Multivariate block simulations of a lateritic nickel deposit and post-processing of a representative subset
J Deraisme, O Bertoli, P Epinoux
Societe le Nickel (SLN) exploits the Dome lateritic nickel orebody at its
Tiébaghi operations in New Caledonia. The site geology has an obvious
bearing on recovery and metallurgical performance, and the controls are
extremely complex at all scales: lithology, oxidation-reduction
conditions, mineralogy, and multivariate geochemistry. In that context,
establishing an adapted recoverable resource estimation method that is
efficient and transparent proves an interesting challenge. The method
must address the key notions of:
* Support effect: the exploration data-set can only warrant the
estimation of large panels 20×20×3 m3, which are much larger
than the selective mining units (SMUs) of 5×5×3 m3
* Information effect: the selection at production stage uses
estimates based on information much denser than that
available for mine planning.
The problem is rendered more complex in this type of deposit by the
fact that the additive variables used for any estimation process (metal
accumulation and ore tonnage) are not the ones used to establish the
selection at mining stage (i.e. the nickel grade of SMUs, which is the
ratio of accumulation on tonnage). Eventually, the selection criteria
involve not only Ni grades but also other constituents such as Al2O3,
Fe2O3, MgO, and SiO2.
The solution presented in this paper is the construction of a platform
of SMU multivariate simulations in the saprolitic horizon, together with
efficient post-processing aimed at producing the multivariate
recoverable resource estimates. The efficiency constraints imposed by
the number of blocks to be simulated (1.5 million) lead to resorting to
direct block simulations. The underlying multivariate discrete Gaussian
model (DGM), the validity of which is tested beforehand, is put to good
use to mimic the selection process at the mining stage, by offering the
ability to simulate in each block a composite value located at random
within the block.
Finally, the paper presents the application of a scenario reduction
algorithm to pick a representative subset of a few simulations to help
appraise the risk attached to the downstream phases (reserve
optimization, mine sequencing) of the project. The implementation
presented here is for the first generation of the Scenario Reduction plugin
built in Isatis software, where measuring the distance between the
initial set of scenarios and the reduced set is based on Ni quantities only.
Research is under way to adapt the measure to the true multivariate
nature of the problem.
Keywords: direct block simulations, discrete Gaussian model, multivariate, scenario
reduction.