@phdthesis{Tranter2022,
  author    = {Tranter, Morgan Alan},
  title     = {Numerical quantification of barite reservoir scaling and the resulting injectivity loss in geothermal systems},
  doi       = {10.25932/publishup-56113},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-561139},
  school      = {Universit{\"a}t Potsdam},
  pages     = {131},
  year      = {2022},
  abstract  = {Due to the major role of greenhouse gas emissions in global climate change, the development of non-fossil energy technologies is essential. Deep geothermal energy represents such an alternative, which offers promising properties such as a high base load capability and a large untapped potential. The present work addresses barite precipitation within geothermal systems and the associated reduction in rock permeability, which is a major obstacle to maintaining high efficiency. In this context, hydro-geochemical models are essential to quantify and predict the effects of precipitation on the efficiency of a system. The objective of the present work is to quantify the induced injectivity loss using numerical and analytical reactive transport simulations. For the calculations, the fractured-porous reservoirs of the German geothermal regions North German Basin (NGB) and Upper Rhine Graben (URG) are considered. Similar depth-dependent precipitation potentials could be determined for both investigated regions (2.8-20.2 g/m3 fluid). However, the reservoir simulations indicate that the injectivity loss due to barite deposition in the NGB is significant (1.8\%-6.4\% per year) and the longevity of the system is affected as a result; this is especially true for deeper reservoirs (3000 m). In contrast, simulations of URG sites indicate a minor role of barite (< 0.1\%-1.2\% injectivity loss per year). The key differences between the investigated regions are reservoir thicknesses and the presence of fractures in the rock, as well as the ionic strength of the fluids. The URG generally has fractured-porous reservoirs with much higher thicknesses, resulting in a greater distribution of precipitates in the subsurface. Furthermore, ionic strengths are higher in the NGB, which accelerates barite precipitation, causing it to occur more concentrated around the wellbore. The more concentrated the precipitates occur around the wellbore, the higher the injectivity loss. In this work, a workflow was developed within which numerical and analytical models can be used to estimate and quantify the risk of barite precipitation within the reservoir of geothermal systems. A key element is a newly developed analytical scaling score that provides a reliable estimate of induced injectivity loss. The key advantage of the presented approach compared to fully coupled reservoir simulations is its simplicity, which makes it more accessible to plant operators and decision makers. Thus, in particular, the scaling score can find wide application within geothermal energy, e.g., in the search for potential plant sites and the estimation of long-term efficiency.},
  language  = {en}
}