Drill Baby Drill? Why the Coal Mining Industry Should Embrace Geostatistics and Stop Wasting Exploration Funds

Coal mining remains a cornerstone of global energy production, particularly in regions like South Africa where coal dominates the energy mix. However, as economic pressures rise and environmental concerns grow, mining companies are increasingly tasked with finding ways to improve efficiency, reduce costs, and provide higher-quality resource estimates. Geostatistics, a proven methodology for analyzing spatial data, offers an opportunity to transform coal exploration and classification.

A few years ago, I spent two years exploring how geostatistics could be applied in coal estimation and classification. The outcome of this labour was a thesis titled, "The Application of Geostatistics in Coal Estimation and Classification". This article reflects on those findings, showing how adopting geostatistics can enhance resource classification, accelerate exploration, and reduce drilling related costs.

What Is Geostatistics?

Geostatistics is a branch of applied statistics focused on analyzing and predicting spatially distributed data. Originally developed for the mining industry, it uses mathematical models, such as variograms and kriging, to estimate resource quantities and qualities based on limited sampling data. Unlike traditional methods that rely on fixed grid spacing, geostatistics accounts for geological variability and uncertainty, providing more accurate and reliable resource estimates.

Key techniques in geostatistics include:

• Variograms: These measure the spatial correlation between data points, helping determine how far apart drill holes should be.
• Kriging: A sophisticated interpolation method that minimizes estimation errors.
• Conditional Simulation: Provides multiple equally probable realizations of a deposit, offering insight into geological uncertainty.

The Current Standard: Limitations of Fixed Grid Spacing

Standards such as SANS 10320 prescribe rigid drilling distances for resource classification. For example, SANS recommends:?

• Measured Resources: Drill spacings of 350–500 meters.
• Indicated Resources: Drill spacings of up to 1 km.
• Inferred Resources: Drill spacings beyond 1 km.

While these guidelines provide consistency, they often result in:

• Over-Drilling: Geological continuity is often sufficient to justify wider spacing, yet companies drill unnecessarily dense grids to comply with standards.
• Higher Costs: Drilling is one of the most expensive components of exploration, and over-drilling inflates budgets.
• Inflexibility: Fixed distances ignore site-specific geology, leading to inefficiencies in resource evaluation.

Why Geostatistics Is a Better Alternative

1. Improved Quality of Resource Estimates

Geostatistics tailors the analysis to each deposit’s unique characteristics. For example, variograms help model the spatial continuity of coal seams, allowing estimates that reflect the true variability of the resource. This leads to:

• More accurate classification of resources as Measured, Indicated, or Inferred.
• Better-informed decisions about mine planning and long-term operations.

2. Faster Exploration

Traditional exploration programs often require excessive drill holes to satisfy classification standards. Geostatistics reduces this need by determining optimal drill hole spacing. By identifying areas of high geological confidence early, companies can accelerate exploration timelines.

3. Cost Reduction

Drilling campaigns are expensive. By optimizing drill hole locations, geostatistics minimizes the number of holes required, leading to substantial cost savings. For instance, studies in Australia’s Bowen Basin found that geostatistical methods could reduce drilling costs by 30% without sacrificing confidence in resource estimates (Bertoli et al., 2013).

4. Handling Geological Complexity

Coal deposits are often geologically complex, with varying seam thicknesses and qualities. Geostatistical methods like kriging and simulation can model this complexity, offering insights that are unattainable with traditional methods. These models allow companies to:

• Predict seam qualities with higher precision.
• Develop robust mine plans that account for variability.

Practical Benefits for the Coal Sector

Real-World Case Studies

• Bowen Basin (Australia): Researchers applied geostatistical drill hole spacing analysis and reduced drilling density by up to 30%, maintaining confidence in resource estimates (Bertoli et al., 2013).
• Jharia Coalfield (India): Geostatistical modeling improved seam thickness predictions, allowing for more accurate classification of resources (Saikia & Sarkar, 2013).
• South African Coalfields: Studies, including my own, have demonstrated that variograms can guide drill hole placement, reducing redundancy while enhancing resource classification (Nengovhela, 2017).

Compliance with SAMREC

The SAMREC Code (2016) emphasizes the need for reliable resource estimates prepared by competent persons. Geostatistical methods align with these principles, providing a rigorous, data-driven approach to resource estimation. By integrating geostatistics, companies can ensure compliance while optimizing exploration and classification practices.

How to Get Started with Geostatistics

Transitioning to geostatistics doesn’t have to be overwhelming. Here’s a step-by-step guide to get started:

1. Build a Strong Database

• Collect high-quality geological and geophysical data. Accurate sampling is critical for reliable geostatistical analysis.
• Ensure data is well-documented and stored in a format compatible with modern software.

2. Train Your Team

• Equip your geologists and engineers with the skills to use geostatistical tools like kriging and simulation.
• Invest in fit for purpose software packages which support geostatistical modeling.

3. Collaborate with Experts

• Partner with geostatistics consultants or academic institutions to build expertise within your organization.
• Use pilot projects to compare geostatistical methods with traditional approaches.

4. Refine Drill Spacing

• Use variograms to analyze spatial continuity and determine optimal drill hole spacing.
• Reduce drilling density in areas of high confidence, reallocating resources to areas of uncertainty.

5. Integrate Geostatistics into Reporting

• Update your resource classification workflows to include geostatistical models.
• Ensure all estimates meet SAMREC’s requirements for transparency, materiality, and competence.

A Sustainable Future for Coal Mining

The coal sector faces increasing pressure to reduce costs and operate sustainably. Geostatistics offers a way to meet these challenges by improving resource classification, accelerating exploration, and cutting costs. By complementing standards like SANS 10320 and the SAMREC Code (2016) with geostatistical methods, mining companies can:

-????????? Optimize resource estimation.

-????????? Improve operational efficiency.

-????????? Enhance transparency and compliance.

The path forward is clear: the coal mining industry must embrace geostatistics as a key tool for smarter, more sustainable exploration. ?

A Call to Action for the Coal Mining Industry

As competent persons, many of us default to over-drilling to increase confidence levels and build defendable cases (self preservation). This cautious approach, while understandable, often comes at a significant financial cost. The reality is that applying geostatistical methods which, by definition, account for geological variability and uncertainty offers a more efficient, reliable and tried and tested alternative.

Yes, there is a fear of being wrong. But is the unlikely risk of being wrong truly worth the millions of rands wasted on over-drilling to meet outdated prescriptive standards like SANS that apply a one-size-fits-all approach?

I challenge my fellow geologists and mining professionals to embrace geostatistics so that we can collectively deliver tangible cost savings, improve estimation accuracy, and, ultimately, achieve better mining outcomes. The tools and methods are available.

For more information, refer to Nengovhela, A. C. (2017). The Application of Geostatistics in Coal Estimation and Classification. MSc Thesis, University of the Witwatersrand.

References

Bertoli, O., et al. (2013). Geostatistical Drillhole Spacing Analysis for Coal Resource Classification in the Bowen Basin, Queensland. International Journal of Coal Geology, 112, 107–113.

Nengovhela, A. C. (2017). The Application of Geostatistics in Coal Estimation and Classification. MSc Thesis, University of the Witwatersrand.

South African Bureau of Standards. (2016). SANS 10320:2016: Systematic Evaluation of Coal Exploration Results, Inventory Coal, Coal Resources, and Coal Reserves. Pretoria, South Africa.

South African Mineral Resource Committee. (2016). SAMREC Code: The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves. Johannesburg: SAMCODE.

Srivastava, R. M. (2013). Geostatistics: A Toolkit for Data Analysis, Spatial Prediction, and Risk Management in the Coal Industry. International Journal of Coal Geology, 112, 2–13.

Saikia, K., & Sarkar, B. C. (2013). Coal Exploration Modeling Using Geostatistics in Jharia Coalfield, India. International Journal of Coal Geology, 112, 36–52.