Measuring the Impact of the Change of Support and Information Effect at Olympic Dam

  • Colin Badenhorst
  • Mario Rossi
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 17)


The change of support and information effect concepts are fundamental in every resource model. It underpins all aspects of resource estimation in every deposit worldwide, yet is poorly understood, rarely taught, and even more rarely applied. This paper describes the practical implications of these concepts using conditional simulation, by deriving a recoverable resource estimate for the first 11 benches of the proposed Olympic Dam open cut mine. The Olympic Dam deposit is one the world’s largest polymetallic resources at 9 billion tonnes grading 0.8 % Cu, 270 ppm U3O8, 0.32 g/t Au and 1.5 g/t Ag. BHP Billion is currently undertaking a feasibility study of a large open cut operation with an estimated mine life in excess of 100 years. The resource estimation practices at Olympic Dam comprise of a combination of linear and non-linear techniques to estimate 16 different grade variables critical to the resource. In the southern portion of the deposit, at the site of the proposed open cut, the current resource estimate data spacing is insufficient to predict the recoverable tonnage and grade that will be selected using closely spaced grade control blast holes once mining commences. Conditional simulation has been used to generate a recoverable resource estimate by quantifying the tonnage and grade uplift resulting from the change of support and the information effect that occurs at the time of mining. Ten realizations of Cu, S, U3O8 and Au were generated using Sequential Indicator Simulation. The simulations were validated visually and statistically, and a single realization was then chosen to represent reality. Several grade control databases were constructed by sampling the realization at the expected blast hole spacing. Each database was used to estimate the first few pushbacks of the proposed open cut mimicking future grade control estimates. Variations were quantified and grade tonnage curves at the smaller grade control support were compared to the larger blocks of the resource. This information has been used to optimize the predictions of expected tons and grades fed to the mill, adjusting the recoverable resource estimate to control its smoothing. This information is critical for optimal mine planning. The results and conclusions of this work unequivocally demonstrate why every resource geologist should have a deep understanding of the change of support and information effect, and how it can be applied in their resource models using conditional simulation.

This publication includes information on Mineral Resources which have been compiled by S. O’Connell (MAusIMM). This is based on Mineral Resource information in the BHP Billiton 2010 Annual Report which for can be found at All information is reported under the ‘Australasian Code for Reporting of Mineral Resources and Ore Reserves, 2004’ (the JORC Code) by Shane O’Connell who is a full-time employee of BHP Billiton and has the required qualifications and experience to qualify as Competent Person for Mineral Resources under the JORC Code. Mr. O’Connell verifies that this report is based on and fairly reflects the Mineral Resources information in the supporting documentation and agrees with the form and context of the information presented.


Ordinary Kriging Information Effect Resource Model Conditional Simulation Blast Hole 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work presented here is a culmination of five years worth of team effort. In particular, the authors would like to acknowledge the contributions of Anthony Bottrill and Shane O’Connell. We would also like to acknowledge BHP-Billiton Uranium for their permission to publish this work.


  1. 1.
    Alabert F (1987) Stochastic imaging of spatial distributions using hard and soft information. MSc thesis. Stanford University, Stanford, CA, 197 pp Google Scholar
  2. 2.
    Isaaks EH (1990) The application of Monte Carlo methods to the analysis of spatially correlated data. PhD thesis, Stanford University, Stanford, CA, 213 pp Google Scholar
  3. 3.
    Journel AG (1988) Fundamentals of geostatistics in five lessons. Stanford Center for Reservoir Forecasting, Stanford Google Scholar
  4. 4.
    Rossi ME, Badenhorst C (2010) Collocated co-simulation with multivariate Bayesian updating: a case study on the olympic dam deposit. In: Castor R et al. (eds) Proc. of the 4th international conference on mining innovation (MININ 2010), Santiago, Chile Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Olympic DamBHP BillitonAdelaideAustralia
  2. 2.GeoSystems International, Inc.Boca RatonUSA

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