How many drillholes do I need to upgrade my Inferred Mineral Resource? Andrew Fowler, Kathy Zunica AMC Consultants Pty Ltd Summary The aim of an in-fill drilling programme is to gain more detailed information so as to convert lower-confidence Inferred Mineral Resources to the higher-confidence categories of Indicated and Measured (grouped together in this paper as higher resource categories ). This upgrade in resource category is necessary to convert a resource to an Ore/Mineral Reserve. Generally, an in-fill drilling programme increases the proportion of higher resource categories relative to Inferred. Ideally, this is done using the fewest drillholes possible. Can we, however, quantify this relationship between drillhole number and conversion to higher resource categories? This could help to develop a benchmark that companies could use to determine how many drillholes would be required to upgrade a desired proportion of their Inferred Mineral Resource. Through an analysis of 50 case studies, this paper aims to see if this relationship can be quantified. In doing so, it is hoped that we can also assist companies to answer other pertinent questions, including: Does the drilling programme meet our Resource upgrade expectations? Are factors such as the commodity type or a change in the Competent Person /Qualified Person (CP/QP) influencing whether in-fill drilling programmes are achieving the desired outcome? What are our expectations of the change in grade when we change the proportion of higher resource categories relative to Inferred? The 50 case studies analysed were selected from the AMC Consultants Pty Ltd (AMC) project database and publically available information. They include precious metal, base metal and bulk commodity projects from the last ten years, reported in accordance with the Australasian JORC Code 1 and the Canadian NI 43-101 2 requirements. For each case study, two consecutive Mineral s are compared.. The percentage increase in the number of drillholes between the two reports is then plotted against the proportional increase in units of contained metal and/or tonnage in the higher resource categories relative to the Inferred Mineral Resource category. The resulting statistics act as proxies for drillhole spacing and resource category upgrade, and allow comparison across different projects and commodity groups. The outcomes of the comparisons vary, but there is a pattern. Some in-fill drilling programmes, for example, result in substantial conversion to higher resource categories. Others result in little change. Despite this variance, the data displays a consistent pattern when grouped into different populations. This suggests a general relationship between increased drillhole density and conversion from Inferred to higher resource categories. The authors hope that the results and discussion presented here will elicit further questions and discussion that will ultimately improve and optimize in-fill drilling programmes. Introduction Comparing mining operations by cost and productivity metrics is common in the mining industry and is referred to as benchmarking. Benchmarking can be defined as measuring an organization s policies, products, programmes, and strategies, and comparing these with 1 Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves, 2012 Edition, prepared by the Joint Ore Reserves Committee of the Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia. 2 The CIM Definition Standards on Mineral Resources and Reserves (CIM Definition Standards) establish definitions and guidance on the definitions for mineral resources, mineral reserves, and mining studies used in Canada. The Mineral Resource, Mineral Reserve, and Mining Study definitions are incorporated, by reference, into National Instrument 43-101 Standards of Disclosure for Mineral Projects (NI 43-101).
standard measurements or the measurements of the company s peers. (BusinessDictionary.com, 2014). AMC has undertaken benchmarking studies of mining operations for more than 20 years. As a result, it has built a substantial database of open-pit and underground data, with the aim of identifying and quantifying improvement opportunities for its clients. (Berry, 2014; Scholz, 2014). In benchmarking studies, geological functions have not generally received the same scrutiny as mining functions. This is mainly due to the difficulty of establishing appropriate metrics for geological functions that allow comparison across mine sites (Berry, 2014). Nevertheless, there are examples where a number of projects or operations have been compared and where implications for geological data capture have been assessed (Berry & McCarthy, 2006). This study applies AMC s benchmarking methodology to compare 50 mineral projects and establish a relationship between in-fill drilling programmes and conversion of Mineral Resources from Inferred to higher Resource categories. Method Table 1 below is an example of the method used in this study, applied to publicallyavailable information on the Juanicipio Silver Project in Mexico. It compares two consecutive Mineral s (from 2012 and 2014). The proportions are calculated on the basis of contained metal. Table 1 Example of applied method 2012 estimate 2014 estimate Tonnag e (Mt) Ag (g/t) Tonnag e (Mt) Ag (g/t) Inferred 4.3 513 5.1 372 Indicated 5.7 702 10.1 511 Indicated proportion proportion change Drillhole number 0.64 0.73 +0.09 222 262 Increase in drilling 18% Indicated proportion = Indicated proportion of total in units of contained metal. Mt = million tonnes, Ag = Silver, g/t = grams per tonne The method and calculations used in the study are detailed below: 1. Projects were found in the AMC project database or online that met the following criteria: a. Two consecutive Mineral Resource reports were available that documented: i. Mineral Resource tonnage and grade by Mineral Resource category at the same cut-off grade. ii. Number of drillholes that informed each Mineral Resource estimation. b. The two reports showed a change in the Mineral Resource by category. 2. Project information, tonnage, grade, number of drillholes, and reasons for the change in the Mineral Resource were recorded by the authors in a database. 3. Percentage change in drillholes was calculated as: Where: DH curr = Number of drillholes in the most recent Mineral DH prev = Number of drillholes in the previous Mineral 4. Measured + Indicated Mineral Resource proportion change was calculated as: Where: MI curr = Measured + Indicated contained metal as a percentage of total contained metal in the most recent Mineral MI prev = Measured + Indicated contained metal as a percentage of total contained metal in the previous Mineral Resource report MI curr calculated as:
Where: MT curr = Measured Mineral Resource tonnage in the most recent Mineral MG curr = Measured Mineral Resource grade in the most recent Mineral IT curr = Indicated Mineral Resource tonnage in the most recent Mineral IG curr = Indicated Mineral Resource grade in the most recent Mineral TT curr = Measure + Indicated Mineral Resource tonnage in the most recent Mineral TG curr = Measured + Indicated Mineral Resource grade in the most recent Mineral Similarly, MI prev was calculated as: The case studies were selected from AMC s in-house project database and from publically available NI 43-101 Technical Reports. The earliest case study is from 2002, although the majority of studies are from the last three years (2012 2014). The projects represented in the case studies are located in twenty-one countries across six continents (see Figure 1 below). Figure 1 Case Studies by Country In Figure 2, the case studies are grouped into commodity types. Figure 2 Case Studies by Commodity Group Commodity Group Where: MT prev = Measured Mineral Resource tonnage in the previous Mineral MG rev = Measured Mineral Resource grade in the previous Mineral Resource report IT prev = Indicated Mineral Resource tonnage in the previous Mineral IG prev = Indicated Mineral Resource grade in the previous Mineral Resource report TT prev = Measure + Indicated Mineral Resource tonnage in the previous Mineral TG prev = Measured + Indicated Mineral Resource grade in the previous Mineral 5. Various charts were plotted from the resulting output. Case studies 28% 20% 12% 40% Bulk Commodity Base Metal Precious Metal - Epithermal/Porphyry Precious Metal - Shear/Breccia Table 2 lists the number of case studies by deposit type. Table 2 Case Studies by Deposit Type Deposit Type Commodity Group Number Base Metal/PM Shear/Breccia hosted (Shear/Breccia) 1/12 Porphyry Base Metal/PM 7/3 Epithermal PM (Epithermal) 7 Skarn Base Metal 3 Iron Formation BC 4 Carlin-type gold PM (Shear/Breccia) 2
Stratiform Copper Base Metal 2 Ultramafic Ni-Cu sulphide Base Metal 2 Iron Oxide Copper Gold Base Metal 1 Mineral sand: Zircon Titanium BC 1 Nickel Laterite Base Metal 1 Pegmatite Lithium Base Metal 1 Phosphate: upwelling type BC 1 Sedimentary Lead-Zinc- Silver Base Metal 1 VMS Base Metal 1 Note: VMS = Volcanogenic Massive Sulphide, BC = Bulk Commodity, PM = Precious Metal The relationship between in-fill drilling and resource upgrade To investigate the relationship between infill drilling and resource upgrade, proportional change in higher resource categories is plotted against percentage increase in drilling in Figure 3. Each point represents one of the 50 case studies or projects evaluated. The dashed line in the figure divides the positive proportion change projects from the negative proportion change projects. Positive proportion change means the higher resource categories have increased at the expense of the Inferred Mineral Resource. Conversely, negative proportion change means the Inferred Mineral Resource has increased at the expense of the higher resource categories. It is important to note that most drilling programmes actually include a combination of in-fill and extensional drilling. However, in a normal situation, only the in-fill drilling will lead to conversion to higher Resource categories. Conversely, purely extensional drilling often adds to the Inferred Mineral Resource and not the Measured or Indicated Mineral Resource. Therefore, the authors interpret that the positive proportional change projects are dominated by in-fill drilling, while the negative proportional change projects are dominated by extensional drilling. This is a simplification of reality, but it aids interpretation of the plots. 1. An optimal population where upgrade to higher Mineral Resource categories is positively correlated with increased drilling. The optimal group is defined as those projects where greater proportion increase is achieved with the least amount of drilling. 2. A sub-optimal population, where there is generally a positive correlation between upgrade to higher Mineral Resource categories with increased drilling. The suboptimal group is offset from the optimal group because it shows that significantly more drilling was required to achieve similar proportion changes. 3. Extensional drilling population showing negative proportional change. This population will not be considered further in this study, because the projects in this population do not contain a significant in-fill drilling component. 4. No significant in-fill drilling population, where significant upgrade to higher Mineral Resource categories has occurred with little or no change in drilling. This population will not be included in the analysis, because the projects in this population do not contain a significant in-fill drilling component. However, the proposed reasons for the upgrade are presented below. Figure 3 Proportion change (metal) versus percentage change in drillholes The 50 projects fall into four populations, as annotated in Figure 3: Note:
A = Dominant effect is in-fill drilling B = Dominant effect is extensional drilling Optimal and sub-optimal populations The optimal population exhibits a similar linear trend to the sub-optimal population. However, significantly more drilling was required to achieve the same proportion changes in the sub-optimal population. The authors analyzed the resource reports and propose the following reasons to account for the sub-optimal population: 1. The in-fill drilling programme did not intersect sufficient mineralization above cut-off and/or did not target areas that would increase the confidence in the Mineral Resource. 2. The initial estimation method and/or interpretation was inappropriate. The additional information changed the understanding of the deposit. The change in understanding meant that more drillholes were required to increase the confidence in the deposit. No significant in-fill drilling population This population represents projects where there was very little additional drilling between estimates, but there was significant upgrading to the higher resource categories for other reasons. The authors have analyzed the resource reports and propose the following reasons for the significant upgrading: 1. The initial estimate was based only on historical data, but then a limited drilling programme verified historical data, resulting in large portions of the resource being upgraded from Inferred to Indicated. 2. The initial estimate was based only on historical data, but then an additional validation work improved confidence in the historical data, resulting in large portions of the resource being upgraded from Inferred to Indicated. No additional drilling occurred between estimates. 3. A change in Competent Person (CP)/Qualified Person (QP) led to considerable changes in the geological interpretation, estimation method, and confidence in the Mineral Resource estimate. Very few additional drillholes were completed between estimates. The correlation between in-fill drilling and resource upgrade for the optimal and sub-optimal populations The aim of the paper is to investigate how in-fill drilling programmes affect the upgrade of Mineral Resources, with the ultimate aim of establishing a benchmark for how many drillholes are required to upgrade a desired proportion of the Mineral Resource from Inferred to higher resource categories. The optimal and sub-optimal populations both exhibit a positive correlation between increased drilling and resource upgrade (Figure 4). Trend lines fitted to each population give correlation coefficients of 0.54 for the optimal population and 0.77 for the sub-optimal population, respectively. The steeper slope of the optimal population suggests that fewer drillholes are resulting in a higher proportion change relative to the sub-optimal population. The authors consider that these are meaningful correlations that further studies will help to refine and improve. The results are elaborated upon further in the discussion section of this paper. Figure 4 Proportion change (metal) versus percentage change in drillholes: infill drilling projects only
Commodity type, Resource size and change of CP/QP The relationship between in-fill drilling and resource upgrade is investigated further by plotting the case studies by commodity groups (Figure 5), resource size groups (Figure 6), and if the CP/QP changed between estimates (Figure 7). The results are interpreted in the discussion section of this paper. Figure 5 Proportion change (metal) versus percentage change in drillholes grouped by commodity type Figure 6 Proportion change (metal) versus percentage change in drillholes grouped by total Resource size Relationship between resource upgrade and grade The effect on grade when the resource is upgraded from Inferred to higher resource categories is investigated by plotting positive and negative grade change against resource proportional change. The grade changes are calculated as percentages. The resource proportional changes are calculated as changes in tonnes rather than metal, as in the examples above. The results are grouped by comodity type (Figure 8) and are interpreted in the discussion section. Figure 8 Proportion change (tonnes) versus grade change: grouped by commodity type Figure 7 Proportion change (metal) versus percentage change in drillholes grouped by total change in CP/QP Discussion Drilling programmes with a significant in-fill component, display a positive linear correlation between percentage increase in drilling and proportional increase in higher resource categories. This positive linear correlation was observed in two populations the optimal and sub-optimal. The authors propose that projects in the sub-optimal population relate to instances where:
1. The CP/QP was not adequately consulted during the planning phase to avoid redundancy in the drilling programme. 2. The initial geological interpretation of the project was simplistic and subsequent drilling revealed complexity that reduced, rather than increased, geological confidence. 3. There were managerial, contractual, regulatory or other requirements to complete drillholes that were not specifically related to resource upgrading. An optimal in-fill drilling programme will upgrade the resource using the fewest drillholes, targeting locations that will define mineralization and increase confidence in the grade and geological continuity. CPs and QPs should design drilling programmes that lie within the optimal population. For example, if a company wishes to have a proportion change of 0.2, the company should increase drilling by 25%. In contrast, achieving a proportion change of 0.2 would require a 110% increase in drilling in the sub-optimal population. Figures 5 7 in this paper attempt to quantify whether commodity type, resource size, or CP/QP influences can explain the optimal and sub-optimal populations. Firstly, there appears to be no obvious relationship between commodity type and whether the project sits in the optimal population or sub-optimal population. However, there is one trend that warrants further investigation the fact that the majority of base metal projects lie in the optimal population. Regarding resource size, the majority of projects with less than 20 Mt in total Mineral Resource lie within the optimal population. A possible reason for this could be that when there is a smaller area that requires in-fill drilling, design of the programme is relatively straightforward. Again, further investigation is required. Regarding change of the CP/QP, there seem to be no obvious trends. Figure 8 explores how the grade changes with the in-fill drilling programme and whether this is impacted by commodity type. The mean percentage grade change is negative across all commodity types,. This suggests that earlier interpretations based on fewer drillholes assumed continuity that subsequent drilling disproved. This is possibly a reflection of human nature, as we tend to be optimistic in our geological interpretation at the early stage of a project. Another trend observed in Figure 8is the large variation in grade changes for the precious metal projects relative to the bulk commodities. This is expected considering that precious metal deposits are naturally more variable than bulk commodities. What was unexpected by the authors, however, was the trend towards more negative-mean grade changes as the variability of the deposit increases. An interesting question that is raised by this is: if the grade is expected to decrease with in-fill drilling, should this not affect how we classify the Mineral Resources in the first place? Further investigation is required to better understand how the grade is impacted with in-fill drilling and the increasing confidence in the Mineral Resource. The authors consider that there is further opportunity to expand on the results presented in this paper. In particular, through acquiring and analyzing more case studies, there is opportunity to confirm the optimal population and whether the optimal population versus the sub-optimal population is a real phenomenon. This might allow the establishment of a benchmark for CP/QPs to aspire to. Conclusion The study results and discussion are presented to assist CP/QPs that are planning drill programmes and managers that approve drilling budgets. It is hoped that the results will prompt CP/QPs and managers to ask if the proposed drill programme will meet the resource upgrade objectives. Where the proposed drill programme is not expected to produce the resource upgrade predicted by the optimal population, it might be pertinent to consider if there is redundancy in the drilling programme.
Further research will build upon what is considered in this paper and will help exploration geologists, CP/QPs and budget managers to achieve their drilling programme objectives while minimizing expenditure. Practical tools that optimize the drilling programme so that it does not fall into the sub-optimal population should be the focus of future work. REFERENCES Berry, M and McCarthy, P, 2006. Practical consequences of geological uncertainty, in Proceedings Sixth International Mining Geology Conference, pp 253-258 (The Australasian Institute of Mining and Metallurgy: Melbourne). Berry, M., (2014). Benchmarking Does it have a role in Improving the performance of mining geology?, in Proceedings Ninth International Mining Geology Conference, pp 361 366. (The Australasian Institute of Mining and Metallurgy: Melbourne). BusinessDictionary.com, (2015). [Online]. Available from: http://www.businessdictionary.com/definit ion/benchmarking.html. Ross, D., Cox, J., Krutzelmann, H., (2014). Technical Report on the Mineral Resource update for the Juanicipio joint venture, Zacatecas State, Mexico. Prepared for MAG Silver Corp by Roscoe Postle Associates Incorporated. Scholz, M., (2014) Real Benchmarking [Online]. Available from: http://www.amcconsultants.com/digging deeper articles/real-benchmarking Thomas, M., Thalenhorst, H., Riles, A., (2012). Minera Juanicipio Property, Zacatecas State, Mexico. Technical Report. Prepared for Minera Juanicipio S.A. de C.V. by AMC Mining Consultants (Canada) Limited. Con formato: Español (Perú)