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Notebook","category":"Renewable Energy","sub_category":"Geothermal Energy","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# GeoThermalCloud: A Physics-informed AI/ML Framework for Geothermal Resource Exploration, Development, and Monitoring\n\n\u003cdiv style=\"text-align: left; padding-bottom: 30px;\"\u003e\n\t\u003ca href=\"https://github.com/SmartTensors/GeoThermalCloud.jl\"\u003e\n    \t\u003cimg src=\"logos/geothermalcloud-small.png\" alt=\"geothermalcloud\" width=25%  max-width=125px;/\u003e\n\t\u003c/a\u003e\n\u003c/div\u003e\n\n**GeoThermalCloud.jl** is a repository containing data and codes required to demonstrate applications of machine learning methods for geothermal exploration, development, and monitoring.\n\n**GeoThermalCloud.jl** includes:\n- site data\n- simulation scripts\n- jupyter notebooks\n- intermediate results\n- code outputs\n- summary figures\n- readme markdown files\n- Phase-I and Phase-II reports\n- peer-review presentation to DOE-GTO\n\n**GeoThermalCloud.jl** showcases the machine learning analyses performed for the following geothermal sites:\n\n- **Brady**: geothermal exploration of the Brady geothermal site, Nevada\n- **SWNM**: geothermal exploration of the Southwest New Mexico (SWNM) region\n- **GreatBasin**: geothermal exploration of the Great Basin region\n\nReports, research papers, and presentations summarizing these machine-learning analyses are also available and will be posted soon.\n\n## Julia installation\n\nGeoThermalCloud Machine Learning analyses are performed using Julia.\n\nTo install the most recent version of Julia, follow the instructions at https://julialang.org/downloads/\n\n## GeoThermalCloud installation\n\nTo install all required modules, execute in the Julia REPL:\n\n```julia\nimport Pkg\nPkg.add(\"GeoThermalCloud\")\n```\n## GeoThermalCloud examples\n\nGeoThermalCloud machine learning analyses can be executed as follows:\n\n```julia\nimport Pkg\nPkg.add(\"GeoThermalCloud\")\nimport GeoThermalCloud\n\nGeoThermalCloud.SWNM() # performs analyses of the Sounthwest New Mexico region\nGeoThermalCloud.GreatBasin() # performs analyses of the Great Basin region\nGeoThermalCloud.Brady() # performs analyses of the Brady site, Nevada\n```\n\nGeoThermalCloud machine learning analyses can be also executed as Jupyter notebooks as well\n\n```julia\nGeoThermalCloud.notebooks() # open Jupyter notebook to acccess all GeoThermalCloud notebooks\nGeoThermalCloud.SWNM(notebook=true) # opens Jupyter notebook for analyses of the Sounthwest New Mexico region\nGeoThermalCloud.GreatBasin(notebook=true) # opens Jupyter notebook for analyses of the Great Basin region\nGeoThermalCloud.Brady(notebook=true) # opens Jupyter notebook for analyses of the Brady site, Nevada\n```\n## SmartTensors\n\nGeoThermalCloud analyses are performed using the [**SmartTensors**](https://github.com/SmartTensors) machine learning framework.\n\n\u003cdiv style=\"text-align: left; padding-bottom: 30px;\"\u003e\n\t\u003ca href=\"https://github.com/SmartTensors\"\u003e\n\t\t\u003cimg src=\"logos/SmartTensorsNewSmaller.png\" alt=\"SmartTensors\" width=25%  max-width=125px;/\u003e\n\t\u003c/a\u003e\n\u003c/div\u003e\n\n[**SmartTensors**](https://github.com/SmartTensors) provides tools for Unsupervised and Physics-Informed Machine Learning.\n\nMore information about [**SmartTensors**](https://github.com/SmartTensors) can be found at [smarttensors.github.io](https://smarttensors.github.io) and [tensors.lanl.gov](http://tensors.lanl.gov).\n\n[**SmartTensors**](https://github.com/SmartTensors) includes a series of modules. Key modules are:\n\n- [**NMFk**](https://github.com/SmartTensors/NMFk.jl): Nonnegative Matrix Factorization + k-means clustering\n- [**NTFk**](https://github.com/SmartTensors/NTFk.jl): Nonnegative Tensor Factorization + k-means clustering\n\n\u003cdiv style=\"text-align: left; padding-bottom: 30px;\"\u003e\n\t\u003ca href=\"https://github.com/SmartTensors/NMFk.jl\"\u003e\n\t\t\u003cimg src=\"logos/nmfk-logo.png\" alt=\"nmfk\" width=25%  max-width=125px;/\u003e\n\t\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cdiv style=\"text-align: left; padding-bottom: 30px;\"\u003e\n\t\u003ca href=\"https://github.com/SmartTensors/NTFk.jl\"\u003e\n\t\t\u003cimg src=\"logos/ntfk-logo.png\" alt=\"ntfk\" width=40%  max-width=125px;/\u003e\n\t\u003c/a\u003e\n\u003c/div\u003e\n\n## Publications\n\n### Book chapter\n\n- Vesselinov, V.V., Mudunuru, M.K. Ahmmed, B., Karra, S., and O’Malley, D., (accepted): Machine Learning to Discover, Characterize, and Produce Geothermal Energy, CRS Press, Boca Raton, FL.\n\n### Peer reviewed\n\n- Rau, E., Ahmmed, B., Vesselinov, V.V, Mudunuru, M.K., and Karra, S. (in review): Geothermal play development using machine learning, geophysics, and reservoir simulation, Renewable Energy.\n- Mudunuru, M.K., Ahmmed, B., Rau, E., Vesselinov, V.V., and Karra, S. (2023): Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico. Energies, 16(7), 3098\n- Mudunuru, M.K., Vesselinov, V.V. and Ahmmed, B., 2022. GeoThermalCloud: Machine Learning for Geothermal Resource Exploration. Journal of Machine Learning for Modeling and Computing.\n- Ahmmed, B. and Vesselinov, V.V., 2022. Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA. Renewable Energy, 197, pp.1034-1048.\n- Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E.R., Siler, D.L., Karra, S. and Middleton, R.S., 2022. Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering. Geothermics, 106, p.102576.\n- Siler, D.L., Pepin, J.D., Vesselinov, V.V., Mudunuru, M.K., and Ahmmed, B. (2021): Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada, Geothermal Energy.\n\n\n### Conference papers\n\n- Mudunuru, M.K., Ahmmed, B., and Frash, L.: GeoThermalCloud for EGS -- An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems, Geothermal Rising Conference, Reno, NV, October 1-5, 2023. \n- Mudunuru, M.K., Ahmmed, B., and Frash, L.: Deep Learning for Modeling Enhanced Geothermal Systems, 48th Annual Stanford Geothermal Workshop, Stanford, CA, February 6-8, 2023.  \n- Frash, L. and Ahmmed, B.: A FORGE Datathon Case Study to Optimize Well Spacing and Flow Rate for Power Generation, 48th Annual Stanford Geothermal Workshop, Stanford, CA, February 6-8, 2023. \n- Frash, L., Carey, J.W., Ahmmed, B., and others: A Proposal for Safe and Profitable Enhanced Geothermal Systems in Hot Dry Rock, 48th Annual Stanford Geothermal Workshop}, Stanford, CA, February 6-8, 2023.  \n- Ahmmed, B., Vesselinov, V.V., Mudunuru, M.K., and Frash, L.: A Progress Report on GeoThermalCloud Framework: An Open-source Machine Learning Based Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources, 48th Annual Stanford Geothermal Workshop, Stanford, CA, February 6-8, 2023. \n- Ahmmed, B., Vesselinov, V.V., Rau, E., and Mudunuru, M.K., and Karra, S.: Machine Learning and a Process Model to Better Characterize Hidden Geothermal Resources, GRC Transactions, v. 46, Reno, NV, August 28-31, 2022. \n- Vesselinov, V.V., Ahmmed, B., Frash, L., and Mudunuru, M.K.: GeoThermalCloud: Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources, 47th Annual Stanford Geothermal Workshop, Stanford, CA, February 7-9, 2022. \n- Vesselinov, V.V., Frash, L., Ahmmed, B., and Mudunuru, M.K.: Machine Learning to Characterize the State of Stress and its Influence on Geothermal Production, Geothermal Rising Conference, San Diego, CA, October 3-6, 2021. \n- Ahmmed, B., Vesselinov, V.V.: Prospectivity Analyses of the Utah FORGE Site using Unsupervised Machine Learning, Geothermal Rising Conference, San Diego, CA, October 3-6, 2021. \n- Ahmmed, B., Vesselinov, V.V., Mudunuru, M.K., Middleton, R., and Karra, S.: Geochemical characteristics of Low-, Medium-, and Hot-temperature Geothermal Resources of the Great Basin, USA, World Geothermal Congress, Reykjavik, Iceland, May 21-26, 2021. \n- Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Karra, S., and Middleton, R.: Hidden Geothermal Signatures of the Southwest New Mexico, World Geothermal Congress, Reykjavik, Iceland, May 21-26, 2021. \n- Mudunuru, M.K., Ahmmed, B., Vesselinov, V.V., Burns, E., Livingston, D.R., Karra, S., Middleton, R.S.: Machine Learning for Geothermal Resource Analysis and Exploration, XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA, December 13-15, 2020, no. 81. \n- Mudunuru, M.K., Ahmmed, B., Karra S., Vesselinov, V.V., Livingston D.R., and Middleton R.S.: Site-scale and Regional-scale Modeling for Geothermal Resource Analysis and Exploration, 45th Annual Stanford Geothermal Workshop, Stanford, CA, February 10-12, 2020. \n- Vesselinov, V.V., Mudunuru, M.K., Ahmmed, B., Karra, S. and Middleton, R.S.: Discovering Signatures of Hidden Geothermal Resources Based on Unsupervised Learning, 45th Annual Stanford Geothermal Workshop, Stanford, CA, February 10-12, 2020.\n\n### Presentations\n\n- Siler, D., Pepin, J., Vesselinov, V.V., Ahmmed, B., and Mudunuru, M.K.: A tale of two unsupervised machine learning techniques: What PCA and NMFk tell us about the geologic controls of hydrothermal processes, American Geophysical Union, New Orleans, LA,, December 13–17, 2021.\n- Siler, D., Pepin, J., Vesselinov, V.V., Ahmmed, B., and Mudunuru, M.K.: A tale of two unsupervised machine learning techniques: What PCA and NMFk tell us about the geologic controls of hydrothermal processes, Geothermal Rising Conference, San Diego, CA, October 3-6, 2021.\n- Ahmmed, B. Vesselinov, V. and Mudunuru, M.K., Integration of Data, Numerical Inversion,  and  Unsupervised Machine Learning to Identify Hidden Geothermal Resources in Southwest New Mexico, American Geophysical Union Fall Conference, San Francisco, CA, December 1-17, 2020.\n- Ahmmed, B., Vesselinov, V.V., and Mudunuru, M.K., Machine learning to characterize regional geothermal reservoirs in the western USA, Abstract T185-358249, Geological Society of America, October 26-29, 2020.\n- Ahmmed, B., Lautze, N., Vesselinov, V.V., Dores, D., and Mudunuru, M.K., Unsupervised Machine Learn- ing to Extract Dominant Geothermal Attributes in Hawaii Island Play Fairway Data, Geothermal Resources Council, Reno, NV, October 18-23, 2020.\n- Vesselinov, V.V., Ahmmed, B., and Mudunuru, M.K., Unsupervised Machine Learning to discover attributes that characterize low, moderate, and high-temperature geothermal resources, Geothermal Resources Council, Reno, NV, October 18-23, 2020.\n- Ahmmed, B., Vesselinov, V., and Mudunuru, M.K., Non-negative Matrix Factorization to Discover Dominant Attributes in Utah FORGE Data, Geothermal Resources Council, Reno, NV, October 18-23, 2020.\n- Ahmmed, B., Vesselinov, V.V., and Mudunuru, M.K., Unsupervised machine learning to discover dominant attributes of mineral precipitation due to CO2 sequestration, LA-UR-20-20989, 3rd Machine Learning in Solid Earth Science Conference, Santa Fe, NM, March 16-20, 2020.\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["parallel"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/19855","html_url":"https://ost.ecosyste.ms/projects/19855"}