This paper presents the ongoing development of a chloride-based wireline tool designed to detect and quantify inflows from feed zones in geothermal wells. The tool primarily aims to characterize stimulation events in EGS wells at Utah FORGE (Frontier Observatory for Research in Geothermal Energy) and other EGS sites. Successful development of the chloride tool would greatly improve production monitoring of the fractures and enable proactive prescrption of additional stimulations over the life of the field, thus helping to improve EGS commercial feasibility. The recent developments of the chloride tool involve conducting updated laboratory experiments and testing under high-pressure and high-temperature conditions. To enhance the precision of the chloride concentration readings, numerical modeling scenarios were run against various fluid mixer and tool placement designs. Multiple feed zone cases have been investigated through experiments and numerical modeling. Moreover, data-driven approaches utilizing machine learning (ML) techniques were investigated to construct a reliable predictor for feed zone presence and inflow rates. The ML models were trained against simulated data from the numerical modeling cases and measured data from laboratory experiments. Sandia National Laboratory is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

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