Uniaxial compressive strength (UCS) is a fundamental indicator of formation hardness, playing a vital role in evaluating geomechanical properties during drilling process. Accurate UCS prediction enables real-time assessment of formation conditions, contributing to improved drilling safety and efficiency. This study proposes a multi-source data fusion approach that integrates vibration data with conventional drilling parameters to enhance UCS prediction accuracy. To address the inconsistency in time scales between the two data sources, a piecewise cubic Hermite interpolation method is applied for temporal alignment. The fused dataset is then used to retrain an extreme learning machine model. Experimental validation is conducted using data collected from a surface drilling test site. Results demonstrate that the proposed method significantly outperforms single-source prediction models, highlighting the effectiveness of vibration-assisted data fusion in real-time UCS estimation.

1. [1] Y. Yao, N. Yao, C. Liang, H. Wei, H. Song, and L. Wang, “Accurate measurement method of drilling depth based on multi-sensor data fusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.3, pp. 367-374, 2022. https://doi.org/10.20965/jaciii.2022.p0367
2. [2] H. Wei, N. Yao, H. Tian, Y. Yao, J. Zhang, and H. Li, “A predicting model for near-horizontal directional drilling path based on bp neural network in underground coal mine,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.3, pp. 279-288, 2022. https://doi.org/10.20965/jaciii.2022.p0279
3. [3] V. Palchik, “Influence of porosity and elastic modulus on uniaxial compressive strength in soft brittle porous sandstones,” Rock Mechanics and Rock Engineering, Vol.32, No.4, pp. 303-309, 1999. https://doi.org/10.1007/s006030050050
4. [4] A. Tuğrul and I. Zarif, “Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from turkey,” Engineering Geology, Vol.51, No.4, pp. 303-317, 1999. https://doi.org/10.1016/S0013-7952(98)00071-4
5. [5] M. Karakus and B. Tutmez, “Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, schmidt hammer and sonic velocity,” Rock Mechanics and Rock Engineering, Vol.39, pp. 45-57, 2006. https://doi.org/10.1007/s00603-005-0050-y
6. [6] S. Yagiz, “Correlation between slake durability and rock properties for some carbonate rocks,” Bulletin of Engineering Geology and the Environment, Vol.70, pp. 377-383, 2011. https://doi.org/10.1007/s00603-005-0050-y
7. [7] N. Zhang, Y. Zhang, and K. Yao, “An optimized identification method of coal-bearing stratum lithology,” J. of Mine Automation, Vol.46, No.7, pp. 100-106, 2020. https://doi.org/10.13272/j.issn.1671-251x.2020010037
8. [8] Z. Liu, D. Li, Y. Liu, B. Yang, and Z. Zhang, “Prediction of uniaxial compressive strength of rock based on lithology using stacking models,” Rock Mechanics Bulletin, Vol.2, No.4, Article No.100081, 2023. https://doi.org/10.1016/j.rockmb.2023.100081
9. [9] C. Jia, F. Chen, M. Lei, J. Huang, C. Shi, and D. Liu, “Determination method of rock strength based on digital drilling parameters,” J. of Southwest Jiaotong University, Vol.60, pp. 1-10, 2024. https://doi.org/10.3969/j.issn.0258-2724.20230328
10. [10] N. Klyuchnikov, A. Zaytsev, A. Gruzdev, G. Ovchinnikov, K. Antipova, L. Ismailova, E. Muravleva, E. Burnaev, A. Semenikhin, A. Cherepanov et al., “Data-driven model for the identification of the rock type at a drilling bit,” J. of Petroleum Science and Engineering, Vol. 178, pp. 506-516, 2019. https://doi.org/10.1016/j.petrol.2019.03.041
11. [11] H. Yang, S. Zhang, J. Qin, and Y. Yuan, “Identification technology of coalbed methane horizontal well while drilling based on multi-information integration,” Coal Science and Technology, No.5, 2016. https://doi.org/10.13199/j.cnki.cst.2016.05.020
12. [12] J. Qiu, X. Yin, Y. Pan, X. Wang, and M. Zhang, “Prediction of uniaxial compressive strength in rocks based on extreme learning machine improved with metaheuristic algorithm,” Mathematics, Vol.10, No.19, Article No.3490, 2022. https://doi.org/10.3390/math10193490
13. [13] X. Shi, G. Liu, X. Gong, J. Zhang, J. Wang, and H. Zhang, “An efficient approach for real-time prediction of rate of penetration in offshore drilling,” Mathematical Problems in Engineering, Vol. 2016, No.1, Article No.3575380, 2016. https://doi.org/10.1155/2016/3575380
14. [14] Y. Xu, D. Zhang, T. Xian, Z. Ma, H. Gao, and Y. Ma, “Two-direction prediction method of drilling fluid based on os-elm for water well drilling,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.4, pp. 594-602, 2023. https://doi.org/10.20965/jaciii.2023.p0594
15. [15] C. Gan, W. Cao, M. Wu, K. Liu, X. Chen, Y. Hu, and F. Ning, “Two-level intelligent modeling method for the rate of penetration in complex geological drilling process,” Applied Soft Computing, Vol.80, pp. 592-602, 2019. https://doi.org/10.1016/j.asoc.2019.04.020
16. [16] X. Yang, M. Wu, C. Lu, W. Li, L. Chen, and S. Du, “Prediction of rate of penetration based on drilling conditions identification for drilling process,” Neurocomputing, Vol.579, Article No.127439, 2024. https://doi.org/10.1016/j.neucom.2024.127439
17. [17] C. Gan, W. Cao, L. Wang, K. Liu, and M. Wu, “An improved dynamic optimization control system for the drilling rate of penetration (rop) and its industrial application,” IEEE Trans. on Industrial Electronics, Vol.70, No.6, pp. 6201-6208, 2022. https://doi.org/10.1109/TIE.2022.3199916