GEOTHERMALCLOUD: MACHINE LEARNING FOR GEOTHERMAL RESOURCE EXPLORATION

Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, play fairway analysis (PFA) studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) have vast potential. However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive, with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework, GeoThermalCloud https://github.com/SmartTensors/GeoThermalCloud.jl. The GeoThermalCloud uses a series of un-supervised, supervised, and physics-informed ML methods available in SmartTensors AI platform https://github.com/SmartTensors. Through GeoThermalCloud, we can identify hidden patterns in the geothermal field data needed to discover blind syst