Ore Reserve Estimation: Methods, Importance & How It Works

Introduction: Why Ore Reserve Estimation Matters

Every mining investment decision ultimately depends on a single fundamental question: how much economically extractable mineral is in the ground? The answer to that question comes from ore reserve estimation — the process of quantifying the tonnage and grade of mineralisation within a deposit and determining what proportion can be profitably mined under current economic and technical conditions.

Ore reserve estimation sits at the intersection of geology, statistics, engineering, and economics. It transforms raw exploration data — drill hole assays, geological observations, and geophysical measurements — into the quantified resource and reserve statements that investors, banks, regulators, and mining companies rely on to make multi-million-dollar decisions. An accurate reserve estimate can secure project financing, attract investment, and justify the construction of a mine. An inaccurate one can lead to catastrophic project failure, financial loss, and reputational damage.

This article explains the key concepts behind ore reserve estimation, describes the most widely used estimation methods, outlines the internationally recognised classification systems, and discusses why getting the estimate right is essential for every stage of the mining value chain. For context on where reserve estimation fits within the broader exploration process, see our guide to the mineral exploration process.

Mineral Resources vs Ore Reserves: Understanding the Distinction

Before examining estimation methods, it is essential to understand the distinction between mineral resources and ore reserves — two terms that are often confused but have fundamentally different meanings.

Mineral Resources

A mineral resource is a concentration of minerals in the ground that has been identified through exploration and sampling and that has reasonable prospects for economic extraction. Mineral resources represent what the geologist believes is present based on the available data, but they have not yet been subjected to the full technical and economic evaluation required to confirm that they can actually be mined profitably.

Mineral resources are classified into three categories based on the level of geological confidence:

Inferred Resources — estimated on the basis of limited geological evidence and sampling. The geological and grade continuity are assumed but not verified. Inferred resources represent the lowest level of confidence and carry the highest degree of uncertainty. They are useful for initial project evaluation and exploration planning but should not be relied upon for investment decisions or mine planning.
Indicated Resources — estimated with a reasonable level of confidence based on more detailed exploration data, including closer-spaced drilling and sampling. The geological and grade continuity can be reasonably assumed, though some uncertainty remains. Indicated resources provide a sufficient basis for preliminary economic evaluation and prefeasibility studies.
Measured Resources — estimated with a high level of confidence based on detailed and closely-spaced exploration data. The geological and grade continuity is well established and verified. Measured resources represent the highest level of geological confidence and form the basis for definitive feasibility studies and mine planning.

Ore Reserves

An ore reserve is the portion of a measured or indicated mineral resource that has been demonstrated to be economically mineable after applying all relevant modifying factors. These modifying factors include:

Mining method and recovery rates
Metallurgical processing and extraction efficiency
Capital and operating costs
Commodity prices and revenue projections
Environmental, social, and regulatory requirements
Infrastructure and logistics
Royalties, taxes, and government participation

Ore reserves are classified into two categories:

Probable Reserves — derived from indicated resources (and in some cases, measured resources) after applying modifying factors. They carry a lower level of confidence than proven reserves but are sufficient for mine planning and investment decisions.
Proven Reserves — derived from measured resources after applying modifying factors. They represent the highest level of confidence that the material can be economically extracted and are the foundation for definitive mine design and financing.

The critical difference is this: a mineral resource tells you what is geologically present; an ore reserve tells you what can be profitably extracted. A deposit may have a large mineral resource but a small ore reserve if economic conditions are unfavourable, or vice versa if commodity prices are high and mining costs are low.

International Reporting Standards

Ore reserve and mineral resource estimates are reported under internationally recognised standards that ensure transparency, consistency, and credibility. The two most widely used standards are:

JORC Code

The Joint Ore Reserves Committee (JORC) Code is the Australasian standard for reporting mineral resources and ore reserves. It is administered by the Joint Ore Reserves Committee of the Australasian Institute of Mining and Metallurgy (AusIMM) and is the mandatory reporting standard for companies listed on the Australian Securities Exchange (ASX). The JORC Code is widely used across Africa, Asia, and the Pacific.

NI 43-101

National Instrument 43-101 (NI 43-101) is the Canadian standard for reporting mineral projects. It is mandatory for companies listed on the Toronto Stock Exchange (TSX) and TSX Venture Exchange and is widely used in North America, Latin America, and internationally. NI 43-101 requires that technical reports be prepared by or under the supervision of a Qualified Person — an individual with relevant professional qualifications and experience.

SAMREC Code

The South African Code for Reporting of Exploration Results, Mineral Resources, and Mineral Reserves (SAMREC) is used by companies listed on the Johannesburg Stock Exchange (JSE) and is the standard most commonly used in Southern Africa.

Common Principles

While these standards differ in some procedural details, they share common core principles:

Competent/Qualified Person — estimates must be prepared or supervised by a professionally qualified individual with relevant experience
Transparency — all material assumptions, parameters, and methods must be disclosed
Materiality — all information that could reasonably affect an investor's assessment of the project must be reported
Competence — estimates must reflect the application of appropriate technical methods and professional judgment

Ore Reserve Estimation Methods

Several mathematical and statistical methods are used to estimate the tonnage and grade of mineralisation within a deposit. The choice of method depends on the geological characteristics of the deposit, the density and distribution of the available data, and the stage of the project. Below, we examine the three most widely used approaches.

Geostatistics (Kriging)

Geostatistics is the most sophisticated and widely used family of estimation methods in modern mining. Developed from the pioneering work of Danie Krige (a South African mining engineer) and Georges Matheron (a French mathematician) in the 1950s and 1960s, geostatistical methods use the spatial correlation structure of the data to produce optimal, unbiased estimates of grade at unsampled locations.

How Kriging Works

The geostatistical estimation process involves several key steps:

Exploratory data analysis — the raw assay data from drill holes are examined for statistical distributions, outliers, and spatial trends. Grade values are often transformed (e.g., using logarithmic or other transformations) to improve the statistical properties of the data.
Variogram modelling — the variogram is the central tool of geostatistics. It quantifies how grade values change as a function of distance and direction between sample points. The variogram captures the spatial continuity of the mineralisation — how grades are correlated at short distances and how that correlation decays with increasing separation. The modelled variogram encodes the geological structure of the deposit into a mathematical form that the estimation algorithm can use.
Kriging estimation — using the variogram model and the available sample data, the kriging algorithm calculates the optimal weighted average of nearby samples to estimate the grade at each unsampled point or block within the deposit. The weights are determined mathematically to minimise the estimation variance — that is, to produce the most accurate estimate possible given the available data. Kriging also provides a measure of the estimation uncertainty (the kriging variance) at each point, which is valuable for resource classification and risk assessment.

Types of Kriging

Several variants of kriging are used in mineral resource estimation:

Ordinary Kriging (OK) — the most commonly used variant, which assumes a locally constant but unknown mean grade. It is robust, well-understood, and suitable for most deposit types.
Simple Kriging (SK) — assumes a known and constant mean grade across the deposit. It is used in specific applications, particularly in the estimation of recoverable resources.
Indicator Kriging (IK) — estimates the probability that the grade at a location exceeds a specified threshold. It is useful for estimating the proportion of material above a cut-off grade and for modelling grade distributions.
Universal Kriging (UK) — accounts for spatial trends in the mean grade, making it suitable for deposits where grade varies systematically across the deposit (e.g., increasing with depth).

Advantages of Geostatistics

Provides optimal, unbiased estimates that minimise estimation error
Quantifies estimation uncertainty, supporting resource classification and risk assessment
Honours the spatial structure of the mineralisation through variogram modelling
Widely accepted and understood by the international mining and investment community
Supported by mature software tools and a large body of technical literature

Block Modelling

Block modelling is the standard framework for representing a mineral deposit in three dimensions and for calculating the total tonnage and grade of the resource. It is not strictly a standalone estimation method but rather a spatial framework within which estimation methods such as kriging are applied.

How Block Modelling Works

The process of building a block model involves the following steps:

Define the model extent — the three-dimensional volume containing the deposit is divided into a regular grid of rectangular blocks. The block size is chosen based on the drill hole spacing, the geological characteristics of the deposit, and the planned scale of mining. Typical block sizes range from 5 to 25 metres on a side, though this varies with the deposit type and data density.
Geological domain modelling — the deposit is divided into geological domains — zones with distinct geological characteristics, mineralisation styles, or grade populations. Domain boundaries are defined based on geological interpretation of drill hole data, geological mapping, and geophysical surveys. Estimating grade within geologically coherent domains, rather than across the entire deposit indiscriminately, significantly improves the accuracy and reliability of the estimate.
Grade estimation — each block within the model is assigned an estimated grade using one of the available estimation methods (kriging, inverse distance weighting, nearest neighbour, etc.). The grade for each block represents the average grade of the rock within that block volume.
Tonnage calculation — the tonnage of each block is calculated by multiplying the block volume by the estimated rock density. Density values may be assigned by domain based on density measurements from drill core samples.
Classification — each block is assigned a resource classification category (measured, indicated, or inferred) based on the density of the surrounding data, the quality of the geological interpretation, and the level of estimation confidence. Blocks close to drill holes with tight spacing are classified at higher confidence levels than blocks far from drill holes or in areas with sparse data.
Reporting — the total resource is calculated by summing the tonnage and grade of all blocks within each classification category, typically reported above a specified cut-off grade.

Advantages of Block Modelling

Provides a fully three-dimensional representation of the deposit geometry, grade distribution, and geological domains
Integrates geological interpretation directly into the estimation process
Supports mine planning, scheduling, and optimisation by providing block-by-block tonnage and grade data
Allows for easy application of different cut-off grades, economic parameters, and mining scenarios
Standard practice in the global mining industry, supported by specialised software packages

Polygonal Methods

Polygonal estimation is one of the oldest and simplest methods for estimating mineral resources. While less sophisticated than geostatistical approaches, it remains useful in certain situations and provides an intuitive, easily understood estimate.

How the Polygonal Method Works

In the polygonal method, the area of influence around each drill hole or sample point is defined by a polygon — typically constructed using the perpendicular bisectors of the lines connecting adjacent drill holes (known as the Thiessen or Voronoi polygon method). The grade of the drill hole is assigned uniformly to the entire polygon, and the tonnage is calculated by multiplying the polygon area by the estimated thickness of the mineralised zone and the rock density.

When Polygonal Methods Are Used

Early-stage projects with limited drill data where more sophisticated methods are not justified
Quick scoping estimates to provide a preliminary indication of the resource potential
Cross-checking geostatistical estimates to ensure reasonableness
Tabular or vein-type deposits with relatively uniform thickness and grade

Limitations

Assigns all material within a polygon the same grade, ignoring spatial trends and variability
Does not account for the spatial correlation structure of the data
Tends to produce "blocky" estimates that do not reflect the true grade distribution
Provides no measure of estimation uncertainty
Generally not considered sufficient for definitive feasibility or bankable resource estimates under modern reporting standards

Other Estimation Methods

Beyond the three primary methods described above, several additional techniques are used in specific situations:

Inverse Distance Weighting (IDW)

Inverse Distance Weighting estimates the grade at an unsampled location as a weighted average of nearby samples, with weights inversely proportional to the distance from the estimation point. Closer samples receive higher weights than more distant ones. IDW is straightforward to implement and produces smoother estimates than the polygonal method but does not account for the spatial correlation structure (variogram) of the data and is generally less accurate than kriging.

Nearest Neighbour

The nearest neighbour method assigns the grade of the closest sample to each unsampled point or block. It is the simplest possible estimation method and is primarily used for initial, order-of-magnitude estimates or as a data validation tool. It does not account for multiple samples or spatial variability and is not suitable for resource reporting.

Multiple Indicator Kriging (MIK)

MIK extends indicator kriging to estimate the full grade distribution at each block, rather than just the mean grade. It is particularly useful for deposits with highly skewed grade distributions (such as gold deposits) where the mean grade is sensitive to a small number of very high-grade samples. MIK provides estimates of the recoverable resource above different cut-off grades, which is valuable for mine planning and economic evaluation.

Why Accurate Ore Reserve Estimation Matters

The consequences of inaccurate ore reserve estimation ripple through every aspect of a mining project. Understanding these consequences underscores why the estimation process demands rigorous methodology, quality data, and experienced professionals.

Investment and Financing

Resource and reserve estimates are the primary basis on which mining projects are valued and financed. An overestimated reserve can attract investment that is not justified by the actual deposit, leading to financial losses, project write-downs, and loss of investor confidence. An underestimated reserve may cause a viable project to be abandoned or underfinanced, leaving economic value unrealised.

Financial institutions and investors increasingly require resource estimates to comply with JORC, NI 43-101, or equivalent standards as a condition of financing. Non-compliant or poorly documented estimates are unlikely to withstand scrutiny during due diligence.

Feasibility Studies

The feasibility study — the comprehensive technical and economic evaluation that determines whether a deposit can be mined profitably — is built directly on the resource and reserve estimate. The mine design, processing plant capacity, infrastructure requirements, capital expenditure, operating costs, and financial projections all flow from the estimated tonnage and grade.

Errors in the reserve estimate propagate through the entire feasibility study, potentially leading to a mine that is too large for the actual deposit, a processing plant that is either undersized or oversized, or financial projections that do not reflect reality. For more on the role of feasibility studies in mining development, see our upcoming article on mining feasibility studies.

Mine Planning and Scheduling

Once a mine is in operation, the block model and reserve estimate guide day-to-day production decisions. The mine plan determines the sequence in which blocks are extracted, the balance between ore and waste, the feed grade to the processing plant, and the expected production profile over the life of the mine. If the underlying reserve estimate is inaccurate, the mine plan will not reflect reality, leading to production shortfalls, grade variability, and economic underperformance.

Regulatory Compliance

In many jurisdictions, including Uganda, mining license holders are required to report mineral resources and reserves as part of their regulatory obligations. Accurate and compliant reporting is essential for maintaining the license in good standing and demonstrating to the government that the resource is being developed responsibly.

Corporate Governance and Disclosure

For publicly listed mining companies, resource and reserve estimates are material information that must be disclosed to shareholders and the market. Misrepresentation — whether intentional or the result of poor methodology — can result in regulatory sanctions, shareholder lawsuits, and criminal liability. The requirement for Competent/Qualified Person sign-off under JORC, NI 43-101, and SAMREC is specifically designed to ensure accountability for the estimates.

The Estimation Process in Practice

A real-world ore reserve estimation project typically follows this workflow:

1. Data Compilation and Validation

All available exploration data — drill hole locations, assay results, geological logs, density measurements, survey data — are compiled into a database. The data are rigorously validated for errors, inconsistencies, and quality control issues. Data validation is a critical but often underappreciated step; errors in the underlying data will produce errors in the estimate regardless of how sophisticated the estimation method is.

2. Geological Interpretation and Domain Modelling

Geologists interpret the drill hole data to define the three-dimensional geometry of the deposit and the boundaries of geological domains. This interpretation integrates lithological, structural, alteration, and mineralisation information to create a geologically coherent framework for estimation.

3. Statistical Analysis

The grade data within each geological domain are analysed statistically to determine their distribution, identify outliers, and establish appropriate treatment for extreme values (such as top-cutting or grade capping). The statistical analysis informs the choice of estimation method and the parameters used in the estimate.

4. Variogram Analysis and Modelling

For geostatistical estimation, variograms are calculated for each domain and each variable of interest. The experimental variograms are modelled with appropriate mathematical functions, capturing the spatial continuity of the mineralisation in three dimensions.

5. Estimation and Validation

Grade estimates are calculated for each block in the model using the selected estimation method. The estimates are validated through a series of checks, including:

Visual validation — comparing estimated grade distributions to drill hole grades in cross-sections and plan views
Statistical validation — comparing the mean and distribution of estimated grades to the input data
Swath plots — comparing estimated and drill hole grades across the deposit in different directions
Change of support — verifying that the estimates behave correctly when block sizes are changed

6. Classification

Blocks are classified as measured, indicated, or inferred based on predefined criteria related to data density, estimation quality, and geological confidence. The classification criteria must be consistent with the applicable reporting standard.

7. Reporting

The final resource and reserve statement is compiled, documenting the tonnage, grade, and classification of the deposit. The report includes all material assumptions, parameters, and methods used in the estimate, as required by the applicable reporting standard.

ALOM's Ore Reserve Estimation Capabilities

ALOM Mining & Geohydro Services provides professional ore reserve estimation services as part of our comprehensive mineral exploration offering. Our technical team applies industry-standard geostatistical methods, block modelling techniques, and polygonal methods to deliver resource and reserve estimates that meet international reporting standards.

Our capabilities include:

Database compilation and validation — rigorous data management to ensure the integrity of the input data
Geological interpretation and 3D domain modelling — expert geological modelling using industry-standard software
Geostatistical analysis — variogram modelling and kriging estimation for all deposit types
Block model construction — three-dimensional resource models with domain-constrained grade estimation
Resource classification — systematic classification in accordance with JORC, NI 43-101, or client-specified standards
Reporting — comprehensive technical reports documenting the estimate methodology, parameters, and results

We work with exploration companies, mining operators, and investors across Uganda and the broader East African region, providing the technical foundation for informed decision-making at every stage of the mining value chain.

Conclusion

Ore reserve estimation is the critical bridge between geological exploration and mining investment. It translates the scientific data collected through exploration into the quantified statements of tonnage, grade, and economic viability that drive project financing, mine development, and production decisions. The methods used — from geostatistics and block modelling to classical polygonal approaches — each have their strengths and appropriate applications, but all share the common requirement for quality data, sound geological interpretation, and rigorous professional execution.

For mining projects in Uganda and across East Africa, where exploration is revealing increasingly significant mineral deposits, the importance of accurate and internationally compliant resource and reserve estimation will only grow. Investors, regulators, and communities all have a stake in ensuring that the estimates underpinning mining decisions are credible, transparent, and prepared to the highest professional standards.

Whether you are advancing an exploration project through the resource definition stage, preparing a feasibility study, or seeking financing for mine development, ore reserve estimation is one of the most important technical processes you will undertake. Getting it right is not optional — it is the foundation on which every successful mine is built.

Frequently Asked Questions

What is the difference between a mineral resource and an ore reserve?

A mineral resource is a geological estimate of the tonnage and grade of mineralisation in the ground, classified by level of geological confidence (inferred, indicated, measured). An ore reserve is the portion of a mineral resource that has been demonstrated to be economically mineable after applying all technical, economic, environmental, and regulatory modifying factors. In simple terms, a resource tells you what is there; a reserve tells you what can be profitably extracted.

Which estimation method is most accurate?

Geostatistical methods, particularly ordinary kriging, are generally considered the most accurate for mineral resource estimation because they account for the spatial correlation structure of the data and minimise estimation error. However, the accuracy of any method depends on the quality and density of the input data, the geological complexity of the deposit, and the skill of the estimator.

What is a variogram and why is it important?

A variogram is a statistical tool that quantifies how grade values vary as a function of distance and direction between sample points. It captures the spatial continuity of the mineralisation and is the mathematical foundation of geostatistical estimation methods. The variogram model directly influences the kriging weights and therefore the estimated grades — making accurate variogram modelling one of the most critical steps in the estimation process.

How many drill holes are needed for a reliable estimate?

There is no fixed number. The required drill density depends on the geological complexity of the deposit, the variability of the grades, and the level of confidence required. A geologically simple, uniform deposit may be adequately defined with fewer holes, while a complex, irregular, or high-variability deposit may require dense drilling. The variogram analysis helps determine whether the available data are sufficient to support the desired level of classification.

Can ALOM perform ore reserve estimation for my project?

Yes. ALOM Mining & Geohydro Services provides ore reserve estimation as part of our mineral exploration services. Our team uses industry-standard geostatistical methods, block modelling, and polygonal techniques to deliver resource and reserve estimates that meet international standards. Contact us to discuss your project requirements.

What software is used for ore reserve estimation?

Industry-standard software packages used for resource estimation include Datamine, Surpac (GEOVIA), Vulcan (Maptek), Leapfrog (Seequent), and Micromine, among others. These tools support geological modelling, variogram analysis, block model construction, grade estimation, and resource reporting. The choice of software depends on the project requirements and the estimator's expertise.