Abstract

Economic returns for the copper industry have been assessed by building cash flow models for each of the 100 new mines brought into production over the past 20 years. Ninety-one of the 100 new mines returned the assumed 8% cost of capital and generated a positive net present value (NPV) at the development decision point. Economic criteria for individual mines are highly variable with NPV ranging from −$111 million (−$111M) to +$3850M (all money values are presented in United States dollars) and internal rate of return (IRR) ranging from −8% to +95%. The copper industry collectively would generate approximately $39 billion dollars in NPV through the development of the 100 new mines.

The returns to exploration ($174M per deposit) were measured by deducting the cost of exploration per deposit ($221M) from the returns to development per deposit ($395M). Overall, the copper business has provided positive returns to exploration. However, the variability in returns across deposits means that only 41 of the 100 new mines can carry the average cost of exploration. The long lead times and high costs associated with exploration result in only those deposits with large amounts of contained copper being able to cover the average finding cost of the industry.

The returns to development and exploration are shown to be highly sensitive to the cost of capital and metal price assumptions. With respect to deposit type, porphyry deposits are both larger (NPV) and exhibit lower profitability (IRR) than the nonporphyry deposits. On a geographic basis, returns to development and exploration are higher in Chile than elsewhere, reflecting the larger average deposit size and lower average exploration cost per deposit.

Sommaire

Les retours sur investissement ont été évalués par des modèles de valeur actualisée de flux de trésorerie pour chacune des 100 dernières mines mises en production au cours des dernières 20 années. Quatre-vingt onze des cent mines les plus récentes ont retourné les coûts en capital assumés de 8% et généré une valeur actualisée nette positive (VAN) au moment de la prise d’une décision de développer. Les critères économiques de ces mines individuelles sont hautement variables avec des VAN allant de −$111 million (−$111M) à +$3850M (toutes les valeurs monétaires sont présentées en dollars Américains) et les taux de retour internes (TRI) vont de −8% à +95%. L’industrie du cuivre générerait collectivement environ $39 milliards de dollars en VAN par le développement de 100 nouvelles mines.

Les retours sur l’exploration ($174M par gisement) ont été calculés en déduisant le coût de l’exploration par gisement ($221M) des retours sur le développement par gisement ($395M). L’industrie du cuivre a généralement généré des retours sur l’exploration positifs. La variabilité des retours d’un gisement à l’autre n’a toutefois permis qu’à 41 des 100 nouvelles mines de soutenir les coûts moyens d’exploration. La longueur des délais de mise en production et les coûts d’exploration élevés font que seuls les gisements contenant une grande quantité de cuivre peuvent soutenir les coûts moyens encourus par l’industrie pour leur découverte.

Les retours sur le développement et l’exploration s’avèrent être très sensibles au coût du capital et aux assomptions sur le prix des métaux. En ce qui a trait aux types de gisement, les gîtes de type porphyrique présentent à la fois une plus grande taille (VAN) et une profitabilité moindre (TRI) que les autres types de gisement. D’un point de vue géographique, le retour sur le développement et l’exploration est plus grand au Chili qu’ailleurs, ce qui découle de la taille moyenne des gisements qui y est plus grande et des coûts moyens d’exploration par gisement qui sont plus faibles.

Introduction

The analysis of returns to copper development and exploration presented here is an update and extension of earlier work carried out by the same authors (Leveille and Doggett, 2006), and continues the research of mineral industry costs and returns that has been ongoing for more than two centuries.

Smith (1776) was the first to express the returns to exploration and mine development in the context of the high cost of failure spread across the few successful mine developments. Many of the classical and neo-classical economic treatises published over the past two centuries have touched on this subject to some degree. For a summary of this work, the interested reader is referred to Robinson (1981, 1989). Mackenzie and Bilodeau (1979) assessed exploration costs and returns in the framework of the mineral supply process whereby unknown deposits are converted to saleable mineral commodities.

A large body of work published by Mackenzie and various co-authors captured the costs, risks, and returns of mineral exploration. Mackenzie and Woodall (1987) developed a methodology and compared economic returns to base metal exploration in Australia and Canada. Cranstone (1987) and Mackenzie and Doggett (1992) assessed trends in gold exploration, and Mackenzie et al. (1997a, 1997b) determined returns to mine development and exploration for a range of commodities in Australia and Chile, respectively. Blain (1992), Doggett (1994), and Horn (2002) assessed the changing effectiveness of exploration over time.

Numerous authors have addressed the issue of exploration success from the perspective of how to improve the odds of discovery using both qualitative and quantitative case studies. Approaches range from the discussion of luck in exploration (Bailly, 1979; Parry, 1998) to assessment of strategic planning and corporate structure (Dickerson, 1978; Frost, 1980; Woodall, 1984, 1993; Dow 1998; Fardon, 1998; Lowell 1998; Mercer, 2004). Broader discussions of decision making in the minerals industry can be found in Herfindahl (1955), Harris (1990), and Singer and Kouda (1999). Commodity specific assessments of return to exploration and development are presented for copper (Cabello, 2004) and gold (Schodde, 2004). The importance of the exceptional mineral deposits in the overall returns to the industry has been addressed by Singer (1995), Sillitoe (2000), and Schodde and Hronsky (2006).

The objective of this paper is to extend the empirical analysis of the costs and returns of the copper industry to cover the most recent years of extraordinary investment, mine development, and metal price volatility. All new copper mines (with the exception of those in China and Russia) brought on stream during 1989–2008 have been assessed to determine the average and total returns to the industry. These returns to mine development are subsequently compared with the costs of exploration to determine the long-term return for the copper industry. The analysis is extended to consider a number of variants from the base case assumptions. Returns to development and exploration are assessed using a range of copper prices and discount rates. Finally, costs and returns are assessed for subsets of the database by focusing on deposit types (porphyry and others) and geographic variants (Chile and other).

Evaluation Database and Methodology

The database for evaluation includes all significant copper deposits (with the exception of Chinese and Russian deposits) that were developed in the study period 1989–2008, including those approved for development by the end of 2008. For multimetal deposits, only those for which copper is the largest contributor to revenue have been included in the database. Based on discussions with the engineering group at Freeport McMoRan, operating costs during the last year of production have been projected ahead for future production at a rising rate of $0.01 per pound of copper per year. Deposit tonnages and grades reflect feasibility study values, and annual capacity reflects actual past and projected future production.

All deposits included in the study are listed alphabetically in Table 1 along with their location, deposit type, processing method, and date of development decision. Data on the individual deposits, including technical and cost information, have been compiled from public sources such as corporate annual reports, press releases, and newswire stories, and reflect the best available information as of the end of 2008.

Metal prices used in the study reflect the average values for each metal over the 20-year study period. These values have been adjusted for inflation and converted to real 2008 United States dollars. Table 2 shows the commodities used in the economic modeling, with both the nominal and real 2008 dollar average prices. By-product metals are converted to copper equivalencies on the basis of relative prices and metallurgical recoveries. For sensitivity analysis, two other sets of prices have been tabulated reflecting the average prices over both the last 10 years and the last 5 years of the study period. The cost of capital used for discounting cash flows in the base case assessment is 8%. Sensitivity analysis allows for an increase to 12% and a decrease to 6% in this rate.

Exploration expenditures for copper have been published yearly since 1992 by the Metals Economics Group (MEG; Metals Economics Group, 1992–2008–2002). Estimates for breakdown of these expenditures into grassroots and brownfields expenditures as well as geographic region have also been provided by MEG. Expenditures prior to 1992 have been extrapolated based on base metal expenditures in Canada (Statistics Canada), Australia (Australian Bureau of Statistics), and Chile (a range of public and private sources compiled and published in Mackenzie et al., 1997b). All expenditures have been converted to 2008 US dollars using the Consumer Price Index as compiled by the U.S. Department of Labor, Bureau of Labor Statistics (http://stats.bls.gov/cpi/home.htm#data).

Assumptions and definitions used in the calculation of discovery costs per deposit, and subsequently for the determination of returns to exploration, have been described in Leveille and Doggett (2006). For completeness and to capture minor changes, the methodology is also described here in the following eight-step process:

  1. Copper exploration expenditures need to be split into greenfield and brownfield categories. For the purposes of this work, a greenfield discovery is either: (1) a previously unknown deposit with discovery date assigned on the basis of the first drillhole intersecting mineralization across minable widths; or (2) a minimum two times increase in size of a known deposit on the basis of newly discovered resources, which effectively represents a new discovery rather than simply brownfield extensions.

  2. Greenfield discovery cost includes all exploration expenses up through and including a feasibility study, such as is necessary to estimate proven and probable minable reserves.

  3. Brownfield exploration is assumed to relate to deposits already in production and is ignored here.

  4. For the purpose of this study, a discovery is defined as a deposit that was explored to the point of having a publicly announced reserve.

  5. Based on MEG data, 82% of total expenditures are estimated to relate to greenfield projects, with the remaining 18% dedicated to direct brownfield exploration. Thus, during the 1989–2008 period, greenfield exploration expenditures totaled $15.47 billion, and brownfield, $3.28 billion. Within this overall breakdown, it is apparent from MEG data that brownfield expenditure has been increasing as a percentage of overall expenditures in recent years.

  6. We have assumed that some exploration expenditures can be expensed for tax purposes. Based on the 35% effective rate of tax applied in the determination of returns from development, the after-tax cost of exploration would effectively be 65% of the before-tax cost for expenditures that could be expensed. Exploration expenditures of junior companies, which are initially capitalized and infrequently expensed, are not adjusted for tax purposes. Assuming that 70% of the expenditures would be expensed and 30% not expensed, the weighted average effective after-tax cost of exploration would be 75.5% of the before-tax cost. Therefore, the after-tax greenfields exploration expenditure assumed in this study is: $15.47 billion × 0.755 = $11.68 billion.

  7. Some exploration expenditure prior to 1989 is related to deposits developed since 1989, suggesting that we may be under-counting exploration costs per discovery. On the other hand, some expenditures toward the end of the period will be related to discoveries that are not yet included in our data set, effectively over-counting the exploration cost per discovery. We have assumed that these two inaccuracies effectively cancel each other out.

  8. Based on sample deposits in our database, the median time for a discovery to be made and brought to the stage of positive feasibility is 16 years, and the mean is 22 years. We have chosen to use the median, because the mean is highly skewed by two deposits whose time lapse from discovery to development was 106 and 114 years, respectively. The 16-year time period includes 12 years to move from grassroots to prefeasibility, and a further 4 years to allow for pre-easibility, feasibility, and permitting in advance of project development. To simplify the analysis, expenditures are assumed to be spread evenly over the 16 years, and brought forward using the range of selected discount rates to the beginning of the development phase, at which point they can be subtracted from the returns to development. Although this would not be appropriate for individual deposits where expenditures are higher as you get closer to a development decision, on the scale of a mining company and of the mining industry, exploration expenditures, at any time, reflect a mixture of generative programs, initial drill tests, delineation, and feasibility work. It is important to note that the discovery/development time lapse for many deposits also reflects long periods of “warehousing” of projects by various owners, including governments, because of political changes, lack of infrastructure, low metals prices, mineralogy not amenable to then-current processing technology, etc.

Tonnage and Grade Characteristics

Summary physical characteristics of the 100 new copper mines developed from 1989 to 2008 are captured in Table 3, which shows ore reserves, copper equivalent grade, and contained copper metal. The size and grade characteristics reflect the range of deposit types from large lower-grade deposits to small higher-grade deposits. The copper equivalent grade ranges from 0.29% to more than 12% across the set of all 100 deposits. The median copper equivalent grade (1.42%) is significantly below the mean grade of 2.35%. This reflects the lower-grade nature of the larger deposits, which are predominantly open pit mines operating with the benefit of economies of scale and correspondingly lower cutoff grades. Weighting the grades by their respective tonnages results in a lowering of the mean to 1.04% Cu equivalent.

With respect to deposit size, a large range is evident whether measured on the basis of tonnes of ore reserves or tonnes of contained copper. For all deposits, ore reserve size varies from about 1 Mt to 2.4 Gt, around a mean value of 220 Mt and a median value of 72 Mt. Contained copper equivalent metal values range from less than 0.1 Mt to more than 40 Mt. The mean value is 2.3 Mt, whereas the median value is considerably smaller at 0.9 Mt. In aggregate, the 100 deposits contain 229 Mt of copper equivalent metal.

Two subsets of the overall database have also been examined, with results shown in Table 3. The deposit type variant shows the impact of evaluating porphyry deposits separately from other deposit types. Not surprisingly, the physical characteristics of porphyry deposits indicate that they are larger and lower grade than the nonporphyry deposits. The mean and median copper equivalent grade for the 40 porphyry deposits in the dataset is 0.91% and 0.73%, respectively. In this case, the weighted average grade (0.89% Cu) is very similar to the arithmetic mean (0.91% Cu), reflecting the smaller range of grade values across the set of porphyry deposits as compared with the mix of other deposit types. For the 60 nonporphyry deposits, the mean and median grades are 3.31% and 1.88%, respectively. The weighted mean grade (1.72%) is much closer to the median value than to the mean, suggesting that the very high grade deposits are smaller on average.

With respect to size, the mean reserve size (451 Mt) for porphyry deposits is more than six times the corresponding value for nonporphyry deposits. Similarly, the mean and median contained copper values (4.0 Mt and 1.7 Mt, respectively) for porphyries are significantly higher than for nonporphyry deposits (1.1 Mt and 0.6 Mt, respectively).

The second subset considered is a geographic variant based on location of deposits. Because of their important contribution to the global copper industry, Chilean deposits are examined separately from and compared with deposits from the rest of the world. During the study time frame, Chile accounted for the largest number of new copper developments, with 29 mines developed. This compares with 17 deposits in Australia, the country with the second largest number of new developments.

Furthermore, as shown in Table 3, the new developments in Chile accounted for 39% of the total tonnes of equivalent copper in reserves of all new developments. On average, the deposits developed in Chile were about twice as large, as measured by tonnes of ore reserves, but with mean grades less than half those of non-Chilean deposits. On average, the contained copper metal in Chilean deposits (3.1 Mt) was more than four times that of deposits in the rest of the world. These geological characteristics largely reflect the relative contribution of porphyry deposits to new developments in Chile versus the rest of the world.

Economic Return Characteristics

Returns to Development

Based on expected copper prices and cash flow models for each of the 100 new mines, discounted cash flow criteria were calculated for each deposit and for the set of deposits. Table 4 displays the statistical summary of findings highlighting the IRR and NPV at discount rates of 6%, 8%, and 12%. In the base case, IRR values of greater than the 8% hurdle rate are required for a deposit to be considered economic to develop. Across all 100 deposits, the IRR varies from −8% to +95%, around mean and median values of 25% and 21%, respectively. Ninety-one of the 100 deposits would be deemed economic by virtue of meeting the 8% hurdle rate. NPV also varies considerably across the set of deposits, with a high of nearly $4000 M to a low of less than −$100 M, around an average value of $395 M and a median value of $160 M. With the mean value being nearly 2.5 times the median value, it is apparent that the distribution of NPVs includes a few very large values. This is evidenced by the 90th percentile value of $869 M. Collectively, the total NPV at 8% generated by all new projects is $39.4 billion.

The corresponding discounted cash flow statistics increase using a 6% discount rate, with total NPV rising to $55.6 billion with a mean of $556 M. Increasing the cost of capital to 12% results in a reduction of the NPV by more than 50% from the base case values, with a total of $18.5 billion and mean and median values of $185 M and $57 M, respectively.

Analysis of deposit type variants reveals that the 40 porphyry deposits in the dataset are more than twice as large as nonporphyry deposits, as measured by NPV. For example, the mean NPV for the porphyries is $642 M, compared to $230 M for nonporphyries. Under base case conditions, porphyries account for 66% of overall NPV. The trade-off for the larger size is lower profitability for the porphyry deposits. The mean IRR for porphyries is 20% as compared with a mean of 28% for nonporphyry deposits.

The economic returns to development for Chile and rest-of-the-world deposits are also shown in Table 4. The total NPV for Chilean deposits is more than $18 billion, whereas the corresponding value for the rest of the world is $21.3 billion. Thus, Chile accounted for 29% of new developments, but 46% of overall value generated. This relationship is mirrored by the mean values, which show that a new development in Chile generated two times the NPV of a rest-of-the-world deposit ($629 M vs. $300 M). From a profitability perspective, the large Chilean deposits on average generated a lower IRR (21%) versus rest-of-the-world deposits (27%). This reflects the longer development lead times and levels of upfront capital expenditures required for development of large-scale mines. A closer examination of the IRR distribution for Chilean deposits reveals that 20% of the deposits would not reach the 12% hurdle rate, and 38% would not reach a 15% threshold. These hurdle rates are commonly used as the minimum profitability levels for positive investment decisions (Bhappu and Guzman, 1995).

The empirical results discussed above are based on evaluation of all deposits developed during the 1989–2008 period using the average metal prices over that timeframe. Two economic variants on these base case results are shown at the bottom of Table 4. The first variant, considers the economic criteria of the deposits that would generate a positive return to development. The number of deposits that pass the economic test varies with the cost-of-capital assumption. Under base case conditions using the 8% cost of capital, 91 of the 100 mines would generate a positive NPV. If the 9 deposits that generated negative returns are dropped, higher total, mean, and median values are calculated for the remaining 91 deposits as compared with the overall data set. This impact is reflected in the mean NPV, which increases by $45 million. When the discount rate is increased to 12%, only 80 of the 100 deposits generate positive returns to development (at 15% this number decreases to 71 deposits). The IRR (30%) of these 80 deposits, however, is higher than for the entire data set, because the less profitable deposits are screened out.

The other economic variant assessed relates to metal price assumptions. Rather than use the 20-year average prices, economic criteria have been recalculated using 10-year and 5-year average prices. Economic criteria are shown to improve significantly with the higher average prices over ten years, and especially over five years. The average NPV is shown to increase from the base case value of $395 million to $452 million using 10-year average prices, and to $1133 million using the significantly higher 5-year average prices. Similarly, the IRR increases from 25% in the base case, to 27% and 47% using 10-year and 5-year prices, respectively. The number of deposits that would generate positive returns to development increases from 91 using base case prices, to 98 at the higher 2004–2008 prices.

Returns to Exploration

Returns to exploration are calculated by subtracting the average discovery cost per deposit from the average return to development per deposit. As shown in Table 5, greenfields exploration expenditures over the 20-year study totaled $15.47 billion. When adjusted for tax credits associated with expensing exploration costs, the total after-tax exploration cost associated with discovery and delineation of the 100 new mines is 75.5% of the before-tax value, or $11.68 billion. Spreading this total across the 100 new developments results in a discovery cost per deposit of $117 million. If no allowance were made for the time associated with this expenditure, we could determine the return to exploration simply by subtracting this cost from the average return to development. Although it has been suggested that this method of determining exploration costs is appropriate in some circumstances (see Newendorp, 1975), the high-risk nature of exploration and the long time lines associated with moving exploration to production indicate that some type of discount factor should be applied to the exploration expenditure series.

A variety of methods can be employed to relate the time and cost of exploration to the successful returns from exploration (see Davis and Samis, 2006), but we have taken the straightforward approach of spreading total exploration costs across the average time period from discovery to production and discounting the series to a future value at the beginning of mine development. Using this approach, the average cost of discovery per new mine development increases to $221 M at an 8% discount rate. Deducting this cost from the average return to development ($395 M) leaves an average return to exploration of $174 M per developed deposit. From the perspective of the individual deposits, only 41 of the 100 new mines would remain economic after deduction of the average cost of exploration. Thus, the median return to exploration as shown in Table 6 is highly negative at −$63 M. Using a discount rate of 12% results in strongly negative mean and median returns to exploration, with only 13 deposits able to cover the mean exploration cost of $312 M. Statistical values for the mean IRR are not shown because it is not mathematically possible to determine an IRR for some of the deposits under these very uneconomic conditions.

Because we did not have a breakdown of exploration expenditures by deposit type, results for porphyries and nonporphyries have not been determined. On the basis of geographic location, however, we are able to separate exploration expenditures for Chile and the rest of the world and prorate the total amount of greenfields exploration expenditures across the new mine developments in the two jurisdictions. As shown in Table 5, exploration expenditure for greenfield exploration in the rest of the world ($13.61 billion) is approximately 7 times that in Chile ($1.86 billion). On an after-tax basis and spread across the new mine developments, the average discovery cost per deposit is $48 million in Chile and $145 million in the rest of world. As shown at the bottom of Table 5, applying a range of discount factors results in costs ranging from $80 M to $139 M per deposit in Chile, and $232 M to $387 M per deposit in the rest of the world.

The returns to exploration on a geographic basis can now be determined by deducting discounted discovery costs from the NPV determined as the return to development. The results of this process are displayed in Table 6. For Chilean deposits, the average returns to exploration are strongly positive at the 8% discount rate, with an NPV of $530 M per deposit. As the discount rate increases to 12%, the average return to exploration decreases to $160 M and the median value becomes negative at −$93 M. The distribution of IRR indicates that only 10 of the 29 new mine developments would generate an IRR on exploration of 12% or above. The situation for rest-of-the-world deposits is considerably worse than that for Chile. On average, the return to exploration is only $11 M per deposit at the 8% cost of capital and becomes negative at the 12% discount rate, whereas the median value is strongly negative at all of the discount rates shown. In total, only 20 non-Chilean deposits (28%) generated overall positive returns to exploration at the 8% base case cost of capital.

Economic variants considered include assessing the returns to just the deposits that can cover the cost of exploration, and assessing the returns to exploration using the higher average prices for 1999–2008 and 2004–2008. As shown at the bottom of Table 6, the number of deposits that can cover the cost of exploration varies from 54 to 13 as the cost of capital is increased from 6% to 12%. At the 6% rate, the mean NPV of the 54 deposits that can cover the average cost of exploration is $795 M. This value is significantly higher than the value of $655 M determined by applying the base case 8% cost of capital to the 41 deposits covering the average cost of exploration. The mean IRR of the 54 deposits that are economic at the lower discount rate is lower than for the base case because more marginal deposits have been added. When the 12% discount rate is applied, only 13 of the 100 deposits generate sufficient IRR to be deemed economic if the cost of exploration is included. This set of 13 deposits represents the best of the lot as evidenced by a mean NPV of $863 M and a mean IRR of 18%.

When the higher average prices exhibited during the last 10 and last 5 years of the study period are substituted for the base case prices, the returns to exploration look significantly better — especially for the latter scenario. Using the 2004–2008 average prices, the return to exploration as measured by the mean NPV is strongly positive at all three rates of discount, with values ranging from $873 M to $1663 M.

Relating Economic and Physical Characteristics of New Mine Developments

Tonnage–grade characteristics of over 1000 deposits comprising the Freeport McMoRan Exploration global copper database are presented in Figure 1. The 100 new mine developments assessed in this study are highlighted for comparative purposes. Furthermore, those 80 new mine developments that generated positive returns to development using a 12% discount are indicated by the light grey diamonds. We are focusing on the deposits that meet the 12% hurdle rate because we feel that this is a realistic marker for positive investment decisions at the mine development stage. The 80 mines are widely distributed across the tonnage–grade plot shown in Figure 1. Only 3 deposits (4%) have total contained copper of more than 10 Mt, whereas 7 (9%) have less than 0.1 Mt of contained copper. This compares with the overall data set where 3% and 38% of deposits have greater than 10 Mt and less than 0.1 Mt of copper, respectively. The solid line added to the figure is an empirical boundary separating the tonnage grade combinations that allow for a 12% return on investment for new mine developments from those that do not. As shown, the minimum grade required for meeting the 12% economic hurdle rate decreases as the tonnage increases. This reflects the economies of scale in developing mines with higher capacities and lower unit costs.

When the minimum size and grade combinations are reconsidered to allow for the cost of exploration, a different pattern emerges (Fig. 1). As shown by the deposits highlighted by the dark diamonds, only two deposits with less than 1 Mt of contained copper cover the full discounted cost of exploration. Overall, the 13 deposits that could cover the average cost of exploration are the largest deposits in the data set, with an average reserve size of 612 Mt and average contained Cu equivalent metal of 8.9 Mt. The new minimum tonnage grade curve, shown as the dashed line in Figure 1, represents the minimum tonnage–grade combinations that would generate a 12% rate of return on exploration in the copper business. The rightward shift of the curve from the minimum tonnage–grade conditions to meet economic hurdles at development (solid line) to the minimum conditions including exploration (dashed line) visually captures the cost of exploration, as highlighted in Tables 4 to 6.

Conclusions

At the mine-development decision point where exploration is considered as a sunk cost, the returns to the copper industry over the past 20 years have been substantial, as reflected by the NPV at 8% of more than $39 billion for the 100 new mine developments. However, the variability of returns to development is significant, with a few exceptional deposits generating the vast majority of total returns to the industry. At an 8% hurdle rate, 4 deposits account for 74% of the total returns generated by the 100 developed deposits. Thus, most of the returns go to a few highly successful explorers and developers.

From a corporate and industry perspective, economic returns must include the real costs associated with finding, delineating, and assessing the economic merits of prospective copper deposits. The long lead times and high discovery risk associated with copper exploration result in only the very best deposits being able to cover the average discounted cost of exploration. Exploration success in the long run will reflect the ability to maximize returns to development while minimizing the time and cost of discovery and delineation. Shortening the time from discovery to development decreases the discounted exploration cost, and will have a positive impact on total returns to a project, although time lines are not completely in the control of the explorer/miner in today’s regulatory environment in many jurisdictions.

From a strategic perspective, exploration success is a combination of what you explore for, where you choose to do it, and how you go about it. Based on the results of this study, exploring for and developing porphyry copper deposits in Chile was the most successful strategy from 1989 to 2008. The greenfield discovery cost per deposit was lower and the average return to development was higher in Chile than elsewhere during this time frame. This combination evidently reflects the prospectivity and findability (endowment, depth, exploration methods employed) of deposits developed in Chile during this time, as well as the country’s political, legal, fiscal, and regulatory advantages. Given that discovery is “sampling without replacement,” how long Chile’s preeminent position will be maintained is an open question.

Although it is not possible to determine exploration costs on the basis of deposit type, it is clear from this analysis that the chances of covering the high costs of exploration are much better when the target is large (in terms of contained metal). To the extent that the odds of porphyry copper deposits being large are much better than are those for other deposit types, porphyry deposits become the target of choice for an exploration program designed for a higher probability of success.

In the 20-year study period, more than $15 billion has been spent on copper exploration worldwide. The cumulative impact of this expenditure on the assessment of prospective terrains for hosting large copper deposits would suggest that the cost of exploration is likely to become even higher in the future. Two important implications of this eventuality are:

  1. The necessity to be better than average in terms of exploration costs and time commitments becomes absolutely critical to success.

  2. The higher average prices recorded in more recent years of the study will need to be maintained if returns to development are to be sufficiently high to offset increasing exploration costs and sustain the growth of the copper industry.