Basarir, H, Wesseloo, J, Karrech, A, Pasternak, E & Dyskin, A 2017, 'The use of soft computing methods for the prediction of rock properties based on measurement while drilling data', in J Wesseloo (ed.), Deep Mining 2017: Proceedings of the Eighth International Conference on Deep and High Stress Mining
, Australian Centre for Geomechanics, Perth, pp. 537-551, https://doi.org/10.36487/ACG_rep/1704_36_Basarir
Due to recent technological advancements drilling operations conducted for different purposes such as exploration, blasting and even grouting are not considered as auxiliary operations any longer. On the contrary, nowadays onsite drilling operations are considered as important resources for getting more information about rock properties. Many researchers have been working on measurement while drilling (MWD) techniques and their possible use for the prediction of rock mass properties.
This paper presents a literature survey on the use of MWD technology for the prediction of rock mass properties. The survey indicates that the analysis and interpretation of MWD data is as important as recording the data. Both blackbox modelling such as regression and soft computing or grey-box modelling techniques are used as a tool for the analysis and interpretation of MWD data. This paper presents a case study showing the integration of soft computing methods such as adaptive fuzzy inference system (ANFIS) with MWD data for the prediction of rock mass properties such as rock quality designation (RQD). The results indicated that such soft computing methods can successfully be used as an analysis and interpretation tool.
Keywords: measurement while drilling (MWD), adaptive neuro fuzzy inference system (ANFIS), rock quality designation (RQD), soft computing
Adebayo, B & Bello, WA 2014, ‘Discontinuities effect on drilling condition and performance of selected rocks in Nigeria’, International Journal of Mining Science and Technology, vol. 24,
, pp. 603–608.
Akun, M, 1997, Effect of operational parameters and formation properties on drillability in surface set diamond core drilling, PhD thesis, Middle East Technical University, Ankara.
Basarir, H & Karpuz, C 2016, ‘Preliminary estimation of rock mass strength using diamond bit drilling operational parameters’, International Journal of Mining, Reclamation and Environment, vol. 30,
, pp. 145–164.
Basarir, H, Tutluoglu, L & Karpuz, C 2014, ‘Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions’, Engineering Geology, vol. 173,
, pp. 1–9.
Beattie, N 2009, Monitoring-While-Drilling for Open-Pit Mining in a Hard Rock Environment: An Investigation of Pattern Recognition Techniques Applied to Rock Identification, PhD thesis, Queen’s University, Kingston.
Brown, E, Carter, P & Robertson, W 1984, ‘Experience with a prototype instrumented drilling rig’, Geodrilling, February 1984,
Cabalar, AF, Cevik, A & Gokceoglu, C 2012, ‘Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering’, Computers and Geotechnics, vol. 40,
, pp. 14–33.
CATERPILLAR 2016a, CAT Aquila, WesTrac, Midland, Western Australia, viewed 17 August 2016,
CATERPILLAR 2016b, CAT Terrain, WesTrac, Midland, Western Australia, viewed 7 November 2016,
Clark, G, 1982, ‘Principles of rock drilling and bit wear, Part 1’, Colorado School of Mines Quarterly, Colorado School of Mines, Colorado.
Dagdelenler, G, Sezer, EA & Gokceoglu, C 2011, ‘Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters’, Expert Systems with Applications, vol. 38,
Dunn, P, Roberts, C & Ballardin, B 1993, ‘The use of specific energy as a drillability index’, in T Szwedzicki (ed.), Geotechnical Instrumentation and Monitoring in Open Pit and Underground Mining, CRC Press, Bota Raton, Florida, pp. 125–132.
Gui, M & Hamelin, J 2004, ‘Development of An instrumented borehole drilling system for ground investigation’, Electronic Journal of Geotechnical Engineering, vol. 9, pp. 350.
Gui, M, Soga, K, Bolton, M & Hamelin, J 2002, ‘Development of an instrumented borehole drilling system for ground investigation’, Journal of Geotechnical and Geoenvironmental Engineering, vol. 128, pp. 283–291.
Guzek, A, Shufrin, I, Pasternak, E & Dyskin, A 2015, ‘Influence of drilling mud rheology on the reduction of vertical vibrations in deep rotary drilling’, Journal of Petroleum Science and Engineering, vol. 135, pp. 375–383.
Gonzalez, J 2007, Application of Pattern Recognition Techniques to Monitoring-While-Drilling on a Rotary Electric Blasthole Drill at an Open-Pit Coal Mine, Queen’s University, Kingston, Ontario.
Hagan, T & Reid, I 1983, ‘Performance monitoring of production blasthole drills--a means of increasing blasting efficiency’, Proceedings of the 2nd International Surface Mining and Quarrying Symposium, Institution of Mining and Metallurgy, Bristol, pp. 245–254.
Hagan, T & Reid, I 1983, ‘Performance monitoring of production blasthole drills--a means of increasing blasting efficiency’, Transactions of the Institution of Mining and Metallurgy, vol. 92, pp. A171–A179.
Jain, A & Singh, D 1993, ‘Specific energy as a criterion for drillability of rocks – a laboratory study’, in T Szwedzicki (ed), Geotechnical Instrumentation and Monitoring in Open Pit and Underground Mining, CRC Press, Bota Raton, Florida, pp. 253-263.
Jang, JSR 1993, ‘ANFIS: Adaptive-Network-Based Fuzzy Inference System’, IEEE Transactions on Systems, Man and Cybernetics, vol. 23,
, pp. 665–685.
Jang, JSR, Chuen-Tsai, S & Mizutani, E, 1997, Neuro-Fuzzy and Soft Computing, Prentice-Hall, Upper Saddle River, New Jersey.
Kahraman, S, Bilgin, N & Feridunoglu, C 2003, ‘Dominant rock properties affecting the penetration rate of percussive drills’, International Journal of Rock Mechanics and Mining Sciences, vol. 40,
, pp. 711–723.
Kahraman, S, Rostami, J & Naeimipour, A 2016, ‘Review of ground characterization by using instrumented drills for underground mining and construction’, Rock Mechanics and Rock Engineering, vol. 49,
, pp. 585–602.
Kucuk, K, Aksoy, CO, Basarir, H, Onargan, T, Genis, M & Ozacar, V 2011, ‘Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling’, Tunnelling Underground Space Technology Including Trenchless Technology Research, vol. 26,
, pp. 38–45.
Li, Z, Itakura, K & Ma, Y 2014, ‘Survey of measurement-while-drilling technology for small-diameter drilling machines’, Electronic Journal of Geotechnical Engineering, vol. 19, pp. 10267–10282.
Lilly, P 1986, ‘An empirical method of assessing rock mass blastability’, in J Davidson (ed.) Proceedings of the Large Open Pit Mine Conference, The Australasian Institute of Mining and Metallurgy, Carlton South, pp. 88–92.
Liu, H & Karen, YK 2001, ‘Analysis and interpretation of monitored rotary blast hole drilling data’, International Journal of Surface Mining, Reclamation and Environment, vol. 15, pp. 177–203.
MATLAB 2011, Software for technical computing and model-based design, The MathWorks, Inc., Massachusetts,
Mellor, M 1972, ‘Normalization of specific energy values’, International Journal of Rock Mechanics and Mining Sciences, vol. 9, pp. 661–663.
Mozaffari, S 2007, Measurement While Drilling System in Aitik Mine, PhD thesis, Luleå University of Technology, Luleå.
Rabia, H 1982, ‘Specific energy as a criterion for drill performance prediction’, International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, vol. 19, pp. 39–42.
Rai, P, Schunnesson, H, Lindqvist, P-A & Kumar, U 2016, ‘Measurement-while-drilling technique and its scope in design and prediction of rock blasting’, International Journal of Mining Science and Technology, vol. 26,
, pp. 711–719.
Rajesh Kumar, B, Vardhan, H, Govindaraj, M & Saraswathi, PS 2013a, ‘Artificial neural network model for prediction of rock properties from sound level produced during drilling’, Geomechanics and Geoengineering, vol. 8,
, pp. 53–61.
Rajesh Kumar, B, Vardhan, H, Govindaraj, M & Vijay, GS 2013b, ‘Regression analysis and ANN models to predict rock properties from sound levels produced during drilling’, International Journal of Rock Mechanics and Mining Sciences, vol. 58,
, pp. 61–72.
Schunnesson, H 1998, ‘Rock characterisation using percussive drilling’, International Journal of Rock Mechanics and Mining Sciences, vol. 35,
, pp. 711–725.
Scoble, M & Peck, J 1987, ‘A technique for ground characterization using automated production drill monitoring’, International Journal of Surface Mining, Reclamation and Environment, vol. 1,
, pp. 41–54.
Scoble, MJ, Peck, J & Hendricks, C 1989, ‘Correlation between rotary drill performance parameters and borehole geophysical logging’, Mining Science and Technology, vol. 8,
, pp. 301–312.
Segui, J & Higgins, M 2002, ‘Blast design using measurement while drilling parameters’, Fragblast, vol. 6,
, pp. 287–299.
Singh, TN, Sinha, S & Singh, VK 2007, ‘Prediction of thermal conductivity of rock through physico-mechanical properties’, Building and Environment, vol. 42,
, pp. 146–155.
Smith, B 2010, ‘Improvements in blast fragmentation using measurement while drilling parameters’, Fragblast, vol. 6,
, pp. 301–310.
Sugeno, M & Kang, G 1988, ‘Structure identification of fuzzy model’, Fuzzy Sets and Systems, vol. 28,
, pp. 15–33.
Teale, R 1965, ‘The concept of specific energy in rock drilling’, International Journal of Rock Mechanics and Mining Sciences, vol. 2, pp. 57–73.
Turtola, H 2001, Utilization of measurement while drilling to optimise blasting in Large Open Pit mining, Luleå University of Technology, Luleå.
Vardhan, H, Adhikari, GR & Govinda Raj, M 2009, ‘Estimating rock properties using sound levels produced during drilling’, International Journal of Rock Mechanics and Mining Sciences, vol. 46, 604–612.
Vynne, J 1997, ‘The application and economic benefits of blasthole drill monitors’, Proceedings ISEE 23rd Annual Conference on Explosives and Blasting Technique, International Society of Explosives Engineers, Cleveland, pp. 635–646.
Yilmaz, I & Kaynar, O 2011, ‘Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils’, Expert Systems with Applications, vol. 38,
, pp. 5958–5966.
Yilmaz, I & Yuksek, G 2009, ‘Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models’, International Journal of Rock Mechanics and Mining Sciences, vol. 46,
, pp. 803–810.
Yin, K, Liu, H & Yang, H 2000, ‘Extracting information from drill data’, Fragblast, vol. 4,
, pp. 83–99.
Zhou, H, Hatherly, P, Ramos, F & Nettleton, E 2011, ‘An adaptive data driven model for characterizing rock properties from drilling data’, 2011 IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers, pp. 1909–1915.