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Mineral Occurrence Revenue

Estimation &Visualization Tool

Presented by: Michael Billmire, Research Scientist, MTRI

Colin Brooks, MTRIPaul Metz, UAF

Robert Shuchman, MTRIHelen Kourous-Harrigan, MTRI

MOREV Tool

www.mtri.orgwww.mtri.org/mineraloccurrence.html

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Who We Are

Michigan Tech Research Institute (MTRI)– Satellite research institute based in Ann Arbor, MI, owned and

administered by Tech since 2006

– ~40 full-time employees

– Our specialties are signal processing and remote sensing, though we do research in a wide variety of areas:

• National and Homeland Security

• Water remote sensing (Great Lakes focus)

• Fire emissions and carbon cycle modeling

• Wetland characterization

• Farming practices

• Transportation

• Biomedical sensing

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What is the MOREV Tool?

An ArcMap-based suite of tools developed in order to effectively assess and communicate the value of mineral occurrences within regions of interest in Alaska, Yukon Territory, and British Columbia

Primary purpose: spatialized mineral occurrence valuation

Auxiliary functions:– Pre-feasibility economic assessments of individual mineral occurrences

– Ability to modify commodity prices, or use the most recently available commodity prices

– Intermodal (road, rail, water) transportation network for optimal routing• e.g., how much does it cost to ship the ore to a processing facility in China?

– Carbon emissions accounting from transportation

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Potential Users

Small to midsized exploration interests in pre-feasibility stages of project planning for new mining projects

Transportation & infrastructure planners– State & local government

Potential for helping in permitting process– Example: Preparation of NI 43-101 mineral project

disclosures in Canada

Government agencies & resource database maintainers

Investment community & lenders

Researchers (geological, transportation, economic, etc.)

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Origins

Alaska-Canada rail link• Phase 1 Feasibility Study completed in 2007

$10.5 billion projected cost

Dr. Metz argues that the report grossly underestimated the potential economic benefits in terms of increased accessibility to mineral occurrences

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Origins

Key points:

New rail extensions mean increased accessibility to undeveloped mineral occurrences in Alaska and Canada

There was a need for a tool to adequately assess and communicate value of mineral occurrences within a region

• Dr. Paul Metz, Tech alum and Professor in the Dept. of Mining & Geological Engineering of the University of Alaska Fairbanks developed methods for estimating potential revenue from a mineral occurrence, and had produced a database in spreadsheet form. He wanted us to integrate this into a GIS that would:

• (a) Help to summarize and visualize mineral value, and thus help to communicate to the powers-that-be in AK how much revenue potential there is mineral resource development to bolster support to proposed public works projects

• (b) Perform dynamic, on-the-fly, user-customizable calculations of revenue and cost estimates for prefeasibility economic assessments for mineral developers

Background

• In the developmental process, the tool evolved to support and integrate additional functionality:

• Commodity price database• Average price of commodity for each year going back to 2000• pyCommodity – script that scours web sources for current commodity prices

• Optimal routing module for intermodal transportation network• Utilizes ORNL intermodal transportation network with ESRI’s Network Analysis

algorithms• Supplemented with mode-specific freight costs for four transportation modes• Chooses route from mineral occurrence to destination point (port/city/processing

facility) that minimizes freight cost w/ optional way-point forcing and mode restrictions

• Transportation carbon accounting module (TCAM)• Developed/adapted mode-specific equations to calculate carbon emissions based on

distance travelled, vehicle weight, and fuel efficiency data

Background

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Overview

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Mineral occurrence value estimation

Mineral occurrence development cost

estimation

Individual mineral occurrence pre-feasibility

economic assessmentmodule

Region-of-interest multiple mineral

occurrence valuation assessment module

Commodity price database

Get current commodity pricesIntermodal

optimal routing module

Transportation carbon accounting

module (TCAM)

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Overview

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estimation









module (TCAM)


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Assessing mineral occurrence value

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The MOREV tool’s mineral occurrence database combines existing data files specific to each region:

• Alaska Resource Data File (ARDF) (~7000 occ.)• Yukon MINFILE (~3000 occ.)• British Columbia MINFILE (~12,000 occ.)

With Dr. Metz’s guidance, we assigned USGS Deposit Models to ~10,000 of them

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• Dr. Metz developed a methodology for estimating revenue potential of mineral resources based primarily on USGS Deposit Model type

• There are ~75 USGS Deposit Models that describe any and all known historically developed mineral occurrences

• Each USGS Deposit Model provides this information:• Descriptive name (e.g., Low-sulfide Au-quartz veins)

• Commodities included (e.g., gold, silver)

• Tonnage and commodity grade curves, based on all historic occurrences of that type, from which we derive 10th, 50th, and 90th percentile values

• E.g., a 10th percentile value of 1000mT and a gold grade of 1.2 g/mT means that 90% of historic deposits of this type contained at least that tonnage and a grade at least that high

• In other words, 10th percentile is the lowest baseline of what to expect, 90th percentile is about the maximum of what to expect

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• Example tonnage and grade curves for USGS Deposit Model 8: low-sulfide Au-quartz veins:

• Using these models and commodity prices, plus a few other variables (mine/mill recovery rate, concentration factor), Dr. Metz calculated Gross Metal Value (GMV) for each USGS Deposit Model

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Overview

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estimation








module (TCAM)Commodity price

database

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Commodity prices fluctuate constantly, so it was important to provide information and functionality to view and modify prices

We provide the average yearly price of commodity going back to 2000

User can choose which price to use by double-clicking on the cell (or click on a column header to load all prices for a certain year) or input a custom price

Default price is the most recent non-zero yearly average

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Overview

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estimation





Intermodal optimal routing

module


module (TCAM)

Get current commodity prices

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PyCommodity

Python module developed specifically for this project

The script scours online sources of commodity prices (infomine.com, metalprices.com, steelonthenet.com, tradingcharts.com) to find the most recent values

Using this, we are able to get current prices for 15 of the 27 commodities used in the deposit models

Antimony, cobalt, copper, gold, iridium, lead, molybdenum, nickel, palladium, platinum, rhodium, silver, tin, uranium, zinc

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Overview

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estimation


module


module (TCAM)



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Multiple mineral occurrence valuation assessment module

Potential users: Transportation & infrastructure planners (state, local, federal governments)

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Workflow:

1. Select a multiple occurrences using any of ArcMap’s selection tools (left)

2. Click on the correct icon!

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Multiple mineral occurrence valuation assessment module

Provides a summary, using default values for parameters, based on USGS Deposit Model of the selected set of occurrences

In this case, we use estimated-GMV, (GMV * Probability of Development) because we assume that a very small percentage of occurrences will actually ever be developed

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Overview

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estimation




module


module (TCAM)



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Individual mineral occurrence pre-feasibility economic assessment module

Potential users: small to midsized exploration interests in pre-feasibility stages of project planning for new mining projects

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Workflow:

1. Select a single occurrence (left)

2. Click on the correct icon!

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While most mineral occurrences are assigned an appropriate USGS Deposit Model, expert users or those with auxiliary information regarding a specific site are able to view and modify the deposit model: tonnages, commodities, and grades:

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Overview

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module


module (TCAM)


estimation

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Capital and operating costs

– Formula based on reserve tonnage, production rate, and mine type (Camm 1991)

– Plans to update to a more robust costing algorithm (SEE)

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Cost Estimation Methodology:Capital and operating costs

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Cost Estimation Methodology:Transportation

How much would it cost to ship the ore from the mine to a processing facility in China or Manitoba?

Freight volume is estimated from concentrate tonnage and distance traveled for each of four transportation modes:

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Cost Estimation Methodology:Transportation

Costs per revenue-tonne kilometer are user-changeable; default values are based on U.S. freight cost figures

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estimation






Overview

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module (TCAM)


module

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Intermodal Optimal Routing Module

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Intermodal Optimal Routing Module

A route is calculated from mineral occurrence origin to destination point (port, cities, or facilities in U.S., Canada or overseas)

Most cost efficient route is automatically chosen, but user can force route through waypoints

Like Google/Yahoo Maps! Except intermodal.

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Intermodal Optimal Routing ModuleUsers can choose origins & destinations

After a route is successfully calculated, you can review, export the route to SHP/KML, and save the values, which are loaded directly into the costing calculations

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Route KML in Google Earth

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module


estimation






Overview

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module (TCAM)

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Carbon AccountingTransportation Carbon Accounting Module (TCAM)

Carbon AccountingTransportation Carbon Accounting Module (TCAM)

Rail, truck, barge, and OGV (ocean going vessel) emissions models are incorporated

Mode-specific calculator forms show model assumptions and allow user-modification of default parameters

Uses CO2 equivalent values (includes:CO2, CH4, and N2O)

Sources for fuel consumption/emissions model data:

– Rail: Association of American Railroads, US EPA

– Truck: USDOT Federal Highway Administration, Vehicle Inventory and Use Survey (VIUS) 2002, US EPA

– Water: MAN Diesel, European Environment Agency, US EPA, ICF International, Lloyd’s Register

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TCAM Equations & Data SourcesOverview

RailBased on US freight fleet-wide fuel economy as reported by American Association of Railroads

RoadFuel economy regression equation based on total vehicle weight derived from US DOT VIUS and FHA Highway Statistics.

WaterMethodology adopted from ICF/EPA port emission inventory best practices. Utilizes emission factors based on engine power output (g/kWh) instead of fuel consumption. Data sources include: ICF Consulting, US EPA, Swedish Methodology for Environmental Data, Lloyd’s Register, MAN Diesel.

TCAM Equations & Data SourcesRail

Total Rail CO2 (kg) = F * R * C

Where:

F = Revenue tonne-kilometers of freight: distance(km) * tonnes of freight, both figures being derived from the user-defined scenario

R = Fuel consumption rate (L diesel/tonne-km): default value = 0.005946, following American Association of Railroads (AAR) Railroad Facts 2008 (p. 40), which provides the following fleet-wide average: 436 revenue-ton-miles / gallon fuel consumed for 2007. This figure was converted to L/tonne-km using the following equation:

L/tonne-km = 1 / (436 * 0.264 gallons/liter * 1.609 km/mile * 0.907 tonnes/ton)

C = CO2/L of diesel (kg); default value = 2.6681, according to US EPA

http://www.epa.gov/oms/climate/420f05001.htm

TCAM Equations & Data SourcesRoad

Total Road CO2 (kg)= F * R * C / W

Where:

F = Revenue tonne-kilometers of freight: distance(km) * tonnes of freight, both figures being derived from the user-defined scenario

R = Fuel consumption rate (L diesel/km, or 1/e where e is fuel economy). Fuel economy is based on total vehicle weight. Data on vehicle weight from the US Department of Commerce Bureau of the Census 2002 Vehicle Inventory and Use Survey and the US DOT Federal Highway Administration Highway Statistics 2007 (for Class 8 combination trucks) was used to derive a regression equation to calculate fuel economy from combined vehicle and cargo weight (converted to metric units afterwards):

miles-per-gallon= 772.04 * w-0.463 , where w = total vehicle weight (lbs.), r2 = 0.9605

C = CO2/L of diesel; default value = 2.6681, according to US EPA

W = Total vehicle weight (tonnes), defined here as equal to curb weight (weight of empty vehicle) plus freight tonnage. Curb weight values for each truck Class are derived from the FHA’s Development of Truck Payload Equivalent Factor (TPEF)

0 10000 20000 30000 40000 50000 6000002468

101214161820

Vehicle weight (lbs.)

Mile

s per

gal

lon

http://www.epa.gov/oms/climate/420f05001.htm

http://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf2_reports/reports9/s501_2_3_tables.htm

TCAM Equations & Data SourcesWater Freight

Total Water CO2 (kg) = ∑t(∑m (Hm,v * Lm,t,v * Pt,v * Nt,v * Em,t)) for vessel type v

Where:

t = engine type (2 total) (propulsion/main, auxiliary)

m = activity mode (4 total) (cruise, reduced-speed-zone (RSZ), maneuvering, hotelling)

v = vessel type (8 options) (auto carrier, bulk carrier, container ship, cruise ship, general cargo, RORO, reefer, tanker)

H = average or expected amount of time (hrs) a vessel of type v spends in activity mode m. Default values: hotelling = 40, maneuvering = 1, RSZ = 2. Values for cruise activity mode are automatically calculated from scenario-derived distance (km), and average cruise speed for a vessel of type v. Sources: Thesing and Edwards 2006, Lloyd’s Register, ICF/EPA 2006

L = loading factor (percent). The percentage of the maximum continuous rating (MCR) used by engine type t in mode m for vessel type v. Source: US EPA Analysis of Marine Vessel Emissions and Fuel Consumption Data

P = Maximum Continuous Rating (MCR) for engine type t in kW. Auxiliary engine power is based on ICF/EPA fleet averages.

Main engine power is derived from ship domestic weight tonnage (DWT)and vessel type v based on the following EPA regression equationand table:

Main engine power (kW) = (a * DWT) + b

N = number of engines of type t, which varies by vessel type v. Generally, N =1 for main engines, and N < 6 for auxiliary. Source: ICF/EPA 2006: Current Methodologies and Best Practices for Preparing Port Emission Inventories

E = CO2 equivalent emissions rate in grams per kilowatt hour (g/kWh), specific to m and t. Source: SMED Methodology for Calculating Emissions from Ships

Vessel Type a b r2

Auto Carrier 0.4172 7602 0.17

Bulk Carrier 0.0985 6726 0.55

Container Ship 0.8000 -749.4 0.59

Cruise Ship 6.810 -4877 0.72

General Cargo 0.2880 3046 0.56

RORO 0.5264 4358 0.76

Reefer 1.007 1364 0.58

Tanker 0.1083 6579 0.66

http://www.epa.gov/ttn/chief/conference/ei15/session1/browning_pres.pdf


http://www.epa.gov/oms/models/nonrdmdl/c-marine/r00002.pdf


http://westcoastcollaborative.org/files/sector-marine/SMED%20Methodology%20for%20Calculating%20Emissions%20from%20Ships.pdf

TCAM Equations & Data SourcesWater Freight

Total Water CO2 (kg) = ∑t(∑m (Hm,v * Lm,t,v * Pt,v * Nt,v * Em,t)) for vessel type v

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Tool Help

We maintain an online Help page, accessed by double-clicking on any of the parameter labels within the tool forms

The page details all of the equations, assumptions, sources, and justification for default values used in the tool

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How to Access the MOREV Tool

Currently used internally as a Desktop tool within ArcMap, but have installed it on some lab computer at Tech.

Future plans to produce a stripped-down web-based version (sans optimal routing module), depending on funding.

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Example MOREV Tool Analyses:Alaska Pipeline Project

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We used the MOREV Tool to evaluate potential revenue from mineral occurrences within corridors of two separate pipeline routes

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Example MOREV Tool Analyses:Klondike Highway Freight Forecasts

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We provided estimated freight volumes from mineral occurrence development to the Alaska Department of Transportation (AKDOT) for their 20 year freight forecasts.

These forecasts are used to inform the toll rates required to pay for highway maintenance

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The Future

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Improve rail carbon calculator

Airships to the Arctic!– Integrate air-freight carbon calculator

Currently working with Pasi Lautula and Irfan Rasul to adapt the intermodal optimal routing module for analysis of Michigan mining and lumber logistics

Have submitted proposal to expand MOREV Arctic-wide: will require acquiring mineral occurrence data for Norway, Denmark, Russia, etc. and modifying shipping routes to account for seasonally varying ice cover.

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Key Points

Spatializing the mineral occurrence database allows integration of disparate data important to resource development & transportation decision makers, example uses:

Calculate potential revenue & freight volumes from occurrences within 100-km of a proposed transport link Visualize proximity to existing infrastructure, historic mines, nearby deposits Visualize land use patterns, watersheds, political boundaries Track CO2 in transportation segment for a proposed mine Calculate and visualize most efficient multi-modal transportation route.

Sensitivity analyses can be performed, for example:• Transportation costs with and without a new rail link

• Carbon impact of multimodal routing options (truck/rail/OGV)

Inputs and assumptions are transparent to and modifiable by the user• e.g. modal shift costs, carbon cost per ton-mile, port charges, mineral occurrence tonnage, costs

per ton-mile, commodity price, mine recovery rate, etc.

Occurrence data are updateable

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Contact Information

Paul Metz, Ph.D.

Professor, P.E., Geological EngineeringUniversity of Alaska [email protected] Phone: (907) 474-6749 http://www.alaska.edu/uaf/cem/ge/people/metz.xml

Michael Billmire

MTRI Research [email protected] Phone 734-913-6853

Colin Brooks

MTRI Research Scientist & Environmental Science Lab [email protected] Phone 734-913-6858Fax 734-913-6880

Michigan Technological University

www.mtri.orgwww.mtri.org/mineraloccurrence.html

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