DeepScale - Deep geothermal flow assurance; cost-efficient scale handling and heat fluid robustness.

In some homes, pipes and kettles may suffer from mineral scaling. This is because minerals such as calcium carbonate comes into the home dissolved in the tap water, but precipitates out onto say, the inside of a kettle boiling water. This problem is more common when the water supply is groundwater. Deep geothermal wells draw on hot water and steam from far below to power geothermal power plants but the fluid also carry dissolved minerals to the surface. Like with the inside of the kettle, these minerals may coat surfaces in the geothermal power plant, forming a solid layer known as "scale". The minerals can build up until they block pipes and equipment completely. Because laboratory experiments had not been able to explore the high pressures and temperatures involved, scientists do not know exactly when and how minerals dissolve down in the well and have not been able to advice the owners of the wells. The DeepScale project however, has now built equipment in the laboratory that can withstand the high pressures and temperatures. In 2021, the project has studied dissolving minerals at high pressures and up to 400 degrees Celcius. In 2022, the project will explore the range up to 500 Celcius, a range not previously seen in the lab for these minerals.

When producing hot water from deep geothermal wells, the change in pressure and temperature may lead to precipitation of dissolved minerals. This can pose operational or maintenance challenges for geothermal power plants when these minerals grow as a solid layer on the walls, a phenomenon known as scaling. Scaling problems incur extra costs, dealing with scaling is costly and scaling could in a worst case scenario lead to loss of the well. Our understanding of scaling is incomplete, not least because of the challenges of doing high-temperature experiments. This project will study scaling in topside DGE process conditions and develop thermodynamic formalism, implemented in computer models, to predict how and when scaling may occur.