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Machine Learning-Enhanced Modelling and Simulation of Subsurface Reservoirs

  • Full or part time
  • Application Deadline
    Monday, April 15, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The School of Energy, Geoscience, Infrastructure and Society at Heriot-Watt University (HWU), is looking for a PhD candidate to work on an industrially funded project at the interface of machine learning and subsurface flow modelling. Multi-phase flow simulation codes tries to model complex physical phenomena with huge variations in scales within a single reservoir. Adding to that the uncertainties associated with the spatial subsurface properties (e.g. permeability and porosity), one is then faced with a statistical uncertainty quantification problem requiring a large number of simulation runs using an expensive computer model. In realistic settings, this problem is usually intractable due to the massive computational costs.

This project builds on two research themes developed at HWU: supervised machine learning of efficient emulators of physical models [1, 2] and unsupervised representation learning of spatial models [3] for compact representation of complex and high-dimensional spaces. The combined use of these two approaches provides a robust pathway to quantifying uncertainties in large-scale subsurface flow models. This project will investigate the statistical consistency and physical plausibility of the machine learning models. The aims is to address problems associated with learning using limited training data and handling spatial non-stationary fields.

Essential skills:
• Master’s degree in computational mathematics, physics or in a relevant engineering discipline with strong computational skills.
• Programming skills preferably in Python and/or C++
• Ability to write reports, collate information and present it in a clear and engaging manner.
• Excellent communication skills.

Desirable skills:
• Machine learning techniques (theory and applications)
• Background in computational statistics (Spatial Geo-statistics, Bayesian techniques)
• Numerical optimization and nonlinear partial differential equations solvers (FEM, FVM, etc.)

Please complete our online application form, select PhD programme in Petroleum Engineering and include the reference ‘EGIS2018AE’ on your application. You will also need to provide a detailed CV, a covering letter including areas of expertise and research interests, degree certificates and relevant transcripts, a verifiable list of programming skills and one academic reference. If you are an overseas applicant, you must also provide proof of your ability in the English language (if English is not your mother tongue or if you have not already studied for a degree that was taught in English). We require an IELTS certificate showing an overall score of at least 6.5 with no component scoring less than 6.0.

Funding Notes

Funding is available to UK/EU/Overseas candidates. It includes tuition fees and an appropriate stipend for 3.5 years at the EPSRC recommended level.


[1] J. Nagoor Kani, Ahmed H. Elsheikh, Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks, Transport in Porous Media, (2019) 126: 713.
[2] Shing Chan, Ahmed H. Elsheikh, A machine learning approach for efficient uncertainty quantification using multiscale methods, Journal of Computational Physics, (2018) Volume 354, Pages 493-511.
[3] Shing Chan, Ahmed H. Elsheikh, "Parametric generation of conditional geological realizations using generative neural networks",

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