Thermal Forecasting Ability of Temperature-Sensitive Tracers

Abstract

The development of a reliable and accurate method to predict thermal breakthrough time is a significant open problem in geothermal reservoir engineering. Such a method would enable more informed decisions to be made regarding reservoir management. Methods developed at present include analytical models and solute tracers, both of which have limitations. The use of particles as temperature-sensitive tracers is a promising approach due to the high degree of control of the physical and chemical properties of nanomaterials and micromaterials. This could potentially be exploited to infer temperature and measurement location, which could in turn provide useful information about thermal breakthrough. In order to assess whether particle tracers can provide more useful information about future thermal behavior of reservoirs than existing solute tracers, models were developed for both solute tracers and particle tracers. Three existing solute tracer types were modeled: conservative solute tracers (CSTs), reactive solute tracers with temperature dependent reaction kinetics (RSTs), and sorbing solute tracers that sorb reversibly to fracture walls (SSTs). Additionally three particle tracers which have not yet been developed in practice were modeled: dye releasing tracers (DRTs) that release a solute dye at a specified temperature threshold is reached, threshold nanoreactor tracers (TNRTs) with an encapsulated reaction that does not begin until a specified temperature threshold is reached, and temperature-time tracers (TTTs) capable of recording detailed temperature-time histories of each particle. In this study, TTTs represent the most informative tracer with respect to thermal breakthrough. These models were used in the context of an inverse problem in which synthetic tracer data was calculated for several “true” discrete fracture networks. Computational optimization was used to match the location, length, and orientation of a variable number of fractures. Finally, the thermal behavior of the fracture networks with the best fit to the data was compared to that of the true fracture networks, and the forecast accuracy was compared for all tracer types.