Advertisement

Strategies for Mine Planning and Design

  • P. A. DowdEmail author
  • C. Xu
  • S. Coward
Chapter

Abstract

This paper provides an assessment of the current challenges in strategic mine planning and design and suggested approaches for addressing them. The specific challenges covered are:
  1. (1)

    Realistic quantification of downstream processes applied to orebody models to provide an integrated approach to mine design and optimisation.

     
  2. (2)

    Modelling, estimation and simulation of geometallurgical variables and their integration into resource and reserve estimation and mine planning.

     
  3. (3)

    Modelling, estimation and simulation of new variables for new forms of mining—deep mining, particularly block caving, and solution mining.

     
  4. (4)

    Flexibility in planning and design to manage risk and minimise its impact.

     
  5. (5)

    IT infrastructure and platforms for rapid on-line data collection, storage, access and processing.

     
Most of these challenges require new types of data, variables, modelling and estimation methods. Foremost among the new types of variables and data are geometallurgical and dynamic rock mass characterisation variables. New types of data and data collection include rapid generation of very large amounts of on-line sensor data and the consequent need for rapid processing and modelling of these data. This paper outlines the challenges and strategies in each of these areas and uses examples of models and outputs to illustrate approaches and potential solutions.

References

  1. Abdel Sabour SA, Dimitrakopoulos R, Kumral M (2008) Mine design selection under uncertainty, transactions of the institution of mining and metallurgy, Section A. Mining Tech 117(2):53–64Google Scholar
  2. Armstrong A, Galli A, Couët B (2004) Incorporating technical uncertainty in real option valuation of oil projects. J Pet Sci Eng 44(1–2):67–82Google Scholar
  3. Armstrong M, Ndiaye A, Razanatsimba R, Galli A (2013) Scenario reduction applied to geostatistical simulations. Math Geosci 45(2):165–182Google Scholar
  4. Bercovier M, Luzon M, Pavlov E (2002) Detecting planar patches in an unorganized set of points in space. Adv Comput Math 17:153–166CrossRefGoogle Scholar
  5. Bodin J, Porel G, Delay F, Ubertosi F, Bernard S, de Dreuzy J-R (2007) Simulation and analysis of solute transport in 2D fracture/pipe networks: the SOLFRAC program. J Contam Hydrol 89:1–28CrossRefGoogle Scholar
  6. Botin JA, Dell Castillo MF, Guzmán RR, Smith L (2012) Real options: a tool for managing technical risk in a mine plan. In: SME Annual Meeting. Seattle, USA, SME, Pre-print 12–121, 7Google Scholar
  7. Brazil M, Lee DH, Van Leuven M, Rubinstein JH, Thomas DA, Wormald NC (2003) Optimising declines in underground mines, transactions of the institution of mining and metallurgy, section A. Mining Tech 112:164–170Google Scholar
  8. Brazil M, Lee D, Rubinstein JH, Thomas DA, Weng JF, Wormald NC (2004) Optimisation in the design of underground mine access. In: Orebody modelling and strategic mine planning, spectrum series, vol 14. Australasian Institute of Mining and Metallurgy, Melbourne, Australia, pp 121–124Google Scholar
  9. Brazil M, Grossman PA, Lee D, Rubinstein JH, Thomas DA, Wormald NC (2009) Access optimisation tools in underground mine design. In: International symposium on orebody modelling and strategic mine planning: old and new dimensions in a changing world. Melbourne, Australasian Institute of Mining and Metallurgy, pp. 237–241Google Scholar
  10. Brown ET (2012) Progress and challenges in some areas of deep mining. Deep Mining 2012. Australian Centre for Geomechanics, Perth, AustraliaGoogle Scholar
  11. Coward S, Dowd PA, Vann J (2013) Value chain modelling to evaluate geometallurgical recovery factors. In: Proceedings of the 36th APCOM Conference. Brazil, pub. Fundação Luiz Englert, pp 288–289Google Scholar
  12. Coward S, Dowd PA (2014) Geometallurgical models and the quantification of uncertainty in mining project value chains. Presentation at Geometallurgy 2014, London, Institute of Materials, Minerals and Mining; 9–10 June 2014Google Scholar
  13. Coward S, Dowd PA (2015) Geometallurgical models for the quantification of uncertainty in mining project value chains. In: Proceedings of the 37th APCOM Conference. Publication of Society for Mining, Metallurgy and Exploration (SME), pp 360–369Google Scholar
  14. DETCRC (2015) Available at: http://detcrc.com.au
  15. Dimitrakopoulos RG, Abdel Sabour SA (2007) Evaluating mine plans under uncertainty: can real options make a difference? Resour Policy 32:116–125Google Scholar
  16. Dimitrakopoulos R, Farrelly CT, Godoy M (2002) Moving forward from traditional optimization: grade uncertainty and risk effects in open-pit design, transactions of the institution of mining and metallurgy, Section A. Mining Tech 111(1):82–88Google Scholar
  17. Dowd PA (1995) Björkdal gold-mining project, northern Sweden. Trans Instn Min Metall, Sect A: Min Ind 104:A149–A163Google Scholar
  18. Dowd PA, Dare-Bryan PC (2017) Planning, designing and optimising using geostatistical simulation. In this volumeGoogle Scholar
  19. Fadakar AY, Dowd PA, Xu C (2013) The RANSAC method for generating fracture networks from micro-seismic event data. Math Geosci 45:207–224CrossRefGoogle Scholar
  20. Groeneveld B, Topal E (2011) Flexible open-pit mine design under uncertainty. J Mining Sci 47(2):212–226Google Scholar
  21. Kear J, White J, Bunger AP, Jeffrey R, Hessami MA (2013) Three dimensional forms of closely-spaced hydraulic fractures. In: Proceedings of International Conference for Effective and Sustainable Hydraulic Fracturing 2013, Brisbane, May 2013Google Scholar
  22. Mardia KV, Nyirongo VB, Walder AN, Xu C, Dowd PA, Fowell RJ, Kent JT (2007) Markov Chain Monte Carlo implementation of rock fracture modelling. Math Geol 39:355–381CrossRefGoogle Scholar
  23. McCarthy P (2002) Flexible studies and economic models for deep mines. In: Proceeding of First Internat. Seminar on deep and high stress mining. Australian Centre for Geomechanics, PerthGoogle Scholar
  24. Moss A (2012) Keynote address at MassMin 2012. Canada (unpublished), SudburyGoogle Scholar
  25. Seifollahi S, Dowd PA, Xu C, Fadakar-Alghalandis Y (2013) A spatial clustering approach for stochastic fracture network modelling. Rock Mech Rock Eng 47(4):1225–1235Google Scholar
  26. SGS (2014) http://www.sgs.com. Accessed 12 June 2014
  27. Sriram V, Kearney D, Andrews S (2014) Collaborative remote operations centre report. Department of State Development, Government of South Australia, Available at: http://www.statedevelopment.sa.gov.au/upload/manufacturing/1446_collaborative_remote_centre_operations_report.pdf
  28. Xu C, Dowd PA (2010) A new computer code for discrete fracture network modelling. Comput Geosci 36:292–301CrossRefGoogle Scholar
  29. Xu C, Dowd PA, Fidelibus C (2014) Realistic pipe models for flow modelling in discrete fracture Networks. In: Proceedings of the first international discrete fracture network engineering conference. Vancouver, CanadaGoogle Scholar
  30. Xu C, Dowd PA, Wyborn D (2013) Optimisation of a stochastic rock fracture model using Markov Chain Monte Carlo simulation. Min Technol 122(3):153–158CrossRefGoogle Scholar
  31. Xu C, Dowd PA (2014) Stochastic fracture propagation modelling for enhanced geothermal systems. Math Geosci 46(6):665–690Google Scholar
  32. Xu C, Dowd PA, Tian ZF (2015) A simplified coupled hydrothermal model for enhanced geothermal systems. Appl Energy 140:135–145Google Scholar

Copyright information

© The Australasian Institute of Mining and Metallurgy 2018

Authors and Affiliations

  1. 1.The University of AdelaideAdelaideAustralia
  2. 2.Principal Consultant, InterlacedFremantleAustralia

Personalised recommendations