2024 Capability Portfolio Digital | Page 78

Clean Energy Capability Portfolio | Storage Technologies

Energy and Mineral

Exploration

Machine learning can help unlock the secrets contained in vast amounts of geoscientific data about the subsurface for resources extraction , civil and environmental engineering , and the application of emerging technologies , such as underground hydrogen storage and CO2 geosequestration .

Competitive Advantage

• Machine learning techniques can build subsurface models in minutes , rather than weeks
• Greater accuracy in analysis using all available data to create models
• Expertise in the optimisation of infrastructure placement

Impact

• Reduced expenses and environmental impact in the collection of data , extraction or storage of energy resources , and the placement of facilities and infrastructure
• More accurate mapping of subsurface geology
• Reducing the labour-intensity required in the interpretation process

Successful Applications

Capabilities and Facilities

• Evaluation of frontier basin properties and energy or storage potential , especially where little data currently exists
• Software for automated interpretation of seismic data

Our Collaborators

• Lundin Norway
• SoluForce
• Department of Climate Change , Energy , Environment and Water ( DCEEW )
• Geoscience Australia
• CO2CRC
• EXIGE
• Santos
• Kumul Petroleum
• Automated interpretation of complex seismic datasets from the environmentally sensitive Barents Sea region in the Arctic
• Constraining the timing of major basin evolutionary processes in several basins , including the North West Shelf and South Nicholson Basin , Australia , Siberia , East Africa , the Caribbean , and the Scotia Sea
• CSIRO Mineral Resources

More Information

Dr Stuart Clark Minerals and Energy Resources Engineering T : + 61 468 332 796
E : stuart . clark @ unsw . edu . au

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