GMDSI sponsors three PhD students who have spent time in industry, and intend to return to industry. Their research will focus on improving concepts and technologies that support the use of models in real-world decision-making.
- improved uncertainty analysis where hydraulic properties are structurally controlled
- appropriate complexity for decision-support modelling in different geological and decision-support contexts
- greater numerical efficiency in history-matching and in direct predictive hypothesis testing
- optimisation of context-specific methodologies for data assimilation and uncertainty analysis
- optimisation under uncertainty.
We are proud to introduce:
Tomás holds a masters degree in hydrogeology. He has had 11 years experience in groundwater modelling and site characterisation for the evaluation of open pit mine dewatering/depressurisation strategies, estimation of lithium reserves in Salars, and environmental impact assessment. His research interests include optimisation of groundwater modelling workflows, including strategies for appropriate model simplification, parameterisation, history matching, and uncertainty analysis.
Neil has spent the past 13 years developing numerical groundwater models for water resource assessment and environmental approvals of coal, iron ore and gold mines, as well as coal-bed methane extraction. He skills and interests include programming, linear/non-linear optimisation, uncertainty analysis, drilling supervision, and construction of monitoring bores/VWPs.
Iain holds a master’s degree in petroleum engineering, and has spent the last 5 years working on the reservoir modelling aspects of projects at the University of Queensland’s Centre for Natural Gas. This has included work relating to the modelling of coal seam gas production and carbon dioxide sequestration, and Iain has become increasingly focused on the groundwater related aspects of these industries. Iain’s main interests lie in the relationship between, and integration of, modelling and decision making, particularly when uncertainty must be addressed.
Banner photo: BHP