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Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data
https://orcid.org/0000-0001-8984-9809
Precise, rapid, and reliable prediction of rock strength parameters is of great significance for underground engineering. This paper presents a method for predicting rock strength parameters including the Poisson’s ratio (P), elastic modulus (E), and uniaxial compressive strength (UCS) based on computer drilling jumbo measurement while drilling (MWD) data. First, the distribution characteristics and correlation of MWD data are studied; second, a filtering method of MWD data is proposed, which reduces the influence of operational and mechanical factors; finally, an intelligent prediction model of rock mechanics parameters was established, 30 groups of test data were used for application, and the mean absolute percentage error (MAPE) of prediction results for P, E and UCS are 2.11%, 3.11%, and 2.9%, the determination coefficients (R2) are 0.4346, 0.8241, and 0.6616. Compared with the data before optimization, the accuracy of prediction results is improved significantly, it shows that the deep neural network model can accurately predict rock mass parameters.
Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data
https://orcid.org/0000-0001-8984-9809
Precise, rapid, and reliable prediction of rock strength parameters is of great significance for underground engineering. This paper presents a method for predicting rock strength parameters including the Poisson’s ratio (P), elastic modulus (E), and uniaxial compressive strength (UCS) based on computer drilling jumbo measurement while drilling (MWD) data. First, the distribution characteristics and correlation of MWD data are studied; second, a filtering method of MWD data is proposed, which reduces the influence of operational and mechanical factors; finally, an intelligent prediction model of rock mechanics parameters was established, 30 groups of test data were used for application, and the mean absolute percentage error (MAPE) of prediction results for P, E and UCS are 2.11%, 3.11%, and 2.9%, the determination coefficients (R2) are 0.4346, 0.8241, and 0.6616. Compared with the data before optimization, the accuracy of prediction results is improved significantly, it shows that the deep neural network model can accurately predict rock mass parameters.
Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data
https://orcid.org/0000-0001-8984-9809
Precise, rapid, and reliable prediction of rock strength parameters is of great significance for underground engineering. This paper presents a method for predicting rock strength parameters including the Poisson’s ratio (P), elastic modulus (E), and uniaxial compressive strength (UCS) based on computer drilling jumbo measurement while drilling (MWD) data. First, the distribution characteristics and correlation of MWD data are studied; second, a filtering method of MWD data is proposed, which reduces the influence of operational and mechanical factors; finally, an intelligent prediction model of rock mechanics parameters was established, 30 groups of test data were used for application, and the mean absolute percentage error (MAPE) of prediction results for P, E and UCS are 2.11%, 3.11%, and 2.9%, the determination coefficients (R2) are 0.4346, 0.8241, and 0.6616. Compared with the data before optimization, the accuracy of prediction results is improved significantly, it shows that the deep neural network model can accurately predict rock mass parameters.
Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data
Int. J. Geomech.
Zhao, Ruijie (author) / Shi, Shaoshuai (author) / Li, Shucai (author) / Guo, Weidong (author) / Zhang, Tao (author) / Li, Xiansen (author) / Lu, Jie (author)
2023-04-01
Article (Journal)
Electronic Resource
English
Rock strength characterization using measurement while drilling technique
| Springer Verlag | 2020
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