Laboratory Experiment on Fracture Inflow Characterization Using Chloride Concentration In Geothermal Wells
Keywords: Geothermal Resources
Enhanced Geothermal System
Chloride Concentration
Laboratory Experiment
Downhole Logging Tool
Text
Thesis

This master's thesis is part of a comprehensive project that encompasses analytical calculation, numerical simulation, laboratory experimentation and field testing that focuses on the development and application of a downhole tool for characterizing feed zones and quantifying flow rates in geothermal fields using chloride concentration measurements along the wellbore. The specific focus of this research is on conducting and analyzing laboratory experiments, as well as preparing for the upcoming field tests. To identify a suitable candidate for the field test, early data analysis of the Utah FORGE field was conducted. Well 58-32 emerged as the most favorable option due to its successful stimulation, established wellbore-reservoir connection, significant depth of 9145 ft (2787 m) TVD, and vertical configuration that reduces operational risks. The downhole tool underwent calibration, resulting in an updated equation that correlates voltage measurements with chloride concentration. The revised calibration closely aligns with previous studies. Additionally, improvements were made to the laboratory's artificial well system to ensure continuous freshwater circulation. Static measurements indicated that higher chloride concentrations yielded more accurate predictions. Furthermore, distance from the feed zone played a role in measurement accuracy, with measurements farther from the feed zone showing reduced precision. Dye tracer experiments highlighted the presence of blind spots in the wellbore, influenced by downhole and injection flow rates. Dynamic measurements enabled the capture of feed zone locations, although the tool exhibited a delayed response. Notably, the tool demonstrated greater sensitivity to voltage changes during upward motion. These findings emphasize the critical role of tool positioning in determining feed zone locations and chloride concentrations. Larger distance tool positioning posed challenges in accurately identifying feed zones, and experiments with dual feed zones revealed difficulties in locating the upper feed zone when injection rates were significantly lower. The study also explored machine learning algorithms for predicting fracture locations and injection rates. LGBM and Random Forest algorithms exhibited strong performance for single fracture locations, but their effectiveness diminished in cases involving multiple feed zones due to limited data availability. Nevertheless, incorporating feature engineering and error tolerance techniques improved prediction accuracy to approximately 90%. In conclusion, the laboratory experiments showcased the potential of the downhole tool in characterizing feed zone locations based on chloride concentration. Calibration, voltage analysis, and machine learning models contributed to accurate identification and quantification of feed zones. Further research and refinement are crucial to enhance the tool's performance and applicability in practical geothermal applications.