The use of soft computing methods for the prediction of rock properties based on measurement while drilling data

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.

[1]  B. Smith.  Improvements in blast fragmentation using measurement while drilling parameters , 2002 .

[2]  Håkan Schunnesson,et al.  Rock characterisation using percussive drilling , 1998 .

[3]  Oguz Kaynar,et al.  Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils , 2010, Expert Syst. Appl..

[4]  Ali Firat Cabalar,et al.  Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering , 2012 .

[5]  I. Yilmaz,et al.  Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models , 2009 .

[6]  Fabio Tozeto Ramos,et al.  An adaptive data driven model for characterizing rock properties from Drilling data , 2011, 2011 IEEE International Conference on Robotics and Automation.

[7]  G. R. Adhikari,et al.  Estimating rock properties using sound levels produced during drilling , 2009 .

[8]  Helena Turtola.  Utilisation of measurement while drilling to optimise blasting in large open pit mining , 2001 .

[9]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[10]  Jamal Rostami,et al.  Review of Ground Characterization by Using Instrumented Drills for Underground Mining and Construction , 2016, Rock Mechanics and Rock Engineering.

[11]  Hong-Chuan Yang,et al.  Extracting information from drill data , 2000 .

[12]  Celal Karpuz,et al.  Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions , 2014 .

[13]  Malcolm Scoble,et al.  Correlation beween rotary drill performance parameters and borehole geophysical logging , 1989 .

[14]  Ebru Akcapinar Sezer,et al.  Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters , 2011, Expert Syst. Appl..

[15]  G S Vijay,et al.  Regression analysis and ANN models to predict rock properties from sound levels produced during drilling , 2013 .

[16]  H. Rabia,et al.  Specific energy as a criterion for drill performance prediction , 1982 .

[17]  Shahram Mozaffari,et al.  Measurement While Drilling System in Aitik Mine , 2007 .

[18]  T. Onargan,et al.  Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling , 2011 .

[19]  S. Kahramana,et al.  Dominant rock properties affecting the penetration rate of percussive drills , 2003 .

[20]  T. N. Singh,et al.  Prediction of thermal conductivity of rock through physico-mechanical properties , 2007 .

[21]  Uday Kumar,et al.  Measurement-while-drilling technique and its scope in design and prediction of rock blasting , 2016 .

[22]  Harsha Vardhan,et al.  Artificial neural network model for prediction of rock properties from sound level produced during drilling , 2013 .

[23]  M. Mellor.  Normalization of specific energy values , 1972 .

[24]  P A Lilly.  An empirical method of assessing rock mass blastability , 1986 .

[25]  Natalie Beattie,et al.  MONITORING-WHILE-DRILLING FOR OPEN-PIT MINING IN A HARD ROCK ENVIRONMENT: An Investigation of Pattern Recognition Techniques Applied to Rock Identification , 2009 .

[26]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[27]  Arcady Dyskin,et al.  Influence of drilling mud rheology on the reduction of vertical vibrations in deep rotary drilling , 2015 .

[28]  R. Teale.  The concept of specific energy in rock drilling , 1965 .

[29]  H. Basarir,et al.  Preliminary estimation of rock mass strength using diamond bit drilling operational parameters , 2016 .

[30]  J. L. B. Segui,et al.  Blast design using measurement while drilling parameters , 2002 .

[31]  Martin Gonzalez,et al.  Application of Pattern Recognition Techniques to Monitoring-While-Drilling on a Rotary Electric Blasthole Drill at an Open-Pit Coal Mine , 2007 .

[32]  Malcolm Scoble,et al.  A technique for ground characterization using automated production drill monitoring , 1987 .

[33]  B. Adebayo,et al.  Discontinuities effect on drilling condition and performance of selected rocks in Nigeria , 2014 .