Carbon Capture and Storage (CCS) is potentially a game changer for CO2 mitigation, yet it is not widely used, in part because of significant risks of leakage to surface which are difficult to quantify. Top seal leakage occurs when any fracture or fault (new or pre-existing) opens through the sealing layer to allow CO2 to escape. This PhD project addresses sealing and leaking of topseals over CO2 storage sites by simulating both the fracture development (geomechanical simulation) and the fluid scenarios (fluid flow simulation) to identify safe and unsafe scenarios.
The PhD will use a novel physics-based machine learning technique to rapidly quantify geomechanical risks and uncertainties. The companion to fault seal studies from which it borrows widely, this work acknowledges that all topseals are damaged and that fracture-and –matrix flow systems are particularly prone to extreme response changes from minor mechanical oo fluid pressure changes. By embedding some of the system’s physics into the machine learning algorithm, the predictions should more realistically reproduce the essential mechanics of fracture development than a traditional machine learning approach would do, and be more interpretable. The machine learning predictions will represent the physical changes to the reservoir that influence flow by adjusting the fluid flow simulation model inputs (e.g. permeability) in a realistic way to allow more models to be run and so achieve a more accurate estimate of uncertainty.
Success in this project will create a way to properly evaluate geomechanical uncertainties and ultimately enable more CCS or geomechanically sensitive projects through better development planning and risk mitigation. The project aims to fix the current bottleneck to quantifying uncertainty in fracture development/opening/closing in shale and carbonaceous very low permeability topseals, reducing the unfeasibly large numbers of time-consuming coupled simulations. The proposed approach will provide a screening tool to identify the relevant physical mechanisms of the geomechanical deformation. Success will come from making uncertainty quantification of such systems feasible without the need for very large compute resources.
The research will focus on key geomechanical challenges around cap rock integrity, with relevance to reactivation of faults/induced seismicity and within-reservoir geomechanically sensitive responses. These challenges are important in many geoenergy contexts, ranging from porous petroleum reservoirs to fractured/faulted basement/granite as geothermal reservoirs (GWatt). This PhD is part of a wider Machine learning-geomechanics research agenda at Heriot-Watt and may link to future Industry funded PhD projects.
Eligibility
To be eligible, applicants should have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent experience).
We recognise that not every talented researcher will have had the same opportunities to advance their careers. We therefore will account for any particular circumstances that applicants disclose (e.g. parental leave, caring duties, part-time jobs to support studies, disabilities etc.) to ensure an inclusive and fair recruitment process.
How to Apply
Please complete our online application form. Please select PhD Applied Geoscience programme and include the project reference, title and supervisor names on your application. If these details are not included your application may not be considered. Please note that applicants may only submit ONE proposal.
Please also provide a supporting statement outlining how you would approach the research and upload this to the research proposal section of the online application. You will also be required to upload a CV, a copy of your degree certificate and relevant transcripts and one academic reference. Until your nominated referee has uploaded their statement, your application will not be marked as complete and will not be considered by the review panel.
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 or a TOEFL certificate with a minimum score of 90 points.
Timetable
Applications will be reviewed throughout March and applicants will be notified of the outcome of their application by the end of April 2021. Applicants MUST be available to start the course of study on a full-time basis in September 2021.
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