This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/
Project Background
In earth science for hydrocarbon and mineral exploration, determining subsurface and source properties from seismic traces are challenging tasks, commonly known as the full-waveform inversion (FWI) and seismic source inversion, respectively. Often, seismic data are buried under significant amounts of ambient noise and combined with uncertainties in the geological model which complicates the inversion process. Both the FWI and source inversion are important aspects of subsurface monitoring to constrain changing material properties and evolving stress-fields of large geological models. Such inverse problems usually employ Monte Carlo simulation frameworks, requiring thousands of forward simulations on large complex geological models, which demand significant computing time and resource. This project will aim to accelerate this process using recent advances in Bayesian inference and machine learning, especially utilizing deep learning and deep Gaussian process models. Stress accumulation and fluid flow movement monitoring in reservoir needs complex geophysical and petrophysical simulations using known velocity models, permeability, permittivity etc. Efficient management and processing of such large volumes of synthetic seismic and petrophysical data in a probabilistic geophysical inversion, imaging process, history matching and uncertainty quantification is an open challenge, with outcomes that will benefit both industrial and academic research.
Synthetic seismograms for random microseismic events
Project Aims and Methods
This project will explore advanced signal/image processing, machine learning approaches, in particular, deep-learning and Bayesian inference for parameter/uncertainty estimation and probabilistic inversion of large-scale geological models fusing geophysical/seismic and petrophysical data. Here, the aim is to reduce computational time utilising the recent advancements in deep neural networks and deep Gaussian processes to approximate the physical data generation process, i.e. the seismic wave propagation and reservoir fluid flow simulations. The concepts from seismic interferometry will also be used for turning highly noisy traces into useful interpretable signals using various correlation-based methods. Quantification of the uncertainties in such geophysical inverse problems in terms of both seismic source properties and unknown elastic geological models (density, compressional and shear wave velocity) and petrophysical parameters like permeability, porosity etc. is a complex problem. Geophysical inverse problems rely on the travel-time calculation between sources and receivers. However, uncertainties in the velocity model can make these estimates highly erroneous. Alternatively, a full seismic wave based inversion can be attempted for improved imaging, albeit being computationally challenging. The project will also explore the inversion results of 3-component geophone recordings apart from pressure measurements by hydrophones in a marine environment. The traditional inversion or seismic imaging methods involve a series of heuristic filtering steps that can be more optimally selected using a deep machine learning based expert system.
Candidate requirements
The candidate will have a good master’s degree in any of the following disciplines – Mathematics/Statistics, Computer Science, Physics, Engineering, Geophysics or Earth-sciences. Good analytical, computational skills and in particular, some prior experience in Matlab/Python/R programming is necessary. Some previous research experience in big data analytics, large scale scientific computing is also desirable. Prior experience on high-performance computing system especially on GPUs, clouds will be advantageous.
Project partners
The student will be based in the University of Exeter, Penryn Campus and will also closely collaborate with the University of Bath. This will be in the form of sharing methods for analysing seismic and petrophysical datasets, estimating geological model parameters and uncertainties, communicating the research findings with the researchers in Bath, develop better understanding and interpretations of seismic, geophysical and petrophysical data analysis in a broader industrial/academic context, and writing collaborative publications.
Training
The student will receive the required training to pursue fundamental and applied research in this project on geophysics, petrophysics, seismology and will have the opportunity to further develop Mathematical and computational skills. The student will exchange research findings and methods with their peers and other researchers through regular seminars and conference presentations. The project is inter-disciplinary in nature, so the student will have the opportunity to learn and discuss with other researchers both from mathematical and computational geoscience. It is expected that the student will also strengthen the existing collaborations of the supervisors with the Oil and Gas, Geoscience/mining industries, and other stakeholders.
Useful links
For information relating to the research project please contact the lead Supervisor via https://emps.exeter.ac.uk/mathematics/staff/sd565
How to apply
In order to formally apply for the PhD Project you will need to go to the following web page.
https://www.exeter.ac.uk/study/funding/award/?id=4257
The closing date for applications is 1600 hours GMT on Friday 10th January 2022.
Interviews will be held between 28th February and 4th March 2022.
If you have any general enquiries about the application process please email [Email Address Removed] or phone: 0300 555 60 60 (UK callers) or +44 (0) 1392 723044 (EU/International callers). Project-specific queries should be directed to the main supervisor
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