Nanoparticle tracers are being investigated as a potential tool to measure or infer temperature distributions in geothermal reservoirs. If the temperature distributions could be measured more precisely, this would greatly enhance the power and accuracy of thermal breakthrough forecasting, which would in turn inform reservoir engineering and field management decisions. The overall objective of this work is to rank the informativity of various tracer candidates using modeling to aid in the design of smart nanotracers. The design of temperature-sensitive tracers is built around the design of the temperature sensing mechanism. In other words, the mechanism by which temperature is measured and the form and resolution of the resulting data, or response, have a profound impact on how informative that tracer can be about thermal breakthrough. Therefore, it is important to model the responses of candidate tracers in the context of an inverse problem to determine their relative informativity. A framework for inverse modeling was constructed in order to match synthetic data provided by conservative solute tracers (CSTs) and hypothetical temperature-time tracers (TTTs) with the objective of comparing the two classes of tracer in the context of the ability to forecast thermal breakthrough. Optimizations were performed for both classes of tracer, and the thermal breakthrough curves for the optimal fracture networks were calculated using reservoir simulation. The optimal solution to the TTT problem was found to match the topology and thermal breakthrough behavior of the true fracture network better than that of the CST problem. This is because the CST problem is less unique than the TTT inverse problem in that the CST data can be matched very well by a fracture network with a different topology from the true fracture network.

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