GeoDataScence group is involved in PhD projects short-listed by Iapetus  Doctoral Training Partnership open for applications:

• Modelling Natural Hydrogen systems using Agent Based modelling (IAP-24-121) supervised by Dr Daniel Arnold (HWU), Dr Simon Gregory (BGS), Prof Vasily Demyanov (HWU), Dr Uisdean Nicholson (HWU)
  In this project, we aim to pioneer a novel tool for natural hydrogen exploration, based on agent-based modelling (ABM), enabling the rapid screening of uncertainties in natural hydrogen systems. By modelling the interactions between fluid, rocks, and the subsurface biome as simple agents that interact with each other, we can efficiently capture the system’s complex behaviours at a low computational cost. A working tool will help to discover more potential reservoirs of naturally trapped/accumulated hydrogen and derisk drilling into these new resource plays to access this critical energy transition fuel. The key benefit of our approach is its high computational efficiency in capturing the complex migration of hydrogen as it interacts with the geology and the microbiome.
  We will build upon previous work by the Heriot-Watt (HWU) supervisory team on ABM of petroleum migration, as outlined in Steffens et al (2022), later demonstrated on the Wessex basin petroleum system by Kreiensiek et al. (2022). Our goal is to adapt our existing ABM code to incorporate a hydrogen-migrating agent, as well as secondary agents such as hydrogen-consuming microorganisms, and to model the interactions between them. This will require a multi-scale approach to accurately capture microbiome interactions at the small pore scale and then translate those insights into bulk behaviour. The models will use formal rules or reinforcement machine learning (ML) to represent these behaviours. To better capture migration pathways through geological formations, we will enhance our ABM (which constructs geological layers directly from seismic data) by incorporating the more rigorous GEMpy python framework (de la Varga et al., 2019).
  For more details and to apply.

  Can artificial intelligence improve flood risk management? (IAP2-23-130)  supervised by Professor David Copplestone (University of Stirling), Dr Joanna Wragg (BGS) , Professor Vasily Demyanov (HWU), Dr Clare Wilson (University of Stirling)

  Aimed to:

  1) Address the challenges of predicting contaminant mobility and bioaccessibility changes within flood risk areas at a national/landscape scale using artificial intelligence modelling.
  2) Make AI based predictions (as maps) of hazard and risk of remobilisation of contaminants nationally.
  3) Undertake fieldwork to test and validate the AI based predictive maps.

  Initially, spatial databases containing geochemical and flood risk information belonging to BGS, the Environment Agency and the Scottish Environment Protection Agency (SEPA) will provide national scale inputs into the AI tool(s). These will be supported then by targeted fieldwork with subsequent laboratory experiments and sample analysis to determine the drivers influencing contaminant mobility and bioaccessibility, which will be used to train artificial intelligence modelling tools. Key parameters are likely to be soil and water physiochemical properties such as pH and organic carbon. These factors highly influence aspects for example such as i) contaminant ion exchange with soil and sediments, ii) the dissolution of oxides (e.g., iron), which binds with contaminants, and iii) the formation of metal sulfide complexes. These factors all impact a contaminant’s mobility and bioaccessibility. These soil physiochemical properties differ spatially, influenced by soil texture, geology and mineralogy, anthropogenic inputs, and flood frequency. Such heterogeneities in spatial features make modelling changes in contaminant mobility and bioaccessibility complex, which is where artificial intelligence comes in to address these highly unstructured data.
  The PhD will involve a blend of field and laboratory studies as well as sample analysis, processing and modelling.
  For more details and to apply.