Successful geothermal reservoir exploration, characterization, and development requires a thorough understanding of the physical properties of reservoir rocks and their associated fracture systems.  Of particular importance is the reduction of uncertainty in the exploration phase.  Geothermal resources have been recognized for some time as a possible significant source of energy but to date have seen marginal increases in their development and application.  Much of this is attributed to the substantial upfront costs associated with geothermal projects coupled with a lack of reservoir characterization.  While the resource potential is recognized, investors have been hesitant to proceed with these high risk, long return on investment (ROI) scenarios.  While many cost reducing technologies are being developed in the geothermal industry right now, continued refinement and development of Play Fairway Analysis (PFA) exploration workflows and their associated risk reduction will continue to increase investor confidence and support the full-scale growth of geothermal in the coming energy transition.

• 3:00 PM - 3:20 PM: Cursed? Why We Cannot Simply Add New Data to Supervised Geothermal Machine Learning Models | Stanley Mordensky
• 3:20 PM - 3:40 PM: Feature Engineering of Fault Attributes for Play Fairway Analysis, Great Basin Region, USA | Nicole Hart-Wagoner 
• 3:40 PM - 4:00 PM: Geothermal Play Fairway Analysis (GPFA): A Texas Case Study | Kevin McCarthy 
• 4:00 PM - 4:20 PM: Geothermal Resource Evaluation and Applicable Technology for Wyoming | Emilie Gentry 
• 4:20 PM - 4:40 PM: Low-Temperature Geothermal Resources: Relevant Data and PFA Methods to Reduce Development Risk | Estefanny Davalos Elizondo 
• 4:40 PM - 5:00 PM: Machine Learning applications for predicting heat flow and thermal conductivity in northwest U.S. | Dale Bird