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Integrating conditional simulation and stochastic programming: An application in production scheduling
Published in Heping Xie, Yuehan Wang, Yaodong Jiang, Computer Applications in the Mineral Industries, 2020
The mining industry has a pressing need to incorporate uncertainty into production scheduling and mine planning by integrating CS, MIP/SIP into a unified methodology capable of producing robust mine plans. A proven optimisation methodology that quantifies geologic uncertainty will enhance mine profitability and mineral recovery and improve the investment climate for mining projects by quantifying risk. The new Australian JORC Code (JORC, 1999) requires that resource reporting must be related to the level of geologic confidence, i.e., geologic uncertainty must be quantified for all minerals companies listed on the ASX. The JORC criteria are being adopted as the world-wide standard, yet there is currently no standard for quantifying geologic uncertainty nor any means of incorporating uncertainty into production schedules and mine design. The only direct means of quantifying geologic uncertainty is with CS methods. Thus, there is a tremendous surge of interest in stochastic modelling motivated not only by the potential to improve mineral recovery but also from a need to improve the investment climate for mining ventures.
Accounting for the extractive industry
Published in Odwyn Jones, Eric Lilford, A.J.S. Spearing, Grantley Taylor, The Business of Mining, 2019
Odwyn Jones, Eric Lilford, A.J.S. Spearing, Grantley Taylor
The Joint Ore Reserve Committee Code (JORC Code) sets out guidelines for public reporting in Australia of exploration results, mineral resources and ore reserves (The JORC, 2012). Exploration results include data and information generated by mineral exploration programmes that might be of use to investors. A mineral resource is a concentration or occurrence of solid material of economic interest. Public reporting (e.g. annual reports) of mineral resources must satisfy the requirement that there are reasonable prospects for eventual economic extraction (i.e. more likely than not), regardless of the classification of the mineral resource. An ore reserve is the economically mineable part of a measured and/or indicated mineral resource. Resource and reserve assets account for a significant part of the equity value and profits of resources firms (Taylor et al., 2012). In fact, the disclosure of resource and reserve valuations and mine life or petroleum reservoir parameters are important in forecasting earnings, cash flows and growth potential for resource firms and may therefore be a significant determinant of their share prices. However, there is no Australian accounting standard that mandates disclosure of reserve parameters.
Towards quantifying uncertainties in geological models for mineral resource estimation through outside-in deposit-scale structural geological analysis
Published in Australian Journal of Earth Sciences, 2023
According to Canadian Institute of Mining guidelines (Pressacco et al., 2019, p. 13): “… the final wireframe models must be examined to ensure that they are a reasonable reflection of the current understanding of the deposit’s geological, alteration, weathering and structural features, do not contain unrealistic artifacts, and honour the informing data as closely as practical” and, additionally, “Practitioners should ensure that the final resource block model is consistent with the primary data such as the geology and mineralisation wireframe models, and structural models” (Pressacco et al., 2019, p. 22). Similarly, the Australasian Joint Ore Reserves Committee’s (JORC) guidelines (JORC, 2012) direct the Competent Person to discuss the confidence, or uncertainty, of the geological interpretation and its impact on the resource estimate—the Competent Person is ultimately responsible for the software program’s output and should not blame the software.
Assessing the Lithium Potential of the Paleoproterozoic Rocks of the West African Craton; the Case so Far
Published in Geosystem Engineering, 2023
The initial mineral resource estimation for the Ewoyaa mine in Ghana, conducted according to JORC (Joint Ore Reserves Committee) standards, was 14.5 metric tonnes with an average grade of 1.31% Li2O for inferred and indicated resources. However, Atlantic Lithium (formerly IronRidge Resources) has recently reported an increase in the lithium resource by approximately 50% to 35.3 metric tonnes, with the same lithium oxide grade of 1.25% (Atlantic Lithium, n.d.). This updated estimation does not include approximately 28,000 meters of infill and extensional drilling, which suggests that the resource size could be even larger than anticipated. Atlantic Lithium has entered into a legally binding agreement with Piedmont Lithium to finance the entire Ewoyaa lithium project in Ghana. The company states that the metallurgical test work conducted thus far has consistently produced high-purity spodumene concentrate (>6% Li2O) with low levels of contaminants. This bodes well for the project’s potential to have low capital and operating costs, as well as low carbon intensities (Atlantic Lithium, n.d.).
A novel geostatistical index of uncertainty for short-term mining plan
Published in CIM Journal, 2023
G. M. C. Dias, M. M. Rocha, V. M. Silva
Evaluating uncertainty is required as best practice in mineral resource estimation. Uncertainties in any of the criteria that could lead to under- or over-statement of mineral resources should be disclosed (Joint Committee of the Australasian Institute of Mining and Metallurgy, 2012). The Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves, known as the JORC Code, also states that the choice of the appropriate category of ore reserve is determined primarily by the relevant level of confidence in the mineral resource and after considering any uncertainties in modifying factors. Therefore, methods of geostatistical simulation are starting to be more widely accepted and implemented in recent decades to quantify uncertainty and classify mineral resources. Conditional simulation is an ideal tool for uncertainty and risk assessment: the risk and uncertainty can be determined as a probability distribution assembled from the drawn realizations (Emery, Ortiz, & Rodríguez, 2006). The simulated nodes can be averaged into blocks or production areas of any shape to assess their uncertainty.