GeoThermalCloud.jl: An Open-Source Machine Learning Framework for Geothermal Exploration and Resource Development

GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration.

GeoThermalCloud.jl includes:

site data
simulation scripts
jupyter notebooks
intermediate results
code outputs
summary figures
readme markdown files

GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites:

Brady: geothermal exploration of the Brady geothermal site, Nevada
SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region
GreatBasin: geothermal exploration of the Great Basin region

Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.

Julia installation

GeoThermalCloud Machine Learning analyses are performed using Julia.

To install the most recent version of Julia, follow the instructions at https://julialang.org/downloads/

GeoThermalCloud installation

To install all required the modules, execute in the Julia REPL:

import Pkg
Pkg.add("GeoThermalCloud")

GeoThermalCloud examples

GeoThermalCloud machine learning analyses can be executed as follows:

import Pkg
Pkg.add("GeoThermalCloud")
import GeoThermalCloud

GeoThermalCloud.SWNM() # performs analyses of the Sounthwest New Mexico region
GeoThermalCloud.GreatBasin() # performs analyses of the Great Basin region
GeoThermalCloud.Brady() # performs analyses of the Brady site, Nevada

GeoThermalCloud machine learning analyses can be also executed as Jupyter notebooks as well

GeoThermalCloud.notebooks() # open Jupyter notebook to acccess all GeoThermalCloud notebooks
GeoThermalCloud.SWNM(notebook=true) # opens Jupyter notebook for analyses of the Sounthwest New Mexico region
GeoThermalCloud.GreatBasin(notebook=true) # opens Jupyter notebook for analyses of the Great Basin region
GeoThermalCloud.Brady(notebook=true) # opens Jupyter notebook for analyses of the Brady site, Nevada

SmartTensors

GeoThermalCloud analyses are performed using the SmartTensors machine learning framework.

SmartTensors provides tools for Unsupervised and Physics-Informed Machine Learning.

More information about SmartTensors can be found at smarttensors.github.io and tensors.lanl.gov.

SmartTensors includes a series of modules. Key modules are:

NMFk: Nonnegative Matrix Factorization + k-means clustering
NTFk: Nonnegative Tensor Factorization + k-means clustering

Publications

Book chapter

Vesselinov, V.V., Mudunuru, M.K. Ahmmed, B., Karra, S., and O’Malley, D., (2023): Machine Learning to Discover, Characterize, and Produce Geothermal Energy, CRC Press, Boca Raton, FL.

Peer reviewed

Rau, E., Ahmmed, B., Vesselinov, V.V, Mudunuru, M.K., (in preparation): Geothermal play development using machine learning and magnetotellurics data in the Tularosa Basin, south central New Mexico, Geothermics.
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, link.
Ahmmed, B. and Vesselinov, V.V. (2022): Machine learning and shallow groundwater chemistry to identify geothermal resources, Renewable Energy, link.
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, link.
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, link.

Conference papers

Ahmmed, B., Mudunuru, M.K., Frash, L., and Vesselinov, V.V.: 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, link.
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, Geothermal rising conference, Reno, NV, August 28-31, 2022.
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.
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, October3-6, 2021.
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.
Ahmmed, B., Vesselinov, V.V., Mudunuru, M.K., Middleton, R.S., 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.
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.
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. [Extended abstract]
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.
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.

Presentations

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.
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.
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.
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.
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.
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.
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.
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.