Sedimentary basins are ubiquitous, naturally porous and permeable, and the geothermal heat in these basins can be extracted with geologic water or CO2 and used to generate electricity. The UK must decarbonise heating for it to meet its commitments on emissions reduction. UK heat demand can be met from ultra-low-carbon, low enthalpy geothermal energy. In the geothermal energy technology, this is a common practice to drill a doublet (i.e. an injection and production well pair) system. The well spacing distance is usually chosen through engineering judgement. There is, however, a need for improved well placement strategies as in view of optimizing the net energy gained through either use of water or CO2.
Model based optimization strategies for well location, trajectory and thereby spacing are commonly practiced in the oil and gas industry. Using this background, optimization of well spacing can be extended and implemented for doublet design. Additionally, due to the usually large uncertainty present in the subsurface it is essential to also account for geological uncertainties during optimization. In this framework, geological uncertainties are accounted for through an ensemble of equiprobable geological models. Therefore, a single robust solution of well locations is to be found, which is optimal in terms of an expected objective function value over the ensemble of models. The practice for CO2 geothermal systems will produce guidelines for industry.
The project benefits from direct collaboration with Schlumberger using ECLIPSE geothermal simulator, and with Durham Energy Institute (Prof Jon Gluyas).
Applicants should have or expect to achieve at least a 2.1 honours degree in degree in Mechanical Engineering, Chemical Engineering, Civil Engineering, or Petroleum Engineering.
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You MUST contact the lead supervisor for this project - Dr Masoud Babaei firstname.lastname@example.org - before you apply.
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When applying, you’ll need to specify the full name of this project, the name of your PhD (PhD Chemical Engineering and then PhD Multi-scale Modelling), the name of your supervisor, how you’re planning on funding your research, details of your previous study, and names and contact details of two referees.
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