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DOE OSTI · 2368547

GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems

Mudunuru, Maruti K.·Ahmmed, Bulbul·Frash, Luke
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Abstract

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting relatively cold water into subsurface fractures, which are in contact with hot dry rock, and brought back to surface through production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. In this short conference paper, we present a reproducible workflow for modeling EGS. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and high-performance computing. This GTC framework is currently being made open-source, user-friendly, and reproducible through python scripts as well as Google Colab/Jupyter Notebooks. This GTC for EGS modeling scripts are made available at https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS and will constantly be updated to cater for geothermal community. Current GTC framework provides scripts to train deep learning (DL) models for techno-economics and data worth analysis. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. This short paper provides details on the scripts to curate, process, and train DL models. The scripts can easily be modified to train on databases generated by other popular open-source simulators such as PFLOTRAN, STOMP, TOUGH, and GEOSX or commercial software such as ResFrac and COMSOL.

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BibTeXRIS

Mudunuru, Maruti K., Ahmmed, Bulbul, Frash, Luke. 2023-12-12. GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems. https://www.osti.gov/biblio/2368547

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