%

Machine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimization

et al. 2023

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“…From the test data, it was noticed that both thrust and torque would vary almost in the same way at different bit speed. In a rock drilling, the torque and penetration rate of the drill bit could largely influence by operating thrust and speed [23]. Sreenivasulu and Rao [26] studied the significant effect of drill bit diameter, penetration rate, and spindle speed on the bit force and torque while the aluminum alloy is drilling.…”

Section: Literature Reviewmentioning

confidence: 99%

“…During the drilling action, it is hypothesized that the change of thrust is due to using a different set of drill operating conditions and rock strength. The change of thrust while drilling is well associating with machine operating settings and rock strength [1,3,5,14,15,[18][19][20][23][24][25]. The equation, as shown below, can imply the drilling process.…”

Section: Analysis Of Datamentioning

confidence: 99%

Experimental investigation on potential use of drilling parameters to quantify rock strength

Lakshminarayana¹,

Tripathi²,

Pal

2021

Geo-Engineering

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The uniaxial compressive strength (UCS) represents the strength of the rock. It frequently requires during the introductory phase of mining projects such as tunneling, rock excavation, blast hole designs, etc. Usually, the determination of UCS of rocks is carrying out in a concerned laboratory. The main drawback of determining the UCS in a laboratory requires at least five core samples of high-grade quality. Many problems and limitations are associated with removing the core, and also preparing the test specimen for UCS is tedious, time-consuming, and expensive. Therefore an attempt is made to develop an efficient indirect method to estimate the UCS of rocks without using the core samples. In this experimental investigation, the drilling response, such as thrust, is gathered by drill tool dynamometer considering the different drill operating parameters. The prediction model is developed with a regression technique using the measured thrust and calculated torque. The prediction capacity and validation of the model are carried out using the standard procedure. The experimental results show that the model could explain the variance in UCS up to 93.60%. RMSE and MAPE values in terms of percentage are 3.49% and 11.27%, respectively. Besides, the model's validation is checked for sandstone and limestone having the UCS 28 MPa and 35 MPa, respectively, and yielded the best prediction results with an error of 8.51% and 8.01% suggesting that the developed model could predict the UCS of sedimentary rock types within acceptable error limit, and reasonably. The correlation of UCS of rocks and drilling specific energy is also tested and found that linear relationship between them with an R2 value of 92.10%.

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“…(1) and Eq. ( 2) are valid only in a limit range of trust forces [2][3][4], but the bit-rock interaction mechanism for impragnated diamond bits was not illustrated.…”

Section: Introductionmentioning

confidence: 99%

Development of a Rotary Drilling Mechanism for Impregnated Diamond Bits

Zheng

Itakura

2014

AMR

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Impregnated diamond core bits are often used in core drilling for geological surveys. A tri-state theory is proposed to describe the three states in drilling processes for a diamond particle or a bit. These states are contacting state, cutting state, and ploughing state. Furthermore, the relations among thrust and other mechanical parameters are also summarized. The optimal value of the specific energy is in the cutting state. Several experiment results verified the proposed model. This mechanism is useful for selecting operation parameters in core drilling using impregnated diamond core bits.

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Untitled

Cited by 22 publications

References 5 publications

Machine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimization

Machine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimization

Experimental investigation on potential use of drilling parameters to quantify rock strength

Development of a Rotary Drilling Mechanism for Impregnated Diamond Bits

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