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ORE CONTROL WITH ECONOMIC GROWTH APPLYING GEOSTATISTICAL CONDITIONAL SIMULATION

Trabajo ganador del área Gestión y Economía Minera de CONAMIN 2023.
Por: Alfredo Marín Suárez, Universidad Nacional de Ingeniería.


Abstract

This paper achieves results somewhat similar to those historically achieved with Multivariate Conditional Simulation, using the Rotating Band method, to large scale mining in December 1978, in an iron ore deposit in the Pilbara region northwest of the Australian continent, and presented in my PhD thesis, developed under the direction of Dr. George Matheron.

To do this, the grades are transformed with Inverse Anamorphosis (nscore), variograms are constructed in all directions and then the Gaussian Sequential Simulation is applied. Finally, a transformation by Direct Anamorphosis is performed in order to issue a series of results reports for the mining operation, from 30 to 100 simulated points for each unit block (smu), such as:

Ore to concentrator, ore to leach pad, ore to ROM pad, ore to stockpile, and waste to dumps. Ore with controlled real variability for the metallurgical treatment plant. 

Plans with simulated grade and variability in each unit block (smu).

Plans of probability and average grade above a required cut off.

Confidence interval maps of the simulated grade for each unit block, "error" maps, i.e. the accuracy of the simulated grade for each unit block, which also allow to classify recoverable resources taking into account the mining selectivity in measured and indicated resources, with their degree of percentage reliability, as opposed to the traditional approach.

In short, these plans will allow a better definition of the polygons with their tonnage and average grade, as well as to perform an Ore Control with better blending in the short term mine planning. Initially shown in pilot tests at Cerro Verde, Las Bambas, Tantahuatay (2022-2023).

A version of the presented simulation is also being applied to geotechnical variables of the Antamina mine (2019-2023).

These probabilistic results obtained lead to the risk analysis of mining projects. C++, Fortran, Python, and free auxiliary software Sgems were used.

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