Figure 1 - uploaded by Hannah Pauling
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Publicly available geologic mapping coverage at 1:24,000 or larger scale is shown in white; areas with no available geologic mapping at 1:24,000 scale show the USGS undiscovered hydrothermal favorability from Williams et al. (2008).
Source publication
As part of a United States Department of Energy (DOE) supported retrospective analysis of DOE's Play Fairway Analysis (PFA) projects, the National Renewable Energy Laboratory (NREL) compiled and analyzed publicly available geothermal exploration datasets to identify and highlight data gaps in areas prospective for hosting geothermal resources. The...
Contexts in source publication
Context 1
... scale of 1:24,000 or larger was selected as the cutoff for geologic mapping of interest to this study as maps of this scale typically provide sufficient resolution for both regional and local geothermal exploration. Several State geologic surveys as well as the National Geologic Map Database contributed to the compilation shown in Figure 1. As shown in Figure 1, both the majority of the western U.S. and many of the areas identified as possessing high relative hydrothermal favorability lack detailed geologic mapping. ...
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... State geologic surveys as well as the National Geologic Map Database contributed to the compilation shown in Figure 1. As shown in Figure 1, both the majority of the western U.S. and many of the areas identified as possessing high relative hydrothermal favorability lack detailed geologic mapping. ...
Citations
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 systems efficiently. Crucial geothermal signatures often overlooked in traditional PFA are extracted using the GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA (ePFA), which is informative for efficient exploration. We applied our ML methodology to various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work). The results provide valuable insights into resource types within those regions. This ML-enhanced workflow makes the GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.
