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Numerical modelling and design of geothermal heat recovery from subsurface systems

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

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 there is significantly a greater scope to optimize well placement strategies on a regional/geological scale.
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. In this project the added value of model based geothermal field development optimization at the regional scale is explored and presence of uncertainty and realistic heterogeneity are considered.

The project benefits from direct collaboration with DTU (Technical University of Denmark).
For further information contact Masoud Babaei at

Applicants should have or expect to achieve at least a 2.1 honours degree in…

Mechanical Engineering, Chemical Engineering, Petroleum Engineering

Funding Notes

Project is open for self-funded students

How good is research at University of Manchester in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?
Chemical Engineering

FTE Category A staff submitted: 33.90

Research output data provided by the Research Excellence Framework (REF)

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