Clean Energy Capability Portfolio | Energy Markets |
Artificial Intelligence for Rock and Ore
Characterisation
An innovative platform for using advanced data analytics methods , based on convolutional neural networks and generative adversarial networks . These methods enable the prediction of important properties of rock and ore , to improve efficiency and promote automation in minerals and energy resources engineering . |
Competitive Advantage • Ability to determine mineral contents of rock / ore at high-resolution using machine learning
• Ability to predict porosity , permeability , and relative permeability curves of reservoir rock
• Leading the automatic identification of :
• patterns and features in rock / ore images
• fractures in drill cores
Impact • Automated analyses of drill cores for identifying features of interest
• Improved efficiency in the mining industry through the reliable determination of mineral contents
• High-fidelity reservoir models to optimise recovery
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More Information Professor Peyman Mostaghimi School of Mineral and Energy Resources Engineering T : + 61 2 9385 5122 E : peyman @ unsw . edu . au
Professor Ryan Armstrong School of Mineral and Energy Resources Engineering T : + 61 2 9385 5122 E : ryan . armstrong @ unsw . edu . au
Dr Ying Da Wang School of Mineral and Energy Resources Engineering T : + 61 2 9385 5122
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E : yingda . wang @ unsw . edu . au |
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Successful Applications |
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• Tested on several reservoir rocks where petrophysical properties were predicted with high accuracy |
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• Tested on multi-mineral rocks with mineral contents identified |
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Capabilities and Facilities |
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• Computational facilities for training and testing |
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• High-resolution X-ray CT scanners |
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