Geo-Energy with Machine Learning and Data Science MSc

The course leads of MSc GEMS will be holding an Online Information Session on 12th January 2027 for prospective students. Find out more.

In preparation for the energy transition, you will study subsurface geoscience and engineering, with a focus on data science and machine learning. You will develop skills that can be applied to carbon dioxide storage, water management, hydrocarbon recovery, geothermal energy and other subsurface processes.

The programme will strengthen your understanding of numerical, analytical and computational concepts, and is taught by experts in these areas. It is aimed at geoscientists and engineers who want to acquire advanced computational, data science, machine learning and numerical skills relevant to working on various aspects of the energy transition.

Find the most recent ‌course information and specifications on the Imperial MSc GEMS course page. 

Find answers to common questions about preparing for GEMS, choosing a research project and applying for the programme.

Who is the Geo-Energy with Machine Learning and Data Science MSc for?

• a background in petroleum engineering, geology, geophysics or other geoscience subject, and wish to learn about programming, data science and machine learning and how these can be used as modern data-driven problem-solving and analysis tools in energy transition; 
  • a strong methodological background in engineering or physical sciences and are wishing to move to, or specialise in, an applied field with an emphasis on subsurface site characterisation for energy transition. You will explore applications including carbon dioxide storage, geothermal energy, hydrogen storage, water management and hydrocarbon recovery; 
  • The programme also suits energy professionals, petroleum engineers, who want to strengthen their computational skills or move into new areas of the energy sector.

What background in geoscience is necessary?

You do not need a degree specifically in geoscience. Applicants with qualifications in physical sciences and engineering are also considered. The programme includes teaching in subsurface geology, geophysics and engineering. Your application should explain your interest in applying these subjects to energy challenge.

What background in programming is required?

The published entry requirements ask for evidence of coding experience. The programme includes Numerical Programming in Python, so developing familiarity with Python before starting will help you prepare. If your programming experience is limited or is in another language, contact the admissions team for advice about your preparation.

What background in mathematics is required?

You should demonstrate a strong quantitative background. This can be evidenced by at least grade A in A-level Mathematics or mathematics studied during your undergraduate degree. Calculus, linear algebra and statistics are particularly relevant to the computational mathematics, data science and machine learning taught on the programme.

Experience equivalent to A Level mathematics may be sufficient, provided that you can demonstrate intuition for solving mathematical problems.

What research project can I choose?

Your independent research project allows you to apply computational methods, data science or machine learning to a substantial problem. Projects span subsurface energy and storage, geoscience, engineering and related applications. You may choose from proposed projects or put forward your own idea for consideration, including ideas developed with an academic or an external company.

Previous GEMS research projects have included:

• 
  Carbon Capture Site Prospecting with Seismic Analysis and Machine Learning
  • Reservoir Simulation of Hydrogen Storage
  • Rapid modelling of groundwater sourced heating and cooling (GWHC) systems using Machine Learning
  • Forecasting injection-induced seismicity using machine learning techniques
  • Coupled Geomechanics and Fluid Flow Simulation for CO₂ Storage in Heterogeneous Reservoirs Using Physics-Informed Neural Networks (PINNs) for hazard
  • Machine Learning for Heterogeneous Well Data Integration and Leakage Risk Assessment
  • Machine-Learning-Assisted Geotechnical Characterisation of Subsurface Soils for Offshore Infrastructure
  • AI and remote sensing to support solar energy deployment
  • Data inpainting for ultrasound brain imaging
  • Martian and Lunar Simulant Selection Using Machine Learning and AI
  • Predicting the Future: LLM Context Analysis for Commodities Price Prediction – A Carbon Trading Case Study
  • Electricity Smart Meter Data, Smart Energy Consumption and Decarbonisation

Can I complete a project with an external company?

Yes. Across the 2022–23 to 2025–26 cohorts, more than half of GEMS projects involved external organisations. Some students are hosted at the company, while others remain based at Imperial and collaborate with the external organisation. Every student also has an internal Imperial supervisor.

We receive research project proposals from a range of companies and organisations. Previous contributors have included:

AIP Invest; Aramco; Artio; Beyond Mining; BP; CGG; Earth Science Analytics; European Space Agency; Getech Group plc; Ineos; InX Tech; Net-Zero Geosystems; North Sea Transition Authority, UK; SAND Geophysics; Shell; SLB; Sonalis Imaging Ltd.; S&P Global Commodity Insights; TGS; TotalEnergies; Vattenfall; Venterra; Weatherford.

Are scholarships available for the GEMS MSc?

You can explore funding opportunities through Department scholarships, Imperial’s scholarship search tool, postgraduate grants and scholarships pages, including information about external funding organisations.

Check each award’s eligible courses, entry year, criteria and application deadline. Funding opportunities and application arrangements vary between awards.

Is my academic background suitable for the GEMS MSc?

The usual minimum requirement is an upper second-class honours degree (2:1), or an accepted equivalent, in geological sciences, physical sciences or engineering. You should also provide evidence of mathematical preparation and coding experience. Applicants with other qualifications and relevant professional or industry experience may be considered individually.

Check the current entry requirements or contact adalovelace-admissions@imperial.ac.uk for advice about your qualifications.

What will the interview include?

 The interview is intended to give candidates an opportunity to find out more about the course from academics leading its design and delivery, as well as to give us an opportunity to learn more about the aspirations and background knowledge of prospective students. We will look for evidence of your background in mathematics and/or programming, and your intuition for solving geoscience problems. 

If you have further queries about the course, please contact Ying Ashton, Admissions Officer.