Agent Based modelling of natural hydrogen systems for resource discovery and evaluation

Why Natural Hydrogen?

Natural hydrogen, also known as 'gold hydrogen', represents an untapped and potentially enormous clean, sustainable energy source. Formed deep underground through natural geochemical reactions, it migrates and traps in reservoirs like hydrocarbons, as shown by recent discoveries in Australia, France, and the USA. Yet the processes that govern its formation, movement, and accumulation remain poorly understood, and specialised tools for exploration are still lacking, which is holding back this potentially important industry.

This PhD project addresses that gap by creating novel simulation tools to explore and evaluate natural hydrogen systems using agent-based modelling (ABM), a form of AI based that models complex interactions in systems with simplified rules/behaviours. This makes them powerful and interpretable. This PhD, part-funded by a Hydrogen exploration company, H2AU, will develop ABM models on real data to guide real-world exploration and reduce risks, ultimately advancing natural hydrogen toward commercial viability.

The Project

At the heart of this project is the development of an ABM framework that treats rocks, fluids, and microbes as individual agents interacting within a dynamic subsurface system. This approach captures complex geological and microbial processes while remaining computationally efficient, an essential advantage over complex basin migration models.

A critical aspect of the model is how we will integrate the effects of microbial hydrogen consumption using agents. Microbial life, including sulfate-reducing bacteria, methanogens, and acetogens, can significantly impact hydrogen availability, but current migration/exploration models do not account for their effects. Building on established microbiological data, we will incorporate the effect of microbiome rule-based logic or reinforcement learning.

To ensure geological realism, the model will be built directly from seismic interpretations and other geological data, using the open-source GEMpy Python library. We will enhance this open source model using our own ABM models, which will be adapted to hydrogen in this PhD. The result will be a novel tool capable of simulating hydrogen migration and retention across a range of geological settings. 

The final phase of the project will involve applying the model to an active hydrogen exploration area, in partnership with H2Au. This real-world case study will allow you to test, validate, and refine your modelling framework, demonstrating its potential to inform future hydrogen exploration efforts.

What Makes This Project Unique?

This is a rare opportunity to work across disciplines at the cutting edge of energy and Earth sciences. The supervisory team combines expertise in machine learning, uncertainty quantification, subsurface modelling and simulation, microbiology, and hydrogen exploration from Heriot-Watt University and the British Geological Survey. You’ll be supported by a team with deep expertise in data-driven modelling, geological exploration, microbiology and subsurface modelling, with access to computing resources and data through our industry collaboration.

References

Can agents model hydrocarbon migration for petroleum system analysis? A fast-screening tool to de-risk hydrocarbon prospects“ by Bastian Steffens, Quentin Corlay, Nathan Suurmeyer, Jessica Noglows, Dan Arnold and Vasily Demyanov, in MDPI Open Access Energies special issue on “Machine Learning in Reservoir modelling workflows”

Supervision

The successful candidate will be supervised by Dr Dan Arnold, Dr Uisdean Nicholson, Prof Vasily Demyanov, and Simon Gregory (BGS)

You will be based at Heriot-Watt University in Edinburgh, working closely with collaborators at the British Geological Survey, and our industrial partner at H2AU.  

Eligibility

This scholarship project is open to Home fee payers only.

Applicants must possess first or upper second-class Honours degree (or equivalent) and preferably a masters degree.

As this PhD is very mixed disciplinary we can recruit from a wide variety of backgrounds. 

We are looking for a motivated graduate from any of the following fields:

• Geology or geophysics
• Engineering (especially subsurface or energy-related disciplines)
• Microbiology or environmental sciences
• Machine learning or data science

We can tailor the project to suit your background, be it, geology, engineering or biology. This project offers the chance to apply your skills to a truly multidisciplinary project. Programming experience (e.g., Python) is an advantage but not essential.

Funding

This is a fully funded PhD opportunity for UK applicants only. Funding includes:

• Full tuition fees for 42 months. Thereafter, if you have not submitted your thesis you will be expected to pay a continuing affiliation fee (currently £130) whilst completing writing up.
• A competitive stipend paid in line with UKRI recommended rates, with a 10% uplift (£22,858 in 2025/26) for 42 months
• Support for conference travel, training, and research activities
• Field work funding (where necessary)
• Computer resource funding (where necessary)

How to Apply

To apply you must complete our online application form.

Please select PhD GeoEnergy Engineering as the programme and include the full project title, reference number (EGIS2025-DA) and supervisor name on your application form. Ensure that all fields marked as ‘required’ are complete.

Once you have entered your personal details, click submit. You will be asked to upload your supporting documents. You must complete the section marked project proposal; provide a supporting statement (1-2 A4 pages) documenting your reasons for applying to this particular project, outlining your suitability and how you would approach the project. You must also upload your CV, a copy of your degree certificate and relevant transcripts and an academic reference in the relevant section of the application form.

You must also provide proof of your ability in the English language (if English is not your mother tongue). We require an IELTS certificate showing an overall score of at least 6.5 with no component scoring less than 6.0, or a TOEFL certificate with an overall score of at least 85, including reading 20, listening 19, speaking 20 and writing 21. Alternatively, if you have received an English-taught Bachelors or Masters degree from one of the countries listed on the UK Government Guidance under ‘Who does not need to prove their knowledge of English’, and it was obtained less than two years from your intended start date, you should provide evidence of your award that clearly states it was delivered and assessed in English language.

Please contact Dr Dan Arnold (d.p.arnold@hw.ac.uk) for further information or an informal discussion.

Please contact egis-pgr-apps@hw.ac.uk for technical support with your application.

Timeline

The closing date for applications is 25th July 2025 and applicants must be available to start in September 2025 or January 2026. We expect interviews to take place in August 2026.