Modelling the Economic Potential of Mineral Occurrences at Marble Creek Basin, Cassiar, B. C.

Retread Resources > Publications > Economic Potential of Mineral Occurrences

by Dennis Nikols, P. Geo.; Georgia L. Hoffman, P. Geol.; Zoe C. Nikols of Retread Resources Ltd.

Abstract

Evaluating the economic potential of an exploration target can be difficult and comparing targets can be even more daunting. We have developed a model that employs a Risk Adjusted Index for property/prospect ranking and can be used in a wide range of potential applications. The comparative model suggests minimum and maximum investment levels and allows for the preparation of economic evaluations. In addition to mining and geological factors, our model allows economic and social factors to be included in the calculations. We have chosen a known carbonate-hosted Pb, Zn, Ag, Mn, deposit of the late Cretaceous age located at Marble Basin 3 km south of the former townsite of Cassiar, B.C., as our base example. Our 1998 mapping program confirmed earlier work, found new potential ore zones and collected sufficient data to develop a working hypothesis of ore emplacement in order to facilitate planning, budgeting and mining engineering studies. In addition, four similar carbonate hosted Pb-Zn-Ag deposits, in various states of development, were chosen to compare/contrast the Cassiar base example in assessing potential risks and calculating investment strategies. Our model results demonstrate that a relatively small, moderate to high grade, polymetallic deposit can offer surprisingly high potential earnings.

Introduction

Mining and exploration are risk-laden businesses and over the years great efforts have been devoted to quantifying and calculating the various types of risk. A large number of the risk factors however are not easily quantifiable. For many years the mining industry has relied on models based solely on objective criteria such as proven reserves, average assays and more or less quantifiable costs. This paper describes a model that attempts to place more emphasis on the less easily quantified factors. Our approach combines the known objective data with more subjective factors to create a Risk Adjusted Index (RAI).

Our primary example is the Southern Block of the Cassiar Project Areas located in north-central British Columbia, immediately south of the Cassiar townsite. It is 100% owned by Eveready Resources Corp. (Eveready) of Calgary, and covers an area of 62 units (1 unit is approximately 25 hectares). We appreciate Eveready's cooperation and support for this paper and poster by permitting us to use confidential and published data relating to their properties.

In this paper, we have used actual Polymetallic (PM) properties to illustrate the Polymetallic Evaluation Model (PMEM). Most data such as grades and tonnes have been taken from the public record. Please feel free to apply our techniques to your own situations and examples. We hope that you will share your experiences and methodology. We view our model as a work in progress. We know it is not comprehensive. Each user will be approaching their analysis from a different point of view and with different needs and requirements. The model process must therefore be flexible without introducing undue bias.

Polymetallic deposits can be especially difficult to compare or rank when several different properties are competing for a portion of the exploration and/or development budget. Complex polymetallic Cu-Pb-Zn-Ag-Au ores and associated minerals are by nature problematic. The constituent grades often vary widely between properties and even between ore zones on a single property. Metallurgical recovery, transportation, climate and a host of other factors can compound the evaluation process. An appropriate balance between detail and simplification must be achieved to make the property ranking process produce better results than a lottery.

The Cessiar Base Example

The Cassiar area is serviced primarily from Dease Lake, B.C. and Watson Lake, Y.T., about 125 km and 145 km to the south and north, respectively. The Cassiar area has a long history of gold and asbestos mining, and contains good infrastructure, including an airstrip and a network of roads and trails. Much of the Cassiar townsite was dismantled and reclaimed after the closure of the asbestos operations in 1992, but the area is currently the scene of renewed activity.

Geologically, the Southern Block claims cover part of the Cassiar Terrane, a body of metasedimentary rocks that range in age from late Precambrian to late Paleozoic. The dominant rock types are phyllite, slate, quartzite and carbonate rocks (primarily marble and dolomitic marble). Carbonate-replacement manto and chimney deposits and associated skarn deposits occur in the Cambrian Rosella Formation, a thick sequence of carbonate rocks.

Eveready retained Retread Resources Ltd. (Retread) to plan and conduct an exploration program during the summer and fall of 1998. The work consisted of geological mapping, trenching and adit sampling, followed by 1817m of diamond drilling. Most of the 1998 work focused on the McMullen Zone, a structurally controlled alteration zone that includes discrete bodies of metallic sulfide minerals and magnetite hosted in dolomitized, rhodochrositic marble. The McMullen Zone dips almost vertically, and strikes roughly east-west, perpendicular to the strike of the marble. It has been traced over a strike length of about 1.3 km, but may be discontinuous in the central section.

In the western part of the McMullen Zone, two bodies of lead-zinc-silver minerals, termed Body A and Body B, have been partially defined by previous drilling and adit drivage. Both bodies appear to be irregular shoots or chimneys that are elongated vertically. They include masses of silver-bearing galena and sphalerite, with minor amounts of pyrite, pyrrhotite and arsenopyrite, and a variety of iron oxide minerals including magnetite. Lead, zinc and silver oxides and carbonates are sometimes present. The Lower Adit crosses both bodies.

Channel samples taken in the Lower Adit during the 1998 exploration program, excluding layers of low-grade, poorly mineralized rock material, gave a weighted average grade for Body A of 20.76 % Pb, 2.88 % Zn, 608.56 g/t Ag and 32.78 % magnetite and a weighted average grade for Body B of 9.66 % Pb, 2.47 % Zn, 199.88 g/t Ag and 36.51 % magnetite. Metallurgical testing of Body A and B indicates that oxidized ore (oxide and carbonate phases of lead, zinc and silver) occur with the sulfide phases in near-surface ore. If oxidized ore persists to depth in significant quantities, it will necessitate the use of hydro-metallurgical approaches to mineral separation and recovery, rather than conventional gravity techniques.

The Quantifiable Equation

The easier a factor is to quantify, the more weight that factor tends to have in an analysis. That simple statement has more to say about the psychology of the evaluation processes than objective reality. It produces an analysis biased toward the easily quantified factors (described by our K1 factor) while the influence of more subjective factors is consequently under-represented. K1 Factors are the quantifiable adjustments made to the grade of the ore expressed in terms of dollars (termed K-factors by Cope, 1998). We have grouped K2 Factors as the qualitative elements containing a higher risk potential that are expressed in a quantitative form. We feel that exploration projects, especially those widely separated by geography and/or political jurisdictions require an additional, more standardized process for dealing with the subjective factors, which we have attempted to do via our K2 factor.

The K1 Factor

The prioritizing process must be relatively quick and low-cost. For example, an exploration manager may have 5 PM projects in hand, all at different stages of development, in different locations, etc. This fortunate person however, only has enough budgeted resources to devote to two of the potential targets. The question then, is how to prioritize the potential deposits and where to allocate scarce resources. Our model allows for a ranking of the quantifiable influences of complex geology and metallurgy, commodity price fluctuations, corporate objectives, etc.

Some elements of the K1 Factor are :

the ore at each deposit is different;
one or more metals be dominant, but may not necessarily be the same metal at each deposit;
metal recoveries can, and probably will vary;
prices for the constituents will be different and location and access will differ and affect shipping costs;
smelter payments and deductions will vary; and
measured and potential ore quantities and ore body relationships will vary.

By computing or assigning K1 values for each metal in the ore and working through the appropriate equations, an approximate value for each ore constituent can be found:

TABLE 1 - Metric tonne base metal formula
TABLE 2 - K1 suggested elements

Determining K1 Factors

We chose seven metals and minerals to determine individual K1 factors which can then be applied to each individual deposit for a comparative analysis. K1 values have been determined by an experience-based, but not necessarily, arbitrary fashion. These guidelines should assist the evaluator(s) in maintaining a degree of objectivity in setting the specific K1 factors for each potential targets.

TABLE 3 - Suggested criteria for element valuations

The K1 value for each metal or mineral is a number between 0 and 1 and based on the above criteria a base K1 factor value set can be established for the elements under review.

Metal/Mineral	K1 Value
K1 Cu	0.60
K1 Pb	0.50
K1 Zn	0.30
K1 Au	0.80
K1 Ag	0.70
K1 Mn	0.70
K1 Magnetite	0.65

TABLE 4 - Formulated base K1 values

TABLE 5 - Average ore grades for each prospect

For the purposes of determining a K1 dollar value for each potential target we have selected December, 1998 US dollar metal prices as an index to be used in combination with the base K1 factors and the average grades of the different potential targets. The same K1 factors can be used for each deposit as long as the deposits under review are approximately the same size or potential size, and other considerations are about equal.

Application of K1 Factors

The application of the base K1 values (shown in Table 4) to the example deposits produces a relative ranking that could be used in priority setting. However, when the site-specific K1 values are substituted into the formula the ranking changes as seen in Table 8.

This model also allowed the evaluator to test the sensitivity of changes in grade, price and K1 factors. Using this approach, we have been able to identify the potential impact of both the geological risk of grades being lower then forecast and the market force impacts, should metal prices vary significantly. We were also able to estimate the magnitude of the changes before significant project impact would result. This type of analysis is possible without the use of our model, but results derived from a semi-empirical model tend to impart a psychological weight (i.e. they are viewed with a higher degree of confidence then more subjective methods).

The K1 factors can also be used with a relatively high degree of precision to rank or prioritize parts or subsets of an ore body or separate ore bodies occurring on a property. By making appropriate modifications to the K2 factors, making them highly property specific, the RAI can be applied as a planing tool in designing property exploration and development plan.

K2 Factors and the Rist Adjusted Index (RAI)

This part of the analysis attempts to deal with risks that are difficult or impossible to quantify exactly. It also allows people who are not geologists or engineers to provide meaningful input. Each element of the K2 average is important as long as the total number of elements remains between 5 and 10. Fewer than 5 elements runs the risk of overweighting one or more. More than about 10 elements tends to put all elements on a near-equal footing. These difficulties can be overcome by using a more complex weighted normalizing evaluation system. We suggest that 6 or 7 elements provides a good balance without requiring complex methodology. In addition, we suggest that once a standard description of each prospect has been assembled, the input or ranking for K2 elements need not be done by one person or even the same group of people for each element. For example, the rating of the business climates, assuming they are different for one or more prospects, may be made by non-technical management.

Potential Size (a)

The potential size of a prospect can have a significant influence on its priority ranking. At the exploration stage this is not likely to be a well known or well understood quantity. Given the level of speculation required, the forecast of potential size becomes highly judgmental. However, the experience-based prognostication of one or more professionals can provide a fair evaluation of this factor.

Established Size (b)

The established size of a prospect represents a factor that has already been quantified. However, our experience shows that the confidence levels of those measures are not likely to be uniform; a consideration that can be taken into account in the point-assigning process.

Data Density (c)

The existing data density, like established size, is also quantifiable but just as likely to be highly variable. Treating the data density objectively, by. assigning a point-rating, functions to moderate or emphasize the geological risk.

Infrastructure (d)

Very few prospects are located within a short distance of major transportation, habitation and market facilities. Given that real cost analysis is usually not performed on properties without feasibility studies, some judgment of added cost, time and planning should be made.

Political Stability (e)

The political stability, the fear of instability, or the expected change in stability of a geographic location is an important risk factor. By political stability, we mean the ability of a political unit to keep the peace, govern by the rule of law, protect property, ensure human rights and so on. Questions include: Are mining and environmental regulations reasonable and applied evenly? Am I likely to enter into regulatory difficulties down the road? Are land/mineral titles secure? The differences between two Canadian provinces may be slight, while the difference between Canada and another country may be considerable. The risk being evaluated may in fact be more perceived than quantifiable, but if it is identifiable it is real enough to judge.

Social Situation (f)

Questions include: Is sufficient skilled labor available? Is the social structure of the community healthy? Do the local people want the project? If not, what are their concerns?

Business Climates (g)

The business climate of a place includes an assessment of taxes, royalties, and mining and non-mining business regulations of a political jurisdiction. In other words, can I work here, and do I want to?

Risk Adjusted Index (RAI)

The RAI is a unitless measure that incorporates the perceived value of the potential ore (less mining and environmental overheads) that has been adjusted for perceived risk. This should not be viewed as a definitive number but a ranking system that has attempted to account for some of the most important risks effecting further development. We view it as a tool that can aid management in setting investment priories.

TABLE 6 - Ranking guidelines used in this analysis

Manipulation of K1 and K2 Factors and RAI

In formulating the priority of where to place exploration dollars, using the data from our suggested base K1 factors, reported average assay values and US dollar prices in December 1998 the properties received the following ranking:

South Yukon
Cassiar North
Cassiar South (1998 and 1999
North B.C.
Mexico

At a first glance this would seem fair in that each assay set was effected by the same factors. The variation must be related to the ore grade mix alone. Cassiar North while a quality prospect, is only a prospect, while the North B.C. property is now in the permitting stages of development. Applying only the K1 base factor is clearly only useful for properties that have little difference in non-assay variables.

When the RAI's are applied the Ranking changes to:

Southern Yukon
Cassiar South(1998 and 1999)
Northern B.C.
Cassiar North
Mexico

The K2 elements that most influenced the order in this example are: measured size, potential size and infrastructure. The Mexico example suffers from relatively low grades and even the most optimistic K1 and K2 factors would not improve that. The Cassiar North prospect is now ordered closer to its comparative knowledge and risk or confidence status.

When the site specific K1 factors are applied to the same assay set a new ranking emerges.

1. Cassiar North
2. Cassiar South 1999
3. Northern B.C.
4. Cassiar South 1998
5. South Yukon
6. Mexico

Cassiar North again moves up in the ranking to a level which over states the value of a relatively raw prospect. Cassiar South 1999 moves up as result of good metallurgy and strong infrastructure. Northern B. C. at number three is in a strong center position. The first three positions are separated by about 10%. which is largely reflected in average grade differences between them We are not convinced that this small marginal difference accurately represents the difference in opportunities, however. The Cassiar South 1998 sits about where it would be expected. The big surprise is South Yukon. The site specific metallurgical data for this deposit has completely overcome strong assay grades and re-ranked the property in a less attractive position

Applying the K2 factors and computing the RAI for each property and incorporating the site specific K1 factors should produce the desired result of providing a ranking that reflects both perceived risk and strong assays.

The RAI ranking of site specific K1 values results in:

Cassiar South 1999
Northern B.C.
Cassiar South 1998
Cassiar North
South Yukon
Mexico

Cassiar North has now moved down in rank which better reflects its prospect status and is essentially the same rank as South Yukon. Cassiar South 1998 when compared to Cassiar South 1999, illustrate how a modest exploration budget can change the confidence and effect the ranking or overall priority of determining where best to put further resources.

The RAI ranking has identified Cassiar South and North B.C. as the two best opportunities from the list presented. Southern Cassiar rank, which is higher than North B.C. is largely a function of grade, the addition of two other metals and the potential size. Most other factors for these two deposits are quite similar.

Conclusions

Polymetallic mineral prospects can be objectively ranked or prioritized using a model that includes both quantitative and qualitative factors.
The application of the Risk Adjusted Index as a ranking tool allows the melding of quantitative and qualitative information.
Site specific adjusted factors can have a great influence on ranking.
Prospects or properties at different stages of development (i.e. with different levels of ore measure confidence) can be equitably compared and the risk level differences accommodated
by using the concept of the Risk Adjusted Index.
The K1 values can also be used as a part of property development or operational planning and analysis.

References:
COPE, L.W., 1998., Praning Ploymetallic Projects. Engineering and Mining Journal, June 1998, pp.34-35