Exploitation
Mojtaba Dehghani Javazm; Mohammadreza Shayestehfar
Abstract
In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with ...
Read More
In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with a block model constructed for the analysis. Four methods were employed: UC, LUC, DCSBG, and SGS. The correlation coefficients for UC, DCSBG, and SGS methods in the supergene zone, as well as the results from extraction drill holes (extraction blocks) at a cut-off grade of 0.15%, were 0.637, 0.527, and 0.556, and the correlation coefficient for calculating tonnage and the metal content using UC was 0.364 and 0.629, respectively. For the hypogene zone, the correlation coefficients for metal content at a cut-off grade of 0.15% were 0.778, 0.788, and 0.790 for UC, DCSBG, and SGS, and at a cut-off grade of 0.65%, they were 0.328, 0.431, and 0.458, respectively. By employing The LUC method in the supergene zone with a change in SMU and comparing the results obtained from the E-Type map, the performance of this method is higher across all cut-off grades. As the cut-off grade increases in the hypogene zone, the performance of the LUC method relative to simulation methods decreases. The LUC method can be used to observe the impact of the convergence of results obtained from this method with real data from low-grade to high-grade sections, highlighting the necessity of differentiating this zone into low and high-grade segments during the estimation process.
M. Taghvaeenejad; M.R. Shayestefar; P. Moarefvand
Abstract
At different stages of mining, we always face a degree of uncertainty. Some of these uncertainties, such as the amount of reserve and grade of the deposit, are due to the inherent changes in the deposit and directly affect the technical and economic indicators of the deposit. On the other hand, the heavy ...
Read More
At different stages of mining, we always face a degree of uncertainty. Some of these uncertainties, such as the amount of reserve and grade of the deposit, are due to the inherent changes in the deposit and directly affect the technical and economic indicators of the deposit. On the other hand, the heavy costs of the exploration sector often limit the amount of exploratory information, which necessitates the use of accurate estimation methods. In this work,we examines the modeling and estimation results using the conventional and simple kriging methods and the effects of the diverse indicators used in the classification of mineral storages or the parameters defining these indices. 127 exploratory boreholes with an average depth of 95 m are used to build the block model of the deposit in the Data Mine software. After the statistical studies, the 3D variographic studies are performed in order to identify the anisotropy of the region. A grade block model is constructed using the optimal variogram parameters.Then, using various methods to estimate the block model uncertainty including the kriging estimation variance, block error estimation, kriging efficiency and slope of regression, the mineral reserves are classified according to the JORC standard code. Based on different cut-off grades, the tonnage and average grade are calculated and plotted. In this work, an innovative quantitative method based on the grade-number and grade-volume fractal model is used to indicate the classification of mineral reserves. The use of fractal patterns due to the amplitude of the variation is greater and more important than the standard and provides us with a better understanding of the deposit changes per block. The existence of a minimal difference between the use of the standard and fractal patterns in the slope of the regression method indicates less error and is a proof of more reliable results.
