24-MMP-A5 Surface Mining Methods and Design · May 2016
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
Paper format. National Exams, May 2016 — 09-MMP-A5, Surface Mining Methods and Design. Three hours, closed book; one hand-written, double-sided 8.5×11″ reference sheet and an approved Sharp or Casio calculator are permitted. Question 1 is compulsory (six parts, 40 marks); candidates then choose three of the five optional questions (2–6, 20 marks each) for a 100-mark paper — only the first three optional answers appearing in the answer book are graded. All six parts of Question 1 and all five optional questions are answered here, because this set is a study resource rather than an exam script.
Reference texts. The answers below are keyed to the works normally recommended for this syllabus code:
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.
What a block model is. A block model discretizes an ore deposit and its surrounding waste into a regular three-dimensional array of rectangular prisms (“blocks”), typically 10–30 m on a side for a large open pit, each carrying an attribute set — grade of every payable and deleterious element, rock type/lithology code, density, geotechnical domain, and (once estimated) an economic value. The model is built by interpolating or simulating grade from drill-hole assay data onto the block centroids — ordinary kriging, inverse-distance weighting, or conditional simulation — constrained by wireframed geological domains. Every downstream mine-planning calculation (reserve estimation, pit optimization, scheduling, grade control) reads from this single discretized representation rather than from the raw drill-hole database, which is why the addressing scheme discussed in Question 1.2 matters: millions of blocks must be stored, sorted and retrieved efficiently.
Long-range vs. short-range models. The distinction is one of purpose, block size and update frequency, not of software. A long-range (strategic/life-of-mine) model is built once per major study or reserve update, uses coarser blocks (often the full bench height and a 20–30 m plan cell) matched to the drill-hole spacing that actually supports estimation confidence, and drives pit-limit optimization, phase design and the 10–20 year production schedule. A short-range (grade-control) model is rebuilt continuously as new blast-hole and infill drilling arrives, uses much finer blocks (2–5 m, sometimes sub-benched), and exists to answer one operational question every shift: which specific blocks in the exposed bench go to the mill and which go to the waste dump or low-grade stockpile.
In a typical open-pit gold operation the contrast is stark because gold grade is highly variable at short spacing (nugget effect): the long-range model, built from a few hundred metres of diamond-drill spacing, may show a smooth 1.2 g/t domain, while the short-range model, rebuilt weekly from 5 m blast-hole assays, resolves the high-grade shoots and barren pods within that domain that decide the real-time ore/waste cut-off. In a typical oil sands operation the situation is almost reversed: bitumen grade (weight-% bitumen) is comparatively continuous over tens of metres within a channel-sand facies, so the long-range model built from a widely spaced (100–200 m) vertical corehole grid is already a reasonably reliable predictor of short-range performance, and the “short-range” model's real job is tracking the geometry of interburden/mudstone rip-up clasts and the top-of-ore/bottom-of-ore contact surfaces (which drive dilution) rather than resolving a nugget-effect grade population.
Surface topography in the block model. At the feasibility stage, ground surface is captured independently of the grade model — typically as a triangulated irregular network (TIN) or a regular-grid digital terrain model (DTM) built from a topographic survey, LiDAR or photogrammetric flight, or in earlier studies from contoured mine plans. That surface is then draped onto the block model by flagging, for every vertical column of blocks, the topmost block whose floor elevation lies below the interpolated ground elevation as “exposed” (air blocks above it are excluded from tonnage and value calculations, and partially-exposed top blocks are volume-corrected by the fraction of the block actually below surface). This surface constraint is what turns an unconstrained grade block model into a mineable resource: pit-optimization algorithms (Question 1.3) can only ever remove material that starts at or below the true ground surface.
