Webinar: Resource modeling and strategic planning. Part 1

K-MINE is a mining software company with nearly 30 years on the market. The platform provides a single, customizable application built on a proprietary calculation core. This core processes data faster and delivers more precise results for resource estimation, blast design, and other critical calculations.

K-MINE offers 12 modules for open pit and underground mining, covering geological modeling, resource estimation, mine planning, equipment management, surveying, and more. Each module can operate standalone or work together with other modules, creating a single digital workspace where geologists, mining engineers, surveyors, and planners collaborate with real-time site data.

The company also provides custom solutions including end-to-end mine planning for corporations, integration with dispatch systems (Wenco, MineStar, Modular, Caterpillar), and an IoT platform for real-time fleet and personnel monitoring.

Regardless of the deposit type, the process starts with a geological study. Geologists use specialized software to create a database, then apply implicit or explicit modeling to interpret mineralization zones and ore body contours. Geostatistical analysis follows, and a 3D block model is created.

At this stage, the block size and list of attributes are defined. The resulting resource block model is used to evaluate mineral resources and reserves.

K-MINE implements tools for working with relational databases, built-in statistical analysis, a set of formulas and logical expressions for calculations, and visualization tools for analyzing samples in 3D space. Users can display sampling intervals as text, grade curves, histograms, or cylinders with diameters proportional to the sample value.

For ore body contouring, K-MINE supports both implicit and explicit modeling methods. Implicit modeling is suitable when there are no clear geological or lithological boundaries. Explicit modeling is appropriate when lithological or tectonic boundaries are well-defined.

At the block model creation stage, it is important to understand what the model will be used for: operational planning, mine design, or geological and technological mapping. The attribute set will differ for each case.

Choosing the correct block size is critical. The selective mining unit (SMU) is the minimum volume upon which ore/waste allocation decisions are made. SMU is usually smaller than the block model dimensions, especially at exploration and feasibility stages. Direct linear estimation of small blocks has low precision, which is why block size selection directly affects the reliability of resource estimates.

In K-MINE, users can change the minimum block size at any time and perform sub-blocking (splitting blocks into smaller units) or re-blocking (combining blocks into larger units).

Sub-blocking options in K-MINE: - Regular sub-blocking brings all blocks to the same size - Irregular sub-blocking creates minimum-size blocks only at wireframe boundaries, keeping larger blocks elsewhere

Re-blocking methods for grade recalculation: - Weighted average by volume - Weighted average by tonnage - Value by maximum volume (assigns the grade of the largest contributing block)

Using a graphite deposit, the webinar demonstrates how block size affects grade and tonnage estimates.

A block model with 120x120x15 m blocks showed high-grade ore volume of 86 million cubic meters with an average graphite content of 4.83%.

A finer model with 30x30x3.75 m blocks showed high-grade ore volume reduced to 62 million cubic meters, but the average grade increased to 6.49%.

The intermediate model (60x60x7.5 m) produced values close to the fine model. This demonstrates that oversized blocks smooth grade variability, overestimating tonnage while underestimating average grade - a common pitfall in resource estimation.

Swath plots (comparative analysis of composite grades vs. block model grades by section) help identify areas of significant discrepancy and analyze interpolation quality.

At some deposits, planning solely by useful component content does not ensure stable processing plant operation. Commercial ore may contain various technological grades with similar content but different processing properties (degree of oxidation, fracturing, material composition).

A geological-technological block model assigns both grade and processing characteristics to each block. This allows planners to schedule mining with the correct ore type feeding the plant, optimize reagent regimes, and determine whether each block should be processed, stockpiled, or sent to waste.

Strategic planning (Life of Mine) is the first and most important stage. It defines the optimal mining strategy, answers questions about company goals, obstacles, and profitability factors. The main output is a life of mine plan with multiple scenarios: low investment (reduced production), maximum investment, or maintain current operations.

The most critical input for strategic planning is geological data. If the geology and processing parameters are wrong, the entire optimization is invalid regardless of how accurate other parameters are.

Rules for successful optimization: - Communicate project objectives to the entire team - Correctly evaluate geology and processing - do not make assumptions - Double-check metal price and operating cost estimates - Evaluate multiple scenarios and alternative strategies - Analyze consequences, not just profitability - Plan mining according to mineral processing capabilities - Use marginal or break-even cutoff grade for ore assessment - Account for dilution and ore losses - Ensure the mine is properly sized for the deposit - Cross-check results using different approaches

Midterm planning covers 5-10 years with annual intervals. Strategic issues are already resolved. The focus is detailing mining areas, developing a mining calendar, and calculating cash flows for planned periods. The output answers when and where to mine, what sequence to follow, and what resources are needed.

Short-term planning covers up to 2 years with weekly, monthly, or quarterly intervals. This is the most time-consuming stage because it accounts for equipment schedules, run-of-mine maintenance, blasting schedules, stockpile balances, and processing plant feed requirements.

Operational planning covers up to 14 days, split by shifts. It is carried out by dispatchers and mine foremen, not planners. They follow directives from short-term planning and make real-time decisions on the ground.

Plan reconciliation closes the loop by comparing planned vs. actual results and feeding that analysis back to all planning departments.

The optimization project uses the following input parameters: - Block model: 60x60x7.5 m - Mineral: graphite (three groups by content) - Final product: graphite concentrate at $400/ton - Mining cost: $4/ton (mineral), $3/ton (overburden) - Processing cost: $13-18/ton depending on grade - Recovery: 4-8% depending on grade - Cutoff grade: 2% - Slope angles: 35-45 degrees by zone - Discount rate: 12% - Annual production: 15 million tons - Ore losses: 10%, dilution: 5%

Revenue adjustment factor (price factor) is applied from 0.5 to 1.2 in increments of 0.05, generating nested pit shells. Each shell corresponds to a set of blocks that are profitable at that price factor.

The differential profit chart shows maximum profitability at coefficient 0.8. However, significant mineral resource growth continues beyond this point, so the final pit contour is often adopted at a higher coefficient. In this example, coefficient 1.05 (shell #12) was selected as the final contour.

K-MINE provides several methods for evaluating the optimal final contour:

Wireframe comparison: Overlaying nested pit shells from largest to smallest shows the growth pattern. Cross-sections through the pit reveal which areas are most sensitive to price changes. In this example, the eastern part expanded significantly with price increases, and the northern wall was more sensitive than the southern one - important for planning surface infrastructure and access.

Marginal profit analysis: Marginal profit (profit-to-expense ratio) is calculated for each shell. The optimal pit is often the one closest to 20% marginal profit. In this case, coefficients 1.05 and 1.1 both met this criterion.

Export to Excel enables further custom analysis with graphs and charts tailored to the specific decision criteria of the project.

With the final contour established, the pit is divided into pushbacks (mining phases). K-MINE offers both automatic and manual pushback construction.

Automatic pushback generation parameters: - Working platform width: 60 m - Minimum pushback area: 20,000 sq. m - Minimum pit bottom area: 5,000 sq. m - Deepening parameter: 120 m - Target: three pushbacks with equal mineral tonnage

The search applies optimality criteria (NPV), continuity criteria (each pushback must be connected by at least the working platform width), and geometric constraints.

Manual pushback construction uses guiding contours or wireframes. In this example, four guiding contours divided the pit into center, south, north, and east sections. Blocks are assigned to pushbacks based on these guidelines, respecting angular dependencies. Blocks that do not fall into any pushback are placed in a separate residual phase.

Wireframes are generated for each pushback in different colors. Visualizing them from largest to smallest reveals the main mining sequence for subsequent planning stages.

This webinar covered the full cycle from resource block model creation to strategic mine planning: - Block model creation with sub-blocking and re-blocking in K-MINE - Impact of block size on grade and tonnage estimates - Open pit optimization using revenue adjustment factors - Methods for evaluating and selecting the final pit contour - Automatic and manual pushback design

Can the block model include environmental constraints? Yes. Any text or numeric information can be added as a block model attribute (e.g., wetland zones, protected areas). These attributes can then be used in visualization and planning calculations.

What is the most important stage of mine planning? Life of mine planning is typically the most critical because it resolves major strategic questions. However, the connection between planning stages is equally important - when departments create plans in isolation without feedback loops, results suffer. Plan reconciliation ensures all planning stages stay aligned.

How does re-blocking affect model accuracy? Splitting large blocks into smaller ones simply distributes the parent block's values. Combining small blocks into larger ones requires choosing a recalculation method: weighted average by volume, weighted average by weight, or using the value from the largest contributing block. Some information loss is inherent when combining blocks, which is why the re-blocking method should be selected carefully.

Does this work for underground deposits? The pit optimization model shown is for open pit mining. Underground mine planning in K-MINE uses a separate module.