Issue 5, p. 105 (2015)

  Article

Validation of reverse circulation drilling rig for reconciliation purposes

  • A.C. Chieregati  
  • T.M. El Hajj
  • C.F. Imoto
  • L.E.C. Pignatari
Dept. of Mining and Petroleum Engineering, University of São Paulo, Av. Prof. Mello Moraes 2373, 05508-030, Sao Paulo, Brazil
[email protected]
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MultiGeo, R. Funchal 19, 8th floor, 04451-060, São Paulo, Brazil
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Yamana Gold, R. Funchal 411, 04451-060, São Paulo, Brazil
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 Corresponding Author
Dept. of Mining and Petroleum Engineering, University of São Paulo, Av. Prof. Mello Moraes 2373, 05508-030, Sao Paulo, Brazil
[email protected]
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Representative samples of ore containing precious metals is a difficult task. The lower the grade and the higher the nugget and/or cluster effect, the more complex and difficult extracting samples that are both accurate and precise. Reconciliation practices can be used as an effective tool to evaluate sampling accuracy throughout grade control processes. However, a proper reconciliation system must be based on reliable data and, therefore, optimisation of sampling techniques is a must for development of a reliable reconciliation system. This paper is a result of an extensive reconciliation study carried out at a copper and gold mine in Brazil, where a significant reconciliation problem took place while using manual sampling for grade control and short-term modeling. After analysing several sampling equipment/sample selection techniques, the authors suggested to use of a reverse circulation drilling rig with an automatic sampling system for grade control sampling. The samples generated by this automatic system were compared with the manual samples collected from the piles generated by the previous percussion rotary air blast drilling rig. Also, three pairs of twin holes were drilled in order to validate the new reverse circulation approach. Results allowed estimation of the bias related to the Increment Weighting Error (IWE) generated by manual sampling. Results show that the reverse circulation rig eliminates significant sampling biases, thus improving the general sample representativeness by increasing both sample accuracy and precision.

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