Open Cut » Coal Extraction
The coal mining industry is reliant on forecasting and monitoring of coal resources and reserves. Geological uncertainty and risk impact heavily upon coal resource assessments, reserve forecasts and long-term planning. Reliability and confidence, transparency and technical defendability are critical. Considering that investment decisions in the Australian coal mining industry are in the order of billions of dollars, there is a genuine need to understand and quantify the geological risk underpinning reserves, projects and operations. Furthermore, modern project evaluation frameworks suggest that the mere quantification of risk helps to shelter investments and adds substantial value to the financial valuation of assets.
Past attempts to assign robust quantitative measures of confidence to resource estimates have been unsuccessful. Furthermore, typical drill hole spacing requirements for a given resource classification are found to commonly over-drill coal seams for the same level of quantified confidence as that of a sparser drillhole pattern. Thus, there is a need to address the implementation of a more general, reproducible, state-of-the-art yet practical approach to quantifying geological risk for reserve / resource classification.
The present ACARP project, developed to assist in meeting the above need, aims to develop, establish and demonstrate a practical, state-of-the-art quantitative approach for reliable, repeatable, and standardised resource and reserve delineation; assessment and classification based on the quantification of geological risk. Defining limits and criteria to be used for the classification of measured, indicated and inferred resources in a coal deposit presents some very specific problems including the
- quantification of risk which involves the in-situ local variability of coal geological attributes, such as thickness with regional trends or splitting, and multiple quality parameters;
- quantification of risk involves and reflects the level of available information (including drilling, sampling, coal quality analysis, geologic interpretations and modelling methods); and,
- quantification of risk arising from structural deformation (typically faulting in Australia). This is important and should be part of resources classification where appropriate.
The integration the of above such as in the work presented in this report will have a substantial impact on transparent and repeatable coal resources classifications, related risk assessment and confidence of estimation, cost savings in exploration programs, and better informed investment decisions. In addition, the work assists Competent Persons to better comply with the JORC Code and technical defence of resource estimates, as well as provide a more confident basis for long-term planning and project evaluation. Last but not least, the ability to quantify geological seam risk contributes to the ability to better assess asset value.
The project objectives were to
- develop a quantitative approach for assessing geological uncertainty and risk as linked to the definition, variation and confidence in estimation of key parameters of coal deposits, suitable to support JORC requirements;
- incorporate the assessed risk of key parameters with the quantitative assessment of structural (fault) complexity, building on the work undertaken under ACARP project C7025;
- Implement and test computerised algorithms for optimal drilling programs based on new and state-of-the-art stochastic simulation methods that address specific issues associated with coal deposits and risk assessment;
- demonstrate the validity of the above approach using back analysis at a suitable mine in combination with reconciliation processes and a ?controlled environment', as appropriate; and
- document practical guidelines and tools for the application of the framework and methods to allow Competent Persons in mines to assign objective classifications to their resource computations for JORC reporting.
The scope of the study included selected parts of the: D14 seam at Peak Downs, Qld; McCarthur seam at Warkworth, NSW; and the German Creek seam and Aquila seam at Oaky Creek Mine, Qld. These areas have quantity and quality coal data available, including some production records for back-analysis. Data were provided by: BHPBilliton-Mitsubishi Alliance (BMA), Coal and Allied (RioTinto Coal), and Oaky Creek Coal. The case studies serve as a prototype for similar studies in any other areas.
Initial examples of the downstream use of the technologies in this project point to benefits from the project deliverables. Examples include the use of quantified relative errors to:
- support better investment decision making;
- contribute to cost savings for drilling programs;
- assist Competent Persons in better compliance with the JORC Code and technical defendability; (d) provide a more confident basis for long-term planning and project evaluation; and
- contribute to improved investor confidence. It is worth stressing that any ?risk' assessment based on an exploration dataset ?as is' can seriously underestimate risk and should be avoided. It should also be noted that traditional coal mine design and planning based on exploration information only implicitly assumes and reflects such risk underestimation.
The present report consists of two volumes. The first volume is divided into seven chapters. The current technological understanding of models used to classify coal resources and reserves, and the stochastic conditional simulation methods developed for the calculation of relative errors are described. A back analysis study is presented for the assessment of the methods developed for relative error calculation and risk quantification/drillhole spacing optimisation using
- a dataset from a mined out part of the Boomerang Pit at Peak Downs and
- production data from the same area. The results from the application of computerised risk quantification to relative error calculation at Peak Downs, Oaky Creek Mine and Cheshunt Mine are reported. Maps and graphs of quantified coal resource and coal quality uncertainty in the study areas are analysed and interpreted. Geological and mine planning issues that require further development are identified.
The main outcomes included a procedure for assisting Competent Persons to make objective resources classifications and quantified levels of confidence; technologies that significantly reduce coal mining investment risks; and a practical new toolkit for the Australian coal mining industry packaged as a software application. The second volume of this report aims to document practical guidelines for quantifying resource risk and facilitate technology transfer to mine sites. Volume II includes a user's manual and the CD contains a user-friendly computerised prototype model for resource risk quantification, uncertainty modelling and drillhole spacing optimisation methods developed in this study. The computerised model is presented in a software program named GeoCoal and can be described as a user-friendly stochastic resource risk quantification toolkit. A data set and detailed instructions are provided to assist users and document practical guidelines for quantifying uncertainty, transportable to any study area.