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.
5.3.a) Extrapolation of historical data. Simplest and fastest, projecting past trend/average forward; poor at capturing structural regime changes or turning points and gives a single deterministic number with no uncertainty band.
5.3.b) Long-term econometric modelling. Models supply/demand fundamentals explicitly (mine supply curves, substitution, end-use demand growth, inventory cycles); more defensible and better at capturing structural shifts, but data- and assumption-intensive, and only as good as the fundamentals data and behavioural assumptions feeding it.
5.3.c) Inferred price / cost-price relationship. Infers price from the position of the marginal (highest-cost) producer needed to meet demand on the industry cost curve, on the theory that price gravitates toward what keeps the marginal producer roughly break-even; useful as a floor/reasonableness check but assumes cost-curve data is current and that the marginal producer genuinely sets price (not always true in cartelized or state-influenced markets).
5.3.d) Breakeven price estimation. Project-specific rather than market-wide – solves for the price at which THIS project's own NPV = 0; deliberately conservative (a go/no-go floor test) rather than a genuine forecast of where price will actually be, so it under-states expected revenue on average.
5.3.e) Monte Carlo simulation. Samples a full probability distribution of future prices (from historical volatility, option-implied volatility, or expert elicitation) rather than a single point estimate, propagating that uncertainty through to a distribution of NPV/IRR outcomes; the most rigorous approach for risk-adjusted decision-making but requires defensible input distributions and is the most computationally/analytically demanding of the five.
Comparison. (a) and (c) are quick, low-cost reasonableness checks; (b) is the most fundamentals-grounded single-point forecast; (d) reframes the question from "what will price be" to "what price must be exceeded"; (e) alone captures the FULL distribution of outcomes rather than a single number, which is why it is increasingly the preferred method for major-project sanction decisions where management needs to see downside risk, not just a base case.
