Mine Planning Strategies for Resource Valuation

“𝑯𝒐𝒘 𝒎𝒖𝒄𝒉 𝒊𝒔 𝒓𝒆𝒂𝒍𝒍𝒚 𝒊𝒏 𝒕𝒉𝒆 𝒈𝒓𝒐𝒖𝒏𝒅?” This is the single most important question in mining. To answer this, mining engineers and geologists use different resource estimation methods. Each method has its own accuracy, data requirements, and ideal use case. 1. Polygonal / Triangular Methods (Classical) Draw polygons (or triangles) around sample points (e.g., drill holes). Assign the grade of the sample to the whole polygon or use the average of three samples for a triangle. Used in : Early exploration, very sparse data, quick first-look estimates. 2. Inverse Distance Weighting (IDW) Estimate the grade at an unsampled point by averaging nearby samples. Closer samples have more weight (weight decreases with distance, often by distance²). Used in : Moderate drill density, mid-stage projects needing a straightforward interpolator. 3. Ordinary Kriging (OK) Use a semivariogram to model how grades correlate with distance and direction. Calculate optimized weights from that model to produce an unbiased estimate and error measure. Used in: Advanced exploration, feasibility studies, and formal resource reporting (JORC/NI 43-101). 4. Indicator Kriging (IK) Convert grades into indicators (e.g., above/below a cutoff). Krige those indicators to estimate probabilities that blocks exceed cutoffs; combine probabilities to infer grade classes. Used in : Highly variable deposits, modelling cutoffs for ore/waste, probabilistic resource classification. 5. Sequential Gaussian Simulation (SGS) / Multiple Simulations Generate multiple equally-probable realizations of the grade distribution that honour data and spatial continuity. Use the ensemble of realizations to assess uncertainty and preserve local variability. Used in : Uncertainty / risk analysis, complex or highly heterogeneous ore bodies, mine planning with scenario testing. 6. Machine Learning (ML)–Based Estimation Use supervised learning algorithms (e.g., random forests, gradient boosting, neural networks) to predict grades or classes from many inputs: drill data, geology logs, geophysics, remote sensing, structural interpretations, and derived features. ML models learn non-linear relationships and can incorporate large multi-source datasets. Often used together with spatial methods (e.g., ML predictions as inputs to kriging or as features in simulations). Used in : Complex datasets with many predictors, integrating geophysics/chemistry/structural data, rapid scenario testing, and when non-linear patterns are suspected. Increasingly used for feature engineering, anomaly detection, and to augment traditional geostatistics. #mining #geology #resources #resourceestimation #geostatistics #Kriging #IDW

🛑 The Biggest Silent Killer of Mining Projects: Overconfidence in the Orebody 🛑 Every mine plan looks good... on paper. Production targets are met. Budgets approved. Equipment ordered. Everyone feels good until the mine starts underperforming. Month after month. Quarter after quarter. And the excuses pile up:   “Unexpected dilution” “Poor ground conditions” “Operational delays”   But here’s the truth nobody wants to say out loud:   The real failure happened years earlier, when we trusted the orebody model more than we should have. Mining is the only industry I know that builds billion-dollar businesses on statistical guesses... and then gets surprised when reality doesn't cooperate. Geological uncertainty is not a rounding error. It’s not a minor risk. It's shown to be the major contributor to project failures. It’s the foundation your entire operation stands on, or collapses on. And yet, companies build LOM plans assuming the estimated block model is the ground truth. Why? Because it's easier to assume certainty than to quantify uncertainty and plan for it. Because spreadsheets are cleaner when you don’t have multiple scenarios. Because no one wants to explain to the board that the “high-confidence” resource might still let them down. But pretending the orebody is perfect doesn't protect you. It just delays the realization.   🔍 Here’s what actually happens: Resource models, even “measured” ones, have built-in errors, including grade, volume, and continuity errors. Estimation methods like Kriging smooth out the grades, where high-grades (where we make money!) are underestimated, and low-grades are overestimated. Mine plans are optimized assuming every block behaves exactly as estimated. Operations find out the hard way that Mother Nature didn’t read the single 3D model.   🔴 And the cost? Missed production targets. Inability to control contaminants at the plant. Cash flow shortfalls. Poor reconciliation. Erosion of investor trust. Bad CAPEX decisions. Inability to fulfill contracts.   All because we decided to ignore the geological uncertainty!   ✅ What actually works? Quantify uncertainty, early and often. Simulate multiple orebody realizations that reproduce the local variability under the ground instead of relying on a single “best guess.” Optimize the strategic mine plan looking at all simulations. This will ensure you have integrated risk-management, prioritizing less risky, yet rich, areas early on till more information is available for later project stages. Report the production schedules probabilistically.   Mining doesn’t fail because it’s inefficient. It fails because it assumes the earth will behave the way a model says it should. And when that assumption breaks, everything else does too. Maybe it’s time we stop treating geological uncertainty as a technical inconvenience. It’s the core business risk, and facing it in advance is the only way we’ll stop falling short. #Uncertainty #Risk #ResourceModel #MinePlanning #Stochastic

WHEN DO YOU STOP THE PIT AND GO UNDERGROUND? (Part 2) In the previous post, we explored one of the toughest questions in mine planning: “When do we stop the pit and start going underground?” That post lit up with ideas, stories, and great debate — and one question kept coming up: 👉🏽 What really dictates the switch? Let’s go deeper — into the logic, the curves, and the math that reveal when value changes hands. Before we look into the governing equations, let's remind ourselves of the mine economics! ⚒️ Open Pit Economics — The Law of Diminishing Depth At shallow depths, the pit is king. Big equipment, bulk movement, quick tonnes. But as you go deeper, the walls open up, waste explodes, and the strip ratio skyrockets. The value of an open pit mine at any depth d is governed by this equation; V_{pit}(d) = R(d) - [C_{mining}(d) + C_{waste}(d) + C_{process}] At some point, every extra tonne of ore comes with mountains of waste — and your profits start slipping away. 🏗️ Underground Economics — The Slow Starter That Wins Deep Underground begins where open-pit struggles. Yes, it’s expensive at first — declines, ventilation, power, access — but once inside, you mine only the valuable material. V_{UG}(d) = R(d) - [C_{dev}(d) + C_{stoping}(d) + C_{UGGA} + C_{backfill}] At first, UG is costly. Then steady. While the pit curve falls, the underground curve rises — until they finally meet. 💡 The Crossover — Where Value Changes Hands This is the magic point planners search for: V_{pit}(d_t) = V_{UG}(d_t) At that depth dₜ, both methods deliver the same NPV. Above it, open pit wins. Below it, underground takes over. It’s the “handshake” between two worlds — where the pit bows out, and underground takes the crown. 🧭 The Planner’s Secret Never stack up totals—they hide ugly benches. Judge the next bench only: if pushing the pit one level deeper won’t pay for its extra waste, haul, dilution, and delay, while the first underground stopes do pay, you’ve hit the transition. That’s the moment smart planners stop digging and start tunnelling. ⚙️ How World-Class Planners Make the Call 1️⃣ Run Whittle or Pseudoflow with a UG shadow (exclude access & crown pillar). 2️⃣ Compare incremental shells — not just “ultimate pit.” 3️⃣ Integrate pit + UG schedules to protect mill feed. 4️⃣ Test sensitivities (±20%) on slopes, prices, and delays. 5️⃣ Plot both curves — where they cross, the numbers speak for themselves. 💬 In Short The pit says, “I can still go deeper.” The underground replies: “But I can do it smarter.” Your job as a planner is to determine where value outweighs volume. 🧮 I’ve built a Transition Calculator (Excel) that visualises this crossover — just enter your pit & UG costs, NSR, and slope, and it shows where the switch happens. Join our toolkit and download the template here! 👇🏽https://lnkd.in/dv8Ney28 Or share this post to your network to get the template for free. Let me hear your thoughts! #MinePlanningClarityToolkit

Strategic Mine Planning (SMP) The long-term blueprint that defines how a mineral resource will be extracted sustainably, profitably, and safely over the entire mine life. Strategic Mine Planning ensures that every stage—from exploration to rehabilitation—is aligned with business goals, market demand, technology, ESG expectations, and regulatory requirements. 1. What is Strategic Mine Planning? Strategic Mine Planning is a long-term decision-making process that determines: When, where, and how to extract ore How much to mine each year (production scheduling) Which technology and equipment will be used How resources/reserves will be converted into financial value How to optimise NPV, IRR, mine life, stripping ratio, costs, and ESG impact It typically covers 10–30 years, depending on mine size 2. Key Objectives of Strategic Mine Planning 1. Maximise the economic value of the mineral deposit (NPV-driven). 2. Ensure long-term stability & sustainability of production. 3. Reduce risk related to geology, market, environment, and operations. 4. Optimise resource utilisation and convert maximum reserves to profit. 5. Improve safety and ESG performance for social license to operate. 6. Create a foundation for tactical and operational mine plans. 3. Levels of Mine Planning Strategic Mine Planning consists of three hierarchical levels: A. Long-term Planning (Strategic) – 10 to 30 years Resource evaluation & block modelling (e.g., Surpac, Datamine) Pit optimisation (e.g., Lerchs–Grossmann algorithm) Mine life strategy & production targets Technology selection & infrastructure planning CAPEX–OPEX modelling Market forecasting Sustainability framework B. Medium-term Planning (Tactical) – 3 to 5 years Annual production schedules Equipment fleet optimisation Drilling, blasting, haulage plans Budgeting & manpower planning C. Short-term Planning (Operational) – Daily to Monthly Shift-wise drilling, blasting, loading, hauling Dispatch & fleet management Grade control Fuel, cycle time, productivity KPIs 4. Key Components of Strategic Mine Planning 1. Geological Model 3D block model Grade distribution Resource/reserve classification (UNFC, JORC) 2. Mine Design & Pit Optimisation Pit limits Pushbacks & phases Haul roads, ramps Production benches & sequences 3. Production Scheduling 4. Equipment & Technology Strategy 5. Financial Optimization CAPEX & OPEX forecasting Discounted cash flow (DCF) modelling Sensitivity analysis (grade, fuel, price) NPV maximisation 6. Risk Assessment 7. ESG & Mine Closure Integration 8. Strategic Mine Planning Workflow (Step-by-Step) 1. Exploration & Data Collection 2. Block Modelling (3D) 3. Pit Limit Optimisation 4. Mine Design 5. Production Scheduling 6. Equipment Optimisation 7. Cost & Revenue Modelling 8. NPV Optimization 9. ESG Integration 10. Approval & Implementation