Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data
), Abstract
Rock parameters, including the uniaxial compressive strength (UCS), cohesion, and friction angle, are fundamental in geological and geotechnical engineering. The traditional methods for determining these parameters are often constrained by limitations such as complex operational procedures, time consumption, and difficulty in obtaining in situ parameters. This has led to an exploration of extracting rock parameters through drilling. This paper offers a comprehensive review of the current methodologies for estimating rock parameters derived from drilling tests, encompassing experimental, analytical, numerical, and machine learning (ML) approaches, as well as digital twin (DT) technologies. This review provides an in-depth analysis of recent advancements and research, highlighting the transformative impact of these innovations on the field. It emphasizes the growing application of ML algorithms such as artificial neural networks (ANNs), support vector regression (SVR), random forest (RF), and convolutional neural networks (CNNs) for rock property estimation, underscoring the diversity of techniques utilized. The integration of the DT technology with numerical methods, including finite element analysis (FEA) and discrete element model (DEM), facilitates real-time monitoring, simulation, and optimization of rock parameters extracted from drilling, which can enhance accuracy and robustness even with limited empirical data, representing a notable solution. This review aims to provide a detailed understanding of the application and effectiveness of these methodologies for extracting rock parameters from drilling data.
References
M. Cai. Rock mass characterization and rock property variability considerations for tunnel and cavern design. Rock Mech Rock Eng, 2011, 44: 379–399.
S. Kalantari, H. Hashemolhosseini, A. Baghbanan. Estimating rock strength parameters using drilling data. Int J Rock Mech Min Sci, 2018, 104: 45–52.
B. Jiang, F. L. Ma, Q. Wang, et al. Drilling-based measuring method for the c– φ parameter of rock and its field application. Int J Min Sci Technol, 2024, 34: 65–76.
H. T. Wang, M. M. He. Determining method of tensile strength of rock based on friction characteristics in the drilling process. Rock Mech Rock Eng, 2023, 56: 4211–4227.
M. M. He, Z. Q. Zhang, J. Ren, et al. Deep convolutional neural network for fast determination of the rock strength parameters using drilling data. Int J Rock Mech Min Sci, 2019, 123: 104084.
S. Kalantari, A. Baghbanan, H. Hashemalhosseini. An analytical model for estimating rock strength parameters from small-scale drilling data. J Rock Mech Geotech Eng, 2019, 11: 135–145.
M. M. He, N. Li, Z. Q. Zhang, et al. An empirical method for determining the mechanical properties of jointed rock mass using drilling energy. Int J Rock Mech Min Sci, 2019, 116: 64–74.
M. Mostofi, V. Rasouli, E. Mawuli. An estimation of rock strength using a drilling performance model: A case study in blacktip field, Australia. Rock Mech Rock Eng, 2011, 44: 305–316.
E. Yaşar, P. G. Ranjith, D. R. Viete. An experimental investigation into the drilling and physico–mechanical properties of a rock-like brittle material. J Pet Sci Eng, 2011, 76: 185–193.
H. Karasawa, T. Ohno, M. Kosugi, et al. Methods to estimate the rock strength and tooth wear while drilling with roller-bits—Part 1: Milled-tooth bits. J Energy Resour Technol, 2002, 124: 125–132.
O. Siddig, A. F. Ibrahim, S. Elkatatny. Estimation of rocks’ failure parameters from drilling data by using artificial neural network. Sci Rep, 2023, 13: 3146.
M. Y. Hassan, H. Arman. Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks. Sci Rep, 2022, 12: 20969.
A. Ouladmansour, O. Ameur-Zaimeche, R. Kechiched, et al. Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone reservoirs. Case study: Rhourd Chegga oilfield, Algeria. Geoenergy Sci Eng, 2023, 223: 211511.
Z. Q. Yang, Y. Q. Wu, Y. S. Zhou, et al. Assessment of machine learning models for the prediction of rate-dependent compressive strength of rocks. Minerals, 2022, 12: 731.
Y. M. Li, G. F. Zhao. A numerical integrated approach for the estimation of the uniaxial compression strength of rock from point load tests. Int J Rock Mech Min Sci, 2021, 148: 104939.
A. F. Ibrahim, M. Hiba, S. Elkatatny, et al. Estimation of tensile and uniaxial compressive strength of carbonate rocks from well-logging data: Artificial intelligence approach. J Pet Explor Prod Technol, 2024, 14: 317–329.
M. Nabaei, K. Shahbazi. A new approach for predrilling the unconfined rock compressive strength prediction. Pet Sci Technol, 2012, 30: 350–359.
C. L. Yan, J. G. Deng, B. H. Yu, et al. Borehole stability in high-temperature formations. Rock Mech Rock Eng, 2014, 47: 2199–2209.
F. K. Mody, A. H. Hale. Borehole-stability model to couple the mechanics and chemistry of drilling-fluid/shale interactions. J Pet Technol, 1993, 45: 1093–1101.
N. M. Shahani, B. Ullah, K. S. Shah, et al. Predicting angle of internal friction and cohesion of rocks based on machine learning algorithms. Mathematics, 2022, 10: 3875.
H. Karakul, R. Ulusay. An alternative method for predicting internal friction angle of rock materials. Environ Earth Sci, 2024, 83: 306.
W. C. Maurer. The “perfect–cleaning” theory of rotary drilling. J Pet Technol, 1962, 14: 1270–1274.
Y. M. Li, T. Zhang, Z. F. Tian, et al. Simulation on compound percussive drilling: Estimation based on multidimensional impact cutting with a single cutter. Energy Rep, 2021, 7: 3833–3843.
A. Jarrot, A. Gelman, J. Kusuma. Wireless digital Communication technologies for drilling: Communication in the bits\/s regime. IEEE Signal Process Mag, 2018, 35: 112–120.
L. Sun, C. G. Bu, P. D. Hu, et al. The transient impact of the resonant flexible drill string of a sonic drill on rock. Int J Mech Sci, 2017, 122: 29–36.
Y. M. Zhao, S. Dai. Challenges of rock drilling and opportunities from bio-boring. Biogeotechnics, 2023, 1: 100009.
A. Sapińska-Śliwa, R. Wiśniowski, M. Korzec, et al. Rotary-percussion drilling method—Historical review and current possibilities of application. AGH Drill Oil, Gas, 2015, 32: 313–322.
H. T. Li, X. Y. Chen, Y. F. Meng, et al. A novel method for wireless telemetry during air drilling based on air pressure pulses. Geoenergy Sci Eng, 2024, 234: 212669.
I. Kessai, S. Benammar, M. Z. Doghmane, et al. Drill bit deformations in rotary drilling systems under large-amplitude stick–slip vibrations. Appl Sci, 2020, 10: 6523.
C. Song, J. Chung, J. S. Cho, et al. Optimal design parameters of a percussive drilling system for efficiency improvement. Adv Mater Sci Eng, 2018, 2018: 2346598.
Y. H. Qian, Y. Wang, Z. Q. Wang, et al. The rock breaking capability analyses of sonic drilling. J Low Freq Noise Vib Act Control, 2021, 40: 2014–2027.
X. L. Yue, Z. W. Yue, Y. F. Yan, et al. Experimental study on predicting rock properties using sound level characteristics along the borehole during drilling. Bull Eng Geol Environ, 2023, 82: 310.
Y. M. Li, J. L. Li, Y. H. Wu, et al. Extracting rock parameters through digital drilling test. Rock Mech Rock Eng, 2024, 57: 8215–8241.
B. Chiaia, M. Borri-Brunetto, A. Carpinteri. Mathematical modelling of the mechanics of core drilling in geomaterials. Mach Sci Technol, 2013, 17: 1–25.
Q. Wang, H. K. Gao, Z. H. Jiang, et al. Development and application of a surrounding rock digital drilling test system of underground engineering. Chin J Rock Mech Eng, 2020, 39: 301–310. (in Chinese).
Q. Wang, H. K. Gao, H. C. Yu, et al. Method for measuring rock mass characteristics and evaluating the grouting-reinforced effect based on digital drilling. Rock Mech Rock Eng, 2019, 52: 841–851.
M. Singh, B. Singh. Modified Mohr–Coulomb criterion for non-linear triaxial and polyaxial strength of jointed rocks. Int J Rock Mech Min Sci, 2012, 51: 43–52.
N. Houshmand, S. GoodFellow, K. Esmaeili, et al. Rock type classification based on petrophysical, geochemical, and core imaging data using machine and deep learning techniques. Appl Comput Geosci, 2022, 16: 100104.
Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 2015, 521: 436–444.
J. S. Almeida. Predictive non-linear modeling of complex data by artificial neural networks. Curr Opin Biotechnol, 2002, 13: 72–76.
D. De Ridder, R. P. W. Duin, M. Egmont-Petersen, et al. Nonlinear image processing using artificial neural networks. Adv Imaging Electron Phys, 2003, 126: 351–450.
B. Widrow, M. A. Lehr. 30 Years of adaptive neural networks: Perceptron, madaline, and backpropagation. Proc IEEE, 1990, 78: 1415–1442.
R. S. Sexton, R. E. Dorsey, J. D. Johnson. Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation. Decis Support Syst, 1998, 22: 171–185.
A. Asadi. Application of artificial neural networks in prediction of uniaxial compressive strength of rocks using well logs and drilling data. Procedia Eng, 2017, 191: 279–286.
C. V. Kumar, H. Vardhan, C. S. N. Murthy. Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations. Model Earth Syst Environ, 2022, 8: 141–161.
S. Elkatatny. New approach to optimize the rate of penetration using artificial neural network. Arab J Sci Eng, 2018, 43: 6297–6304.
H. Gamal, A. Alsaihati, S. Elkatatny, et al. Rock strength prediction in real-time while drilling employing random forest and functional network techniques. J Energy Resour Technol, 2021, 143: 093004.
A. J. Smola, B. Schölkopf. A tutorial on support vector regression. Stat Comput, 2004, 14: 199–222.
B. Liu, R. R. Wang, Z. D. Guan, et al. Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data. Tunnell Underground Space Technol, 2019, 91: 102958.
G. T. Ma, Z. M. Chao, Y. Zhang, et al. The application of support vector machine in geotechnical engineering. IOP Conf Ser Earth Environ Sci, 2018, 189: 022055.
X. Chen, W. H. Cao, C. Gan, et al. Semi-supervised support vector regression based on data similarity and its application to rock-mechanics parameters estimation. Eng Appl Artif Intell, 2021, 104: 104317.
N. Ceryan. Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks. J African Earth Sci, 2014, 100: 634–644.
L. Breiman. Random forests. Mach Learn, 2001, 45: 5–32.
T. K. Ho. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell, 1998, 20: 832–844.
J. X. Gu, Z. H. Wang, J. Kuen, et al. Recent advances in convolutional neural networks. Pattern Recognit, 2018, 77: 354–377.
M. Sanei, A. Ramezanzadeh, M. R. Delavar. Applied machine learning-based models for predicting the geomechanical parameters using logging data. J Pet Explor Prod Technol, 2023, 13: 2363–2385.
A. A. Mahmoud, S. Elkatatny, A. Al-AbdulJabbar. Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters. J Pet Sci Eng, 2021, 203: 108574.
D. Y. Li, J. J. Zhao, Z. D. Liu. A novel method of multitype hybrid rock lithology classification based on convolutional neural networks. Sensors, 2022, 22: 1574.
T. M. Hossain, J. Watada, I. A. Aziz, et al. Machine learning in electrofacies classification and subsurface lithology interpretation: A rough set theory approach. Appl Sci, 2020, 10: 5940.
A. M. Gonbadi, S. H. Tabatabaei, E. J. M. Carranza. Supervised geochemical anomaly detection by pattern recognition. J Geochem Explor, 2015, 157: 81–91.
Y. L. Kong, Z. S. Luo. Improving the accuracy of coal-rock dynamic hazard anomaly detection using a dynamic threshold and a depth auto-coding Gaussian hybrid model. Sustainability, 2023, 15: 9655.
G. F. Zhao. Developing a four-dimensional lattice spring model for mechanical responses of solids. Comput Methods Appl Mech Eng, 2017, 315: 881–895.
X. X. Zhou, A. Ghassemi. Finite element analysis of coupled chemo–poro–thermo–mechanical effects around a wellbore in swelling shale. Int J Rock Mech Min Sci, 2009, 46: 769–778.
J. T. Xie, Q. R. Qin, C. H. Fan. Quantitative prediction of fracture distribution of the Longmaxi formation in the Dingshan Area, China using FEM numerical simulation. Acta Geol Sin-Engl Ed, 2019, 93: 1662–1672.
H. A. Aldannawy, A. Rouabhi, L. Gerbaud. Percussive drilling: Experimental and numerical investigations. Rock Mech Rock Eng, 2022, 55: 1555–1570.
T. Saksala. 3D numerical modelling of bit-rock fracture mechanisms in percussive drilling with a multiple-button bit. Int J Numer Anal Methods Geomech, 2013, 37: 325–330.
T. Saksala. Damage–viscoplastic consistency model with a parabolic cap for rocks with brittle and ductile behavior under low-velocity impact loading. Int J Numer Anal Methods Geomech, 2010, 34: 1362–1386.
T. Saksala. Numerical modelling of bit–rock fracture mechanisms in percussive drilling with a continuum approach. Int J Numer Anal Methods Geomech, 2011, 35: 1483–1505.
D. O. Potyondy, P. A. Cundall. A bonded-particle model for rock. Int J Rock Mech Min Sci, 2004, 41: 1329–1364.
L. Scholtès, F. V. Donzé. A DEM model for soft and hard rocks: Role of grain interlocking on strength. J Mech Phys Solids, 2013, 61: 352–369.
S. K. Subbiah, A. Samsuri, A. Mohamad-Hussein, et al. Root cause of sand production and methodologies for prediction. Petroleum, 2021, 7: 263–271.
Z. Y. Zhou, A. B. Yu, S. K. Choi. Numerical simulation of the liquid-induced erosion in a weakly bonded sand assembly. Powder Technol, 2011, 211: 237–249.
D. M. Zhao, Z. S. Cheng, W. W. Zhang, et al. Numerical modeling of thermal behavior during lunar soil drilling. Aerospace, 2023, 10: 472.
N. R. Draper, D. K. J. Lin. Small response-surface designs. Technometrics, 1990, 32: 187–194.
G. F. Zhao, Y. M. Li, B. Y. Jiang. Coupling DDA and 4D-LSM through contact theory for rock cutting. IOP Conf Ser Earth Environ Sci, 2020, 570: 022054.
Y. M. Li, G. F. Zhao, Y. Y. Jiao, et al. A benchmark study of different numerical methods for predicting rock failure. Int J Rock Mech Min Sci, 2023, 166: 105381.
W. G. Zhang, K. K. Phoon. Editorial for Advances and applications of deep learning and soft computing in geotechnical underground engineering. J Rock Mech Geotech Eng, 2022, 14: 671–673.
Z. J. Wang, R. Turko, O. Shaikh, et al. CNN explainer: learning convolutional neural networks with interactive visualization. IEEE Trans Vis Comput Graph, 2021, 27: 1396–1406.
M. Khoshouei, R. Bagherpour, M. H. Jalalian, et al. Investigating the acoustic signs of different rock types based on the values of acoustic signal RMS. Rudarsko-Geološko-Naftni Zbornik, 2020, 35: 29–38.
L. Peyras, P. Rivard, P. Breul, et al. Characterization of rock discontinuity openings using acoustic wave amplitude—Application to a metamorphic rock mass. Eng Geol, 2015, 193: 402–411.
A. Gowida, S. Elkatatny. Prediction of sonic wave transit times from drilling parameters while horizontal drilling in carbonate rocks using neural networks. Petrophysics, 2020, 61: 482–494.
W. G. Zhang, X. Gu, L. B. Tang, et al. Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge. Gondwana Res, 2022, 109: 1–17.
G. Hamada, V. Joseph. Developed correlations between sound wave velocity and porosity, permeability and mechanical properties of sandstone core samples. Pet Res, 2020, 5: 326–338.
H. C. Wang, J. N. Pan, S. Wang, et al. Relationship between macro-fracture density, P-wave velocity, and permeability of coal. J Appl Geophy, 2015, 117: 111–117.
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