Lifetime optimisation of multiple deep closed-loop geothermal wells

Worldwide interest in geothermal energy is increasing as part of the solution mix in the prevailing energy and environmental crisis. Geothermal energy has a high capacity factor compared to solar and wind, hence being able to provide a predictable 24/7 supply of thermal energy that can be used to meet the base load in direct heat to heat applications or produce electrical power in conjunction with, for example, Organic Rankine Cycle plants. In addition, the geothermal footprint (m2/energy produced) is significantly smaller than that required by wind and solar installations making it a more attractive option both in terms of land use and social acceptability.

Closed-loop geothermal systems as proposed here have the benefit of being available/installed anywhere and remove the exploration risks associated with open loop systems. In such systems the fluid is contained in coaxial heat exchangers hence, removing any environmental/societal concerns associated with water extraction and injection (fracking). Recently, the option and financial viability of using geothermal energy has been improved in line with the need to utilise abandoned oil and gas well, in addition to the plans for new ones.

A single geothermal well has a radial area of influence for the transfer of thermal energy between the rock formation and the well bore. This is affected by the thermal diffusivity and conductivity of the geology and the amount of energy extracted. Over time, heat extraction leads to a reduced thermal output of a well. Therefore, multi-well developments as the ones proposed to be studied in the first part of this PhD programme can aid in thermally recharging wells using intermittent full load hours (variable heat flux and transient heat conduction) or switching between wells to allow for the original formation (to the extent of the radius of influence of the well) to recuperate heat at the well bore.

One recurrent issue with geothermal funding is the upfront cost associated with new bore holes. However, geothermal energy offers a lower cost of energy (LCOE). Increasing the overall geothermal plant lifetime for a given site/application by using multiple wells will improve the LCOE, making geothermal an even more attractive option in the drive to net-zero.

Machine learning can aid in decision-making and problem-solving for optimisation and control processes without human interference. In the second part of the work, the research programme will include the development of a deep learning optimisation framework to control geothermal production and to determine optimum intermittency, working flow rates and targeted return fluid temperatures for increased lifetime and production, while also investigating the effects on thermal degradation due to different well architectures and geologies.