Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network

Darbor, Mohammad; Faramarzi, Lohrasb; Sharifzadeh, Mostafa

doi:10.1007/s10064-017-1192-3

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Network Classifiers 221 Multi-layer Perceptron1

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2021

2024

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Cited by 23 publications

(1 citation statement)

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“…Being universal approximators, Multi-layer perceptron (MLP) networks have been used in modeling nonlinear problems including classification [50] and regression [51] tasks. Assuming the input data x i (i = 1, 2, 3, , N ), the network output y is defined in terms of a mapping function shown as below [52]…”

Section: Network Classifiers 221 Multi-layer Perceptronmentioning

confidence: 99%

Application and comparison of feature-based classification models for multistable impact motions of percussive drilling

Afebu

Liu

Papatheou

2021

Journal of Sound and Vibration

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Section: Network Classifiers 221 Multi-layer Perceptronmentioning

confidence: 99%

Application and comparison of feature-based classification models for multistable impact motions of percussive drilling

Afebu

Liu

Papatheou

2021

Journal of Sound and Vibration

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Machine learning-based rock characterisation models for rotary-percussive drilling

2022

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Vibro-impact drilling has shown huge potential of delivering better rate of penetration, improved tools lifespan and better borehole stability. However, being resonantly instigated, the technique requires a continuous and quantitative characterisation of drill-bit encountered rock materials in order to maintain optimal drilling performance. The present paper introduces a non-conventional method for downhole rock characterisation using measurable impact dynamics and machine learning algorithms. An impacting system that mimics bit-rock impact actions is employed in this present study, and various multistable responses of the system have been simulated and investigated. Features from measurable drill-bit acceleration signals were integrated with operated system parameters and machine learning methods to develop intelligent models capable of quantitatively characterising downhole rock strength. Multilayer perceptron, support vector regression and Gaussian process regression networks have been explored. Based on the performance analysis, the multilayer perceptron networks showed the highest potential for the real-time quantitative rock characterisation using considered acceleration features.

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Analysis of rate of penetration prediction in drilling using data-driven models based on weight on hook measurement

et al. 2022

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Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network

Cited by 23 publications

References 42 publications

Application and comparison of feature-based classification models for multistable impact motions of percussive drilling

Application and comparison of feature-based classification models for multistable impact motions of percussive drilling

Machine learning-based rock characterisation models for rotary-percussive drilling

Analysis of rate of penetration prediction in drilling using data-driven models based on weight on hook measurement

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