Meet Senior Geologist, Nerys Walters when she presents on “Data integrity and geological understanding-key requirements for successful resource estimation” at The Geological Society-Mineral Resource Estimation Conference on October 22, 2018 at Burlington House in London.
In an era of rapid technological innovation, opportunities exist to improve efficiency and quality of resource estimates, both developing trust and encouraging investment in mining projects.
Forming part of the Year of the Resource, this conference aims to provide a forum for resource estimate practitioners to meet and discuss new developments and advances in mineral resource estimation and reporting.
Abstract: Data Integrity and Geological Understanding – Key Requirements for Successful Resource Estimation
Authors: Nerys Walters – Senior Geologist and Aaron Meakin Manager – Resources
It’s a well-known fact that reliable resource models translate into profitable projects. But what makes a reliable resource estimate?
Although somewhat of a simplification, the resource estimation work flow is often comprised of 3 stages; data review, geological model assessment and associated modelling, and grade/density interpolation or assignment.
Given the compounding effect of errors, it would be logical that most time is spent on the first two stages. The reality is the opposite, with a much larger amount of time generally spent on grade estimation.
The foundation of any good resource estimate is reliable data. Whether in the form of down hole surveys, collar data, sampling and analytical data, logging data, or 3D points that will form a topographic survey, data quality is critical. Whilst the days of multiple excel spreadsheets being used to manage data are waning, even the most well organised databases can suffer from a lack of consistent data collection or deficiencies from historic datasets.
It is possible, for example, that multiple analytical techniques were used, which may not be comparable. It is also common to find that a total analysis method has been adopted which is not representative of the economic portion of the mineralisation, due to issues around metallurgical recoveries. The analysis method must be considered according to its suitability for the style and tenor of mineralisation.
Quality assurance (QA) protocols and quality control (QC) data also require interrogation, with deficiencies investigated and corrected. Furthermore, QC data should be collected for all data types, not just sampling and analytical data.
Reliable geological models are generally based upon a combination of sound surface mapping (local and regional scale), consistent well-thought out drill hole logging and well-constructed 2-D interpretive sections. These inputs should be used to create a 3D model that is interrogated and tested by further drilling, including the possibility of alternative interpretations which could impact the Mineral Resource. In fact, The JORC Code (2012 Edition) explicitly states that the Competent Person must comment on the effect, if any, of alternative interpretations on Mineral Resource estimation, how geology has been used to guide and control Mineral Resource estimation and the factors affecting continuity both of grade and geology.
Failure to adequately account for data quality and consider the geological model can materially impact the Mineral Resource and other downstream processes. These items can make or break a project. Using case studies, issues that are encountered regularly will be presented and their knock-on effects discussed.
ABOUT OUR PRESENTER
A geologist with over ten years’ experience, spanning early stage exploration through to mine site production. Nerys’ skills include 3D implicit and explicit modelling of geology and mineralisation using Micromine and Leapfrog Geo software, drill hole planning for grass roots exploration through to near mine development, site reviews for JORC / NI43-101 compliance, lab audits and Mineral Resource Estimation.