Publications Details

Chloride-based Wireline Tool for Measuring Feed Zone Inflow in Enhanced Geothermal Systems (EGS) Wells: Experimental, Numerical, and Data-driven Updates

Sausan, Sarah; Judawisastra, Luthfan H.; Su, Jiann-Cherng S.; Horne, Roland

This paper presents the ongoing development of a wireline tool designed to detect and quantify inflows from feed zones in geothermal wells based on measurement of chloride. The tool aims to characterize stimulation events in Enhanced Geothermal Systems (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 prescription of additional stimulations over the life of the field, thus helping to improve EGS commercial feasibility. The recent development of the chloride tool involves an Ion Specific Electrodes (ISE) probe and a reference electrode, assembled through a labor-intensive process, and designed to withstand downhole conditions for field deployment. Through laboratory experiments and numerical simulations, the tool demonstrated efficacy in identifying changes in chloride concentration, indicating its utility in feed zone detection. However, the impact of impedance on voltage measurements and discrepancies between laboratory and simulation results presented opportunities for further refinement. Notably, simulation results consistently underestimated actual chloride concentration by 30-40%, suggesting the need for compensatory calibration. Comparisons between different simulation software indicated that ANSYS was more accurate in replicating key features observed in laboratory experiments. Moreover, a Machine Learning (ML) approach was used to improve feed zone location detection and inflow rate measurement, utilizing Random Forest and Light Gradient Boosting Machine (LGBM) models, which delivered high performance scores. Thus, the chloride tool's recent development and integration with machine learning approaches offer promising advancements in feed zone identification and quantification.