24-MMP-A4 Mine Valuation and Mineral Resource Estimation · May 2016
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
EGBC National Exam — Mining and Mineral Processing Engineering, 09-Mmp-A4 Mine Valuation and Mineral Resource Estimation, 2016-May. 3 hours duration; one handwritten 8.5×11 in reference sheet permitted (not an open-book exam); only approved Sharp or Casio calculators allowed. Question 1 is compulsory (40 marks, parts 1.1–1.6); candidates then select THREE of the six optional Questions 2–7 (20 marks each) to complete the paper.
Reference texts: Isaaks & Srivastava, An Introduction to Applied Geostatistics (variogram modelling, kriging estimators, volume–variance relations); Hustrulid, Kuchta & Martin, Open Pit Mine Planning and Design (mine valuation, NPV and cut-off grade methodology, mineable reserves); Gentry & O'Neil, Mine Investment Analysis (Canadian mining taxation, inflation and financing effects on DCF yield, smelter/refining contract terms, net smelter return); SME Mining Engineering Handbook, 3rd ed. (mineral exploration/evaluation stages, ore reserve classification, ore deposit models); CIM Best Practice Guidelines and NI 43-101 (Canadian Securities Administrators).
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
Most ore-grade populations are positively skewed (lognormal-like), which produces a well-documented proportional effect: local variance scales with the local mean, so high-grade zones are genuinely MORE erratic (noisier, shorter effective range) than a single global variogram – fitted mostly to the much more numerous low/moderate-grade data – would suggest, while low-grade zones are correspondingly LESS erratic than the global model implies. Applying one stationary variogram everywhere therefore under-estimates local variance/kriging uncertainty in high-grade zones (leading to over-smoothed, over-confident high-grade block estimates and, downstream, poor high-grade selectivity/dilution control), and over-estimates it in low-grade zones (leading to unnecessarily wide search neighbourhoods and over-smoothing of genuinely lower, less-continuous grade). The practical fix is to work with relative (normalized) variograms computed within grade domains, or to use indicator variograms fitted separately at multiple cutoffs (Question 6.2 below) so that continuity is allowed to change with grade level rather than being forced into a single global structure.
