ENGR 3346 Ore Reserve Analysis and Geostatistics PDF

Mining Optimization Laboratory Ore Reserve Analysis and Geostatistics ENGR 3346 Lecture 01b - Introduction Dr. Eugene Ben-Awuah, PEng Bharti School of Engineering Laurentian University Office: F220 Tel: 705-675-1151 ext. 2195 Email: [email protected] Why ENGR 3346 1. The intrinsic value of...

Mining Optimization Laboratory Ore Reserve Analysis and Geostatistics ENGR 3346 Lecture 01b - Introduction Dr. Eugene Ben-Awuah, PEng Bharti School of Engineering Laurentian University Office: F220 Tel: 705-675-1151 ext. 2195 Email: [email protected] Why ENGR 3346 1. The intrinsic value of a mining business is the geologic resource or reserve estimate 2. Accurate, reliable estimate of resources and deposit characteristics is essential for good mine planning & design 3. Today, geostatistics is the main technique for modeling deposit characteristics and estimating resources Eugene Ben-Awuah 2 Mining Optimization Laboratory Geostatistics 3 • Geostatistics is statistical and Monte Carlo simulation principles applied to geological variables • Provides a framework to: – Work within all known constraints – Quantify and exploit spatial dependence – Develop numerical algorithms for modeling and quantifying uncertainty • Allows one to: – Estimate attributes (ore grades, pollutant concentrations, porosity, etc.) – Quantify uncertainty surrounding estimates for decision making – Design optimal sampling plans • Does not: – Replace good additional data – Replace the need for common sense and good judgment – Save time Numerical Modeling 4 • There has been a major revolution in science and reasoning over the last 100 years that has largely gone unnoticed. • Historically, science involved (1) extensive data collection and physical experimentation, then (2) deduction of laws and relationships consistent with the data • Now, science is much more concerned with (1) understanding and quantifying physical laws, and (2) numerical modeling for inference • In general: – Numerical modeling has become more important than physical experimentation; – Uncertainty is quantified and managed rather than ignored. • Numerical modeling is ubiquitous in modern science and engineering - virtually all designs are on computers Eugene Ben-Awuah Mining Optimization Laboratory Historical Perspective 6 • Geostatistics was started in the 1960's by Krige and Sichel in South Africa and Matheron in France • The application of geostatistical techniques became popular in mining and meteorology • Petroleum applications took off in the mid 80s with the use of conditional simulation (heterogeneity and uncertainty) • Now, these techniques are applied in many diverse applications from mining, petroleum, environmental, fisheries, forestry, environmental remediation and so on Applications of Geostatistics 1 • Business Need: make the best possible decisions in the face of uncertainty. One of the biggest uncertainties is the numerical description of the subsurface. • Statistics is concerned with scientific methods for collecting, organizing, summarizing, presenting and analyzing data, as well as drawing valid conclusions and making reasonable decisions on the basis of such analysis. • Geostatistics is a branch of applied statistics that places emphasis on: – (1) the geological context of the data, – (2) the spatial relationship between the data, and – (3) data measured with different volumetric support and precision. Eugene Ben-Awuah 7 Mining Optimization Laboratory Applications of Geostatistics 2 • • Geostatistical techniques are an indispensable part of resource management because quantitative numerical models are required for planning and economic optimization Following are some objectives of studies that use geostatistical tools: – – – – – • • 8 create 3-D models of heterogeneity refine estimates of in-place resources quantify uncertainty in those resources assess influence of heterogeneity on process performance quantify uncertainty in production predictions Geostatistics provides a variety of tools that are combined in different ways for different objectives and for particular types of geological settings Some considerations: – Geological understanding / zonation guides geostatistical modeling – Data quality / sampling must be addressed before geostatistical modeling Why Geostatistics? • Practicality/consistency with data – The best approach to model the spatial distribution of reservoir properties • Repeatability/audit-trail • Easy to merge incremental data i.e. new drillholes, etc • 3-D models lead to better resource assessments than set of 2-D interpretations • Better and more reliable modeling of variability • Framework to integrate data: – geological interpretation – hard and soft data – data representing different measurement supports • Assessment of uncertainty in process performance due to uncertainty in geological model Eugene Ben-Awuah 9 Mining Optimization Laboratory Key Geostatistical Concepts 1 10 • Numerical Modeling: At any instance in geological time, there is a single true distribution of properties in each deposit. This true distribution is the result of a complex succession of physical, chemical, and biological processes. Although some of these depositional and diagenetic processes may be understood quite well, we do not completely understand all of the processes and have no access to the initial and boundary conditions in sufficient detail to provide the unique true distribution. Key Geostatistical Concepts 2 11 • Uncertainty: All numerical models would be found in error if we were to excavate that unsampled volume and take exhaustive measurements: there is uncertainty. This uncertainty exists because of our ignorance/lack of knowledge. It is not an inherent feature of the deposit. • Uncertainty exists because of incomplete data: – Cannot be avoided, – Can be reduced by consideration of all relevant data, and – Can be managed • Uniqueness and Smoothing: Conventional mapping algorithms were devised to create smooth maps to reveal large scale geologic trends; for reservoir applications, however, the extreme high and low values often have a large effect on fluid flow. Eugene Ben-Awuah Mining Optimization Laboratory Nevsun Resources Ltd. (NYSE: NSU) 12 – In February 2012, Nevsun revised its reserve estimates and reported it to the market • Impacts: – Nevsun stock dropped dramatically – Nevsun had to cut the production forecast for 2012 – Class action law suit filed by investors who felt duped Summary • ENGR 3346 introduces theoretical concepts and a design component aimed to motivate and equip students on the basics of ore reserve estimation and geostatistical analysis • This is relevant because: – Numerical modeling has largely replaced physical experimentation – Inference has largely replaced deduction – Uncertainty exists because of our incomplete knowledge • Geostatistical Analysis is key to quantifying uncertainty • Must also quantify consequences of making a mistake for optimal decision making……………… Eugene Ben-Awuah 13 Mining Optimization Laboratory Reading Assignment 14 • Read Chapter 2 of Isaaks & Srivastava (1990): An Introduction to Applied Geostatistics before next class • Read AMEC Minproc (2010), Oyu Tolgoi Project: Technical Report, 629pp http://www.turquoisehill.com/i/pdf/IDP10_June062010.PDF Eugene Ben-Awuah