Risk management in evaluating mineral deposits

RISK MANAGEMENT in Evaluating Mineral Deposits By Jean-Michel Rendu RISK MANAGEMENT in Evaluating Mineral Deposits By Jean-Michel Rendu PUBLISHED BY THE SOCIETY FOR MINING, METALLURGY & EXPLORATION Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved Society for Mining, Metallurgy & Exploration (SME) 12999 East Adam Aircraft Circle Englewood, Colorado 80112 (303) 948-4200 / (800) 763-3132 www.smenet.org The Society for Mining, Metallurgy & Exploration (SME) is a professional society whose more than 15,000 members represent professionals serving the minerals industry in more than 100 countries SME members include engineers, geologists, metallurgists, educators, students, and researchers SME advances the worldwide mining and underground construction community through information exchange and professional development Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc Electronic edition published 2017 All Rights Reserved Printed in the United States of America Information contained in this work has been obtained by SME from sources believed to be reliable However, neither SME nor the authors guarantee the accuracy or completeness of any information published herein, and neither SME nor the authors shall be responsible for any errors, omissions, or damages arising out of use of this information This work is published with the understanding that SME and the authors are supplying information but are not attempting to render engineering or other professional services It is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services If such services are required, the assistance of an appropriate professional should be sought Any statement or views presented here are those of the authors and are not necessarily those of SME The mention of trade names for commercial products does not imply the approval or endorsement of SME No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher ISBN: 978-0-87335-448-6 eBook: 978-0-87335-449-3 Library of Congress Cataloging-in-Publication Data Names: Rendu, Jean-Michel, 1944- author Title: Risk management in evaluating mineral deposits / by Jean-Michel Rendu Description: Englewood, Colorado : Society for Mining, Metallurgy &   Exploration, [2017] | Includes bibliographical references and index Identifiers: LCCN 2017012801 (print) | LCCN 2017017792 (ebook) | ISBN   9780873354493  | ISBN 9780873354486 (print) Subjects: LCSH: Mine valuation | Mineral industries Risk management |   Mining engineering Risk assessment Classification: LCC TN272 (ebook) | LCC TN272 R463 2017 (print) | DDC   622.068/1 dc23 LC record available at https://lccn.loc.gov/2017012801 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved CONTENTS Preface v Chapter Introduction Chapter Mining as a Complex and Risky Business Chapter Expensive Decisions: What May Have Gone Wrong? 17 Chapter Definition and Public Reporting of Mineral Assets 29 Chapter Life-of-Mine Cycle and Risk Factors 43 Chapter Risk Assessment Using Monte Carlo Simulation 57 Chapter Decision Tree to Evaluate Multistage Projects 69 Chapter Modeling of Space- and Time-Related Variables 79 Chapter Risk Tolerance and Utility Function 99 Chapter 10 Project Utility and the Triple Bottom Line 121 Chapter 11 Variables Influencing the Three Bottom Lines 137 Chapter 12 Geology and Deposit Characterization 165 Chapter 13 Resource Modeling 185 Chapter 14 Mining Engineering 211 iii Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved iv RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS Chapter 15 Metallurgy and Process Engineering 233 Chapter 16 Infrastructure 249 Chapter 17 Management 261 Chapter 18 Conclusions 281 Appendix A Application of Monte Carlo Simulation to a Copper-Gold Deposit 283 Appendix B Geostatistical Simulation of Gold Prices 289 References 297 About the Author 299 Index 301 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved PREFACE Some of the largest investments and property acquisitions ever made by mining companies took place in the first decade of the twenty-first century Many of these acquisitions were followed by write-downs of historical magnitude only a few years later In 2013 alone, write-downs from six major international companies reached a total of $20 billion These write-downs resulted in investors losing confidence in the mining industry; company shares losing significant value; and chief executive officers, top mining executives, and mining professionals losing their jobs These investments and subsequent write-downs are commonly attributed to the excitement resulting from the commodity super-cycle that characterized the 12-year period from 2004 to 2016 The first seven years depicted unprecedented commodity price increases, except for the short interruption following the 2008 global financial crisis This bull market ended around 2011–2012 and was followed by a sharp decrease in prices for the next four years One could point at the super-cycle to justify the risky decisions made in 2004–2010 and to explain the unprecedented write-downs that followed in 2011–2016 But one should not assume that prevailing economic conditions were the root cause of all flawed decisions This would imply that management had no part in these determinations It would mean that no lesson could be learned, that the mistakes made were unavoidable, and that the same mistakes will inevitably be made when the next economic cycle unavoidably occurs Why is it, for example, that a large base-metal mineral deposit was purchased for nearly $4 billion one year and written down less than three years later after updating the feasibility study? Why is it that a mining company paid a billion dollars to purchase mineral rights on a 1,000-km2 exploration area and wrote down an even larger amount a few years later, after determining that v Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved vi RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS the exploration potential was insignificant? Why is it that a mining project was approved for initial investment, including purchase of trucks, shovels, and major processing equipment, before having governmental agreement that mineral rights would be granted under reasonable terms and before evaluation of the mineral deposit had shown economic viability? Was it because the market prices of minerals were increasing at a historically record rate at the time the investments were made, while falling just as fast or even faster a few years later when write-downs proved necessary? Was it because companies that did not take drastic actions to show growth and willingness to take risk were highly criticized by financial analysts and punished in the marketplace? Might it be that the pressure toward acquisition and development of new projects was such that overly optimistic outcomes were assumed and that the resulting increased risk of failure was either not recognized or largely ignored? Was it because the irrational exuberance that prevailed at the time encouraged managers to make rash decisions to show decisiveness? Was it because industry-standard due diligence processes were bypassed? Is it possible that sellers were overvaluing the properties they offered and that buyers were overestimating their ability to create additional value? Was it because of management’s overconfidence in the competence of the project team to accurately assess geological, mining, processing, infrastructure, legal, financial, environmental, political, and social risks? Was it because managers responsible for project evaluation were steered by the company’s predetermined expectation of a positive outcome? Was it that a recommendation to go ahead with an acquisition would have resulted in immediate personal reward while the penalty for a project failure would have only been evident years later? In the current environment, with lower and still depressed commodity prices, it may be difficult to understand how so many high-risk decisions could have been made only a few years ago One must remember that prices were rushing up, a so-called super-cycle was in progress, demand for minerals was believed to be destined to increase continuously for the next decade or longer, and there was confidence that any unreasonable decision would be forgiven as a result of ever-increasing prices Higher prices would make even the highest risk investments profitable Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved PREFACE It is a well-known but often ignored fact that price cycles have always characterized the mining business environment, and there is no reason to believe that this will change in the future Stable prices are the exception Even if at a given time all fundamentals are interpreted as predicting that long-term demand will continue to grow indefinitely, one should not forget that booms never last forever Most mines require very high up-front investments that must be recovered over many years Decisions must be made with full consideration of the fact that short- to medium-term price cycles will occur during the mine life, with both positive and negative consequences When considering the possible consequences of price cycles, one must keep in mind that costs of goods and services follow a similar but lagging pattern During the early phase of the price super-cycle, investment decisions were often made under the assumption that the cost of goods and services would remain constant over time while the value of products sold would continue to increase Would it not have been more reasonable to assume that not only shareholders, but also employees, suppliers, nearby communities, and governments would demand higher contributions from the economic benefits resulting from higher prices? The cost increases followed the rise in commodity prices, but the costs did not decrease proportionally when prices fell Why is it that decisions made by publicly traded mining companies are strongly influenced by the opinions of financial analysts, even though these analysts are not responsible for those decisions? Analysts bring valuable insight, but are they more knowledgeable than the company management? Why is it that so many mining companies make similar decisions at the same time, such as risky investments during the upside of price cycles and disinvestment during the downside? Is it because there is perceived safety in following group decisions? Mining companies lean toward following the same logic to make the same decisions at the same time The result is an increase in peaks and troughs of price cycles New mines are developed when prices increase This is logical as cash flows, expected profits, and borrowing capabilities are improving But bringing new mines into production takes years, and, more often than not, the result is overproduction when prices drop, thus increasing the magnitude and duration of this fall When a mine is built and capital expenditures are sunk, production is maintained as long as cash flows remain positive Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved vii viii RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS Conversely lack of investment during the downside of the price cycle results in underproduction during the upside as well as sharper price increases Leading managers are those who anticipate cycles and consistently make highervalued decisions But this implies making decisions that are counterintuitive at the time and losing the short-term comfort that comes from following group dynamics These are some of the issues that must be considered if the investment decision-making process is to be improved And there is no better time to consider the preceding questions than during the tumultuous period the mining industry is currently going through, when consequences of some of the best and worse practices have been emphasized by arguably challenging worldwide economic conditions Business cycles will continue to occur, and the timing of these cycles will continue to be unpredictable We should attempt to learn from the past and not repeat the same mistakes This book is intended to help move this learning process forward The objective is to provide guidelines that can be used to improve the decisionmaking process in a broad variety of circumstances But each project is different, and it is appropriate to conclude with a quote from Alexis de Tocqueville in his 1848 introduction to Democracy in America (New York: Harper Perennial, 1988): I realize that despite the trouble taken, nothing will be easier than to criticize this book, if anyone thinks of doing so Those who look closely into the whole work will, I think, find one pregnant thought which binds all its parts together But the diversity of subjects treated is very great, and whoever chooses can easily cite an isolated fact to contradict the facts I have assembled, or an isolated opinion against my opinions I could not have said it better But this does not absolve me from keeping full responsibility for errors, lapses, or absence of clarity that may be found in this book Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved PREFACE ACKNOWLEDGMENTS I thank the Society for Mining, Metallurgy & Exploration who made publication of this book possible, including Jane Olivier, manager of book publishing, and Diane Serafin, managing technical editor I am grateful to Roussos Dimitrakopoulos, Francois Grobler, Steve Hoerger, Peter McCarthy, Harry Parker, and Pat Stephenson, who provided a number of helpful comments on an early draft of this manuscript And as always, I cannot thank my family enough for their love and support throughout my mining career Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved ix REFERENCES CCG (Centre for Computational Geostatistics) 2014 Home page University of Alberta, Canada www.ccgalberta.com COSMO Stochastic Mine Planning Laboratory n.d Home page McGill University, Canada http://cosmo.mcgill.ca (accessed Nov 2016) CRIRSCO (Committee for Mineral Reserves International Reporting Standards) 2013 CRIRSCO International Reporting Template for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves www.crirsco.com/template.asp DMDU (Society for Decision Making Under Deep Uncertainty) n.d Home page www.deepuncertainty.org (accessed Nov 2016) The Economist 2009 Triple bottom line Nov 17 (online extra) www.economist com/node/14301663 FASB (Financial Accounting Standards Board) 2008 EITF abstracts Mining Assets: Impairment and Business Combinations Issue No 04-3 www fasb.org/jsp/FASB/Document_C/DocumentPage?cid=1218220141031 &acceptedDisclaimer=true Graff, R.P 2008 Recognition of value beyond proven and probable reserves in business combinations Min Eng 60(8):45 ICCPM (International Centre for Complex Project Management) 2014 What is complex project management? Connect (newsletter), September https://iccpm.com/content/september-2014 ICMM (International Council on Mining and Metals) 2015 www.icmm com/en-gb/about-us/member-commitments/icmm-10-principles IRM (Institute of Risk Management) 2016 Definition of risk www.theirm org/about/risk-management/ ISO 31000:2009 Risk Management: Principles and Guidelines www.iso.org/ iso/home/standards/iso31000.htm 297 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved 298 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS Journel, A.G., and Huijbregts, Ch.J 1978 Mining Geostatistics London; New York: Academic Press Leuangthong, O., and Deutsch, V 2003 Stepwise conditional transformation for simulation of multiple variables Math Geol 35(2) NI 43-101 National Instrument 43-101: Standards of Disclosure for Mineral Projects Ontario Securities Commission www.bcsc.bc.ca/ For_Companies/Mining/ Rand Corporation 2016 Decision Making Under Uncertainty Research Team www.rand.org/jie/infrastructure-resilience-environment/projects/ improving-decisions/researchteam.html Rendu, J.M 2014 An Introduction to Cut-off Grade Estimation, 2nd ed Englewood, CO: SME Sarbanes–Oxley Act 2002 Public Law 107-204, 116 Stat 745 Section 404, Management assessment of internal controls https://www.sec.gov/ about/laws/soa2002.pdf SRA (Society for Risk Analysis) 2016 Home page www.sra.org (accessed Nov 2016) UN (United Nations) 1987 Report of the World Commission on Environment and Development: Our Common Future (the Brundtland Commission) Oxford: Oxford University Press Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved ABOUT THE AUTHOR Jean-Michel ( J-M) Rendu is an independent consultant supplying services to the international mining industry J-M retired from Newmont Mining Corporation in 2001 as vice president of resources and mine planning with worldwide responsibility for corporate and mine-site mining engineering activities, and for estimation and reporting of resources and reserves Before joining Newmont, J-M was an associate consultant with Golder Associates; an assistant professor with the University of Wisconsin– Madison; head of operations research with Anglovaal, Johannesburg, South Africa; and systems analyst with Kennecott Copper Corporation J-M graduated from École des Mines de Saint-Étienne in 1966 and obtained M.S and Eng.Sc.D degrees from Columbia University in the City of New York He authored more than 50 technical papers on deposit modeling, mine engineering, estimation of resources and reserves, and U.S and international regulatory requirements for public reporting He is the author of An Introduction to Geostatistical Methods of Mineral Evaluation, published by the South African Institute of Mining and Metallurgy in 1978 (second edition, 1981) and An Introduction to Cut-off Grade Estimation, published by SME in 2008 (second edition, 2014) 299 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved 300 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS In addition to being a founding registered member of SME, J-M has chaired SME’s Ethics Committee, Resources and Reserves Committee, and Mining and Exploration Division He was a founding member and U.S representative of the Committee for Mineral Reserves International Reporting Standards (CRIRSCO) and is a fellow of the Australasian Institute of Mining and Metallurgy and the Southern African Institute of Mining and Metallurgy J-M was a recipient of the Henry Krumb Lecturer Award, the SME Presidential Award, the AIME Mineral Economics Award, the SME Distinguished Member Award, and the American Mining Hall of Fame Medal of Merit J-M is an elected member of the U.S National Academy of Engineering Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved INDEX Note: f indicates figure; t indicates table catastrophic events, 144, 151–152, 164 caveat emptor, principle of, 22 chief executive officers (CEOs), 3, 262 climate change, as a risk variable, 89–90 closure, 48, 150–151, 226 Committee for Mineral Reserves International Reporting Standards (CRIRSCO), 30, 267 communication among specialists, 2, 178, 246 dimensions of, failure of example, 25–26 between geologist and metallurgist, 178 horizontal, 2–3 by managers, 2–3, 263 matrix management, 129–130 with stakeholders, 154–158 terminology guidelines, 29 communities, local, 2, 162–164, 228–229, 245 competent person (CP), 31–32 complex projects, 7–8, 43–44 construction, 48 contracts, 263 accuracy, 52–56, 55f Agricola, Georgius, 1, 7, 17, 29, 43, 121, 137, 145–146, 152–153, 161, 165, 211, 233, 249, 261 analysis in the mine life cycle, 49 as a source of error, 52–53 aquifers, 203–204 auditing, of resource models, 209 banks, 158, 226–227, 244 base case, 144 Bayesian statistics, 14 biological environment, 150, 225–226, 243 black swan events, block models See resource models boards of directors (BOD), 2–3 bonuses, 268–269 bottom line, 121 bottom-line analyst, 131 business analysts See financial analysts business strategy risk mitigation and, 93–94 risk-seeking vs riskaversion, 104–105 301 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved 302 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS corporate governance, 264–265 corporate social responsibility, 265–267 CRIRSCO International Reporting Template for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves, 30–31 customers, 160, 228, 245 cutoff grade estimation, 217, 218f De Re Metallica (Agricola), 1, 7, 17, 29, 43, 121, 137, 145–146, 152–153, 161, 165, 211, 233, 249, 261 decision tree analysis for decision-making under uncertainty, 14 graphical representation of a multistage project, 69–72, 70f numerical analysis of an exploration project, 74–78, 75t., 76f use of to analyze an exploration project, 72–74 utility function and, 111–115 decision-making defined, examples of poor, 17–27 in the mine life cycle, 49–50 and space related (spatial) variables, 79–84 and time related (temporal) variables, 84–86 See also statistical methods deposit characterization early-stage exploration, 170–172 late-stage exploration, 175–177 as a multistage process, 169–170 risk management and, 179–183 deposit simulation, 81, 194–195 domain definition, 189–190 early-stage exploration, 170–174 Elkington, John, 122n employees and their families, 159–160, 227, 245 engineering detailed, 48 environmental, 177, 199 infrastructure, 177 mining, 175 process, 176 See also infrastructure; mining engineering; process engineering engineering, procurement, and construction (EPC) companies, 251 environmental bottom line defined, 124 environmental impact variables, 147–152 and exploration programs, 171 general considerations, 146–147 mining decisions and, 224–226 process engineering and, 241–243 uncertainty and risk assessment, 152 See also triple bottom line environmental engineering, 177, 199 environmental impact variables, 142, 147–152 environmental models, 199, 201 environmental risks, 13–14, 144 errors classification of estimation, 52–56 sources of, 51 experts, 2–3 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved INDEX exploration, 47 exploration potential overestimation of example, 23–25 exploration programs early- and late-stage geologists, 183 early-stage, 170–174 early-stage financial evaluation example, 172–174 justification for, 169–170 late-stage, 175–177 exploration results defined, 33–34 relationship with mineral resources and reserves, 33f external infrastructure social requirements, 257–258 technical requirements, 254–256 feasibility studies, 38, 47–48, 274 feedback, 49–50 financial analysts, 158–159, 227, 244 financial bottom line environmental information and, 142 and exploration programs, 171 financial information and, 141–142 general considerations, 137–139 how measured, 137 mining decisions and, 222–224 process engineering and, 239–241 social information and, 142–143 sunk costs and, 139–140 technical information and, 140–141 uncertainty and risk assessment, 143–145 See also triple bottom line financial information, 141–142, 223–224, 240–241 financial institutions, 158, 226–227, 244 financial models, financial risks, 12–13 4-D models See hydrological models full-cost accounting defining project utility using, 122–125 organization chart for, 128–130, 130f See also environmental bottom line; financial bottom line; social bottom line; triple bottom line future generations, 164, 229, 245 geochemistry, 190 geologic models, 189–190 geological risks underestimation of example, 21–23 geologists communication with metallurgists, 178 early-stage vs late-stage, 183 geology domain definition, 189–190 general considerations, 165–167 relationship with mining and processing, 167–169, 168f.–169f., 211–214, 212f.–213f., 233–236, 234f., 236f See also deposit characterization; resource modeling geometallurgical models, 196–198 geometallurgical properties, 11n, 190 geostatistics, 81–83, 186–187, 191–196 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved 303 304 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS simulation of gold prices, 289–296, 290f.–291f., 294f.–295f geotechnical models, 201–202 geotechnical risks and infrastructure design, 200 and mine design, 216 underestimation of example, 21–23 governmental risks, 13–14 governments, 2, 161, 228, 245 grade models See resource models Hoover, Herbert Clark, 1n Hoover, Lou Henry, 1n horizontal communication, 2–3 hydrological models, 202–204 indicated mineral resources, 32–33, 36 inferred mineral resources, 32–33, 35–36 information environmental impact, 142, 147–152 financial, 141–142, 223– 224, 240–241 gathering, 48 social impact, 142–143, 153 technical, 140–141 See also environmental bottom line; financial bottom line; resource modeling; social bottom line; triple bottom line information effect, 83 infrastructure engineering assessment of requirements, 259 defined, 177 design, and geotechnical characterization, 200 design, and topographic models, 188 external, 254–258 internal, 251–253 risk management and, 258–260 social, 251 types of, 250–251, 250f underestimation of costs example, 18–21 internal control reports, 41 internal infrastructure, 251–253 International Council on Mining and Metals (ICMM), 265–267 International Finance Corporation, 94 International Organization for Standardization, 10 key performance indicators (KPIs), 268–269 kriging, 82–83, 191–193 late-stage exploration, 175–177 life-of-mine cycle See mine life cycle local communities, 2, 162–164, 228–229, 245 management boards of directors (BOD), 2–3 corporate, 3, 262 corporate governance, 264–265 decisions and risk mitigation, 263 decisions with costly outcomes examples, 271 matrix, 129–130 and mining company success, 261 mission and values, 262–264 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved INDEX motivators and risk control, 182 project, 267–270 and project stages, 271–274 and project value assessment, 131–133 responsibilities of, 40–41, 130 and risk creation, 275–277 and risk mitigation, 277– 279, 282–283 risk-seeking vs risk-aversion, 104–105 marketing, 177 measured mineral resources, 32–33, 36–37 metal prices, as risk variables, 86, 86f.–87f metallurgical testing, 176–177 See also process engineering metallurgists, communication with geologists, 178 mine closure, 48, 150–151, 226 mine life cycle as a linear process, 46–49, 47f with multiple decision points and feedbacks, 49–50, 50f mine planning See mining engineering mineral reserves classification of, 33, 35–37 defined, 35 relationship with exploration results and mineral resources, 33f mineral resources classification of, 32–33, 35–37 defined, 34 relationship with exploration results and mineral reserves, 33f mineralogy, 190 mining engineering defined, 175 design and geotechnical risks, 216 design and water management, 219 and the environmental bottom line, 224–226 and the financial bottom line, 222–224 relationship with geology and processing, 167–169, 168f.–169f., 211–214, 212f.–213f., 233–236, 234f., 236f risk management and, 229–232 role of, 214–222, 222f and the social bottom line, 226–229 model reliability, 185 modeling See resource modeling modifying factors, 35 Monte Carlo simulations application of to a copper-gold deposit, 283–288, 285t.–287t for decision-making under uncertainty, 14 and evaluation of staged projects, 67 general considerations, 58–59 of multiple correlated variables, 64, 66 process, 59–60, 61t of uncorrelated variables, 61–64, 62t., 63f., 65f national reporting organizations (NROs), 30 natural disasters, 144, 151–152, 164 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved 305 306 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS net present value (NPV), 58, 99, 218 nongovernmental organizations (NGOs), 159, 227, 245 operation, 48 opportunities, 44 optimization, 202 ordinary kriging (OK), 192–193 ore control, 215 ore type, 189–190 political risks and the financial bottom line, 144 underestimation of example, 18–21 pollution See environmental bottom line precision, 52–56, 55f predictable uncertainty, 87–90, 89f pre-feasibility studies, 38, 273–274 probable mineral reserves, 33, 37 process engineering defined, 175 and the environmental bottom line, 241–243 and the financial bottom line, 239–241 and geometallurgical characterization, 246 relationship with geology and mining, 167–169, 168f.–169f., 211–214, 212f.–213f., 233–236, 234f., 236f risk management and, 246–248 role of, 235f., 236–239 and the social bottom line, 243–245 processing See process engineering project evaluation complexity of, 7–8, 43–44 defined, 1–4 possible estimation errors graph, 45–46, 45f and project manager qualifications, 271–274 stages of, 47–49 See also deposit characterization; exploration programs; geology; mining engineering; process engineering project managers and project stages, 271–274 responsibilities of, 2–3, 267–270 See also management project risk, 44 project utility defining using full-cost accounting, 122–125 evaluating the triple bottom line, 131–133 proved mineral reserves, 33, 37 public disclosure, 12–14, 39–40 qualified person (QP), 31n quality assurance/quality control (QA/QC), 180–181, 206 random noise, 87–90, 89f real options analysis, 57–58 reconciliation, of models, 206, 208–209 regulations Sarbanes-Oxley Act of 2002, 41 See also environmental bottom line; social bottom line Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved INDEX reserve estimation, 47 resource estimation in the mine life cycle, 47 precision and accuracy in, 52–56 resource evaluation projects See project evaluation resource modeling as a complex process, 167–169, 169f environmental models, 199, 201 general considerations, 185–187 geologic models, 189–190 geometallurgical models, 196–198 geotechnical models, 201–202 hydrological models, 202–204 linear process, 167–168, 168f model compatibility, 207 model reconciliation, 206, 208–209 resource models, 191–196, 215f., 230 risk management and, 204–209 soil, subsoil, and bedrock models, 204 topographic models, 187–188 resource models deposit simulation, 194–195 geostatistical, 191–196 influence of mine selectivity on, 193–194 modeler competence, 195–196 relationship with mining method, 215f selectivity and, 230 risk defined, 8, 44 error sources, 51 increased by KPIs and bonuses, 268–269 public disclosure of, 12–14 risk analysis, with and without flexibility, 145 risk assessment defined, 8–9 and the environmental bottom line, 152 failed communication between geologist and metallurgist example, 25–26 and the financial bottom line, 143–145 management and, 278 mathematical methods of, 14–15 overestimation of exploration potential example, 23–25 project flexibility example, 95–97, 95t and simulation, 90–93, 91f., 95–97, 95t tasks to complete, 50–51 underestimation of geological and geotechnical risk example, 21–23 underestimation of political risk and infrastructure cost example, 18–21 risk aversion, 99–100, 102f risk creation, management and, 275–277 risk factors assessing, 27 environmental, social, and governmental, 13–14, 144 financial, 12–13 public disclosure of, 39–40 technical and operational, 13 understanding, 44–45 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved 307 308 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS See also risk variables risk ignorance, 278 risk management conclusions, 282–283 conditions to satisfy, 44–45 and deposit characterization, 179–183 and infrastructure, 258–260 management and, 278 and mining engineering, 229–232 and model building, 204–209 motivators and risk control, 182 options to consider, 10–11 and process engineering, 246–248 risk mitigation and business strategy, 93–94 the impact of risk variables on, 90–94 management and, 263, 277–279 risk assessment and, 8–11 risk reporting matrix, 134–136, 135f risk tolerance, 9, 99–100 See also utility function risk variables climate change as, 89–90 metal prices as, 86, 86f.–87f mitigating the impact of, 90–94 and the risk reporting matrix, 134–136, 135f space related (spatial), 50–52, 53f., 54t., 70f., 79–84 time related (temporal), 50–52, 53f., 54t., 84–86, 87–90 understanding, 44–45 See also risk factors sample selection, for metallurgical testing, 176–177 scope definition, 48 scoping studies, 37, 272–273 Securities and Exchange Commission, 41 selective mining unit (SMU), 193n, 215 semivariogram, 192n sensitivity analysis, 144 shareholders, 2, 158, 226–227, 244 social bottom line catastrophic events and natural disasters, 144, 151–152, 164 communication, 156–158 customers and suppliers, 160– 161, 228, 245 defined, 124, 153 employees and their families, 159–160, 227, 245 and exploration programs, 171–172 financial analysts, 158–159, 227, 244 future generations, 164, 229, 245 general considerations, 153–155 governments, 161, 228, 245 local communities, 162–164, 228–229, 245 minimum requirements, 155–156 mining decisions and, 226–229 nongovernmental organizations (NGOs), 159, 227, 245 process engineering and, 243–245 shareholders, banks, and financial institutions, 158, 226–227, 244 See also triple bottom line social impact variables, 142–143, 153 See also social bottom line Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved INDEX social infrastructure, 251, 257–258 social license to operate (SLO), 265–267 social risks, 13–14, 144 soil, subsoil, and bedrock models, 204 space related (spatial) variables decision tree example, 70f decision-making and, 79–84 and risk, 50–52, 53f., 54t stakeholders, 2, 123–124, 226–227, 244 See also social bottom line; social impact variables statistical methods Bayesian statistics, 14 decision tree analysis, 14, 69–78 geostatistics, 81–83, 186–187, 191– 196, 289–296, 290f.–291f., 294f.–295f Monte Carlo simulations, 14, 57–67, 283–288, 285t.–287t statistical stationarity, 195 subject matter experts, 32 sunk costs, 139–140 suppliers, 160–161, 228, 245 sustainable development, 122–123, 164 principles of, 265–267 technical and operational risks, 13 technical departments geology, 165–183 mining, 211–232 processing, 233–248 technical information, 140–141 technical models, 3-D modeling See geostatistics; resource modeling time related (temporal) variables assessing project flexibility example, 95–97, 95t metal prices, 86, 86f.–87f properties of, 84–85 and risk, 50–52, 53f., 54t trends and random noise in, 87–90 topographic models, 187–188 trends, 87–90, 89f triple bottom line evaluating, 131–133 and exploration programs, 171–172, 175 full-cost accounting, 122–125 graphical representation of uncertainty, 126–128, 127f infrastructure and, 254–256 measuring, 125–126 relationship between information and, 133–134, 134f three pillars of sustainability, 121–122, 123f See also environmental bottom line; financial bottom line; social bottom line uncertainty and the environmental bottom line, 152 and the financial bottom line, 143–145 graphical representation of bottomline, 126–128, 127f and the risk reporting matrix, 134–136, 135f sources of, 50–52, 87–90 Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved 309 310 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS sources of in resource modeling, 204–209 variables in project evaluation, 44–45 United Nations, 122 unpredictable uncertainty, 87–90, 89f utility defined, 5, as a measure of risk assessment, 14 project, 121–125, 131–133 utility function and decision tree analysis, 111–115 defined, 100–104, 102f.–103f how to define a company’s or individual’s, 116–120, 118t., 119f., 120t mathematical representation of, 105–108 simple applications of, 108–111, 109f., 109t.–111t value assessing with the estimated triple bottom line, 131–133 defined, estimating using utility function, 108–111, 109f., 109t.–111t variograms, 192 vertical communication, 2–3 waste characterization models, 199 water hydrological models, 202–204 impact of mining operations on, 147–149 mine design and water management, 219 See also environmental bottom line whistleblowers, 275 Williams, Terry, Copyright © 2017 Society for Mining, Metallurgy & Exploration Inc All rights reserved RISK MANAGEMENT in Evaluating Mineral Deposits By Jean-Michel Rendu Don’t invest in a mining project until you’ve read Risk Management in Evaluating Mineral Deposits Mining is not for the fainthearted Yes, the rewards are enormous But so are the risks—and consequences—of failure Risk Management in Evaluating Mineral Deposits walks you through the many-faceted risk evaluation you need to conduct before you invest your hard-earned dollars Written by a mining professional with a strong background in technical and financial studies, risk assessment, and statistics, this book provides a detailed suite of tools so you can determine whether investing in a mining project makes sense for you Looking at a host of issues—the composition of the ore deposit, the management’s previous record, This book provides a lucid and comprehensive analysis of the wide range of factors to be considered when making [investment] decisions, and I recommend it to anyone who is embarking on the evaluation or delivery of any future projects Tom Butler CEO, International Council on Mining and Metals, London, UK I would consider this a “must read” for those who want a better understanding of the risks associated with evaluating mineral deposits Michael N Cramer Managing & Senior Technical Director Citibank NA, Corporate & Investment Banking, New York, New York the quality of the information at hand, and your own risk-tolerance comfort level, to name a few—author Jean-Michel Rendu provides a comprehensive guide to determine when to invest with high confidence, when to demand a plan that reduces the risks and increases the chances of a positive outcome, and when to just walk away This book is a welcome guide the likes of which have not been seen since Herbert Hoover’s Principles of Mining (1909) or Hugh McKinstry’s Mining Geology (1948) Harry Parker Consulting Mining Geologist & Geostatistician Amec Foster Wheeler A first of its kind and a technically outstanding book on risk management This in-depth book… sets a new standard and addresses a major need in the technical literature Roussos Dimitrakopoulos Professor, Canada Research Chair, McGill University, Montreal, Quebec Risk Management in Evaluating Mineral Deposits is the kind of book the mining industry has been waiting for Marcelo Godoy VP Resource Evaluation and Mine Planning, Newmont Mining Corporation, Denver, Colorado ISBN 978-0-87335-448-6 0 0 The Society for Mining, Metallurgy & Exploration (SME) is a professional society whose members include engineers, geologists, metallurgists, educators, students, and researchers SME advances the worldwide mining and underground construction community through information exchange and professional development 780873 354486 ... for Mining, Metallurgy & Exploration Inc All rights reserved 11 12 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS PUBLIC DISCLOSURE OF RISK IN MINING The main difference between mining risk and... reserved vi RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS the exploration potential was insignificant? Why is it that a mining project was approved for initial investment, including purchase... Mining, Metallurgy & Exploration Inc All rights reserved 18 RISK MANAGEMENT IN EVALUATING MINERAL DEPOSITS in the future Other examples are given in later chapters to add additional insight into