GEOLOGIC RESERVOIRS + AI
GeoML uses cutting-edge science-informed AI methods and tools to help to analyze and process data characterzing geologic reservoirs.
GeoML can be applied to facilitate geologic engineering projects throughout their cycles. From initial exploration to development and utilization.
GeoML supports alternative geologic reservoirs uses: energy extraction, energy storage, waste disposal, carbon sequestration, oil & gas production, water supply, rear-earth mineral extraction, and in-situ mining.
Geologic reservoirs are critical for our society and its economy. Our commercial product allows you to explore the subsurface with depth and detail, giving your project the certainty it needs to succeed.
GeoML will be deployed as a Software-as-a-Service (SaaS) on the cloud. Tiered licensing and customer support options will be available.
Please contact us for licensing information, commercial support, and consulting.
Need policy-grade references?
Explore the GeoML Multi-Use Resource Hub
Browse curated federal and state regulations, 45Q finance playbooks, and multi-use storage research to accelerate GeoML deployments.
Visit the Resource HubExplore EnviCloud
Explore related demos
Try interactive public demos that bring these ideas to life.
- Public demo
3D Geo Modeling
View wells, terrain, and subsurface context in an interactive 3D scene.
Open demo (opens in a new tab) - Public demo
Energy Production & Storage
Explore how science-informed models can compare energy production and storage strategies.
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Carbon Capture & Storage
Compare carbon capture and subsurface storage choices in an interactive workflow.
Open demo (opens in a new tab) - Public demo
Spatial Feature Extraction
Discover spatial patterns, clusters, and hidden features in 2D and 3D data.
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Related publications
- AI-augmented geothermal model for scalable energy uncertainties in buildings (2026) — A. Markowitz et al. — Scientific Reports
- Deep-Embedded-Clustering of Microseismicity Identifies Multiple Failure Mechanisms at The Geysers Geothermal Field (2026) — P. Lara et al. — Geophysics (submitted)
- Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin (2026) — V.V. Vesselinov, T.L. Kliphuis — Stanford Geothermal Workshop
- GeoDAWN To GeoTGo: From Complex Data To Decisions Related To Geothermal Prospectivity (2025) — T.L. Kliphuis et al. — Stanford Geothermal Workshop, Stanford, CA
- Machine-Learning Methods and Tools Designed for Community-Based Equitable and Inclusive Geothermal Development (2024) — V.V. Vesselinov, H. Jasperson, T.L. Kliphuis — Stanford Geothermal Workshop, Stanford, CA
- ChemML: Physics-Informed AI/ML of Geochemical Datasets for Characterization, Parameterization, and Prediction of Contaminant Transport and Remediation Processes (2023) — V.V. Vesselinov, T.L. Kliphuis — DOE
- DPFEHM: a differentiable subsurface physics simulator (2023) — D. O'Malley et al. — Journal of Open Source Software
- Machine Learning for Geothermal Resource Exploration in Tularosa Basin, New Mexico (2023) — M. Mudunuru et al. — Energies
- A Progress Report on GeoThermalCloud: Framework An Open-Source Machine Learning Based Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources (2022) — B. Ahmmed et al. — Stanford Geothermal Workshop, Stanford, CA
- Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering (2022) — V.V. Vesselinov et al. — Geothermics
- GeoThermalCloud: Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources (2022) — V.V. Vesselinov et al. — Stanford Geothermal Workshop, Stanford, CA
- GeoThermalCloud: Machine learning for geothermal resource exploration (2022) — M. Mudunuru, V.V. Vesselinov, B. Ahmmed — Journal of Machine Learning for Modeling and Computing
- Machine learning and a process model to better characterize hidden geothermal resources (2022) — M. Ahmmed, V.V. Vesselinov — Geothermal Rising Conference (transactions)
- Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin (2022) — B. Ahmmed, V.V. Vesselinov — Renewable Energy
- Machine Learning to Discover, Characterize, and Produce Geothermal Energy (2022) — V.V. Vesselinov et al. — Machine Learning Applications in Subsurface Energy Resource Management
- SmartTensors: Unsupervised and physics-informed machine learning framework for the geoscience applications (2022) — B. Ahmmed, V.V. Vesselinov, M.K. Mudunuru — Second International Meeting for Applied Geoscience & Energy
- Discovering the Hidden Geothermal Signatures of Southwest New Mexico (2021) — V.V. Vesselinov et al. — World Geothermal Congress, Reykjavik, Iceland
- Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada (2021) — D.L. Siler, J.D. Pepin, V.V. Vesselinov, et al. — Geothermal Energy
- Discovering Signatures of Hidden Geothermal Resources based on Unsupervised Learning (2020) — V.V. Vesselinov et al. — Stanford Geothermal Workshop
- Subsurface energy: Flow and reactive-transport in porous and fractured media (2020) — M.K. Mudunuru et al. — Handbook of Porous Materials (invited), World Scientific Publishers
- Identifying Arbitrary Parameter Zonation using Multiple Level Set Functions (2018) — Z. Lu, V.V. Vesselinov, H. Lei — Journal of Computational Physics
- Randomization in Characterizing the Subsurface (2018) — Y. Lin et al. — SIAM News
- Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Filed Data and Simulation Outputs (2018) — V.V. Vesselinov et al. — Computational Methods in Water Resources (CMWR), Saint-Malo, France
- A high-performance workflow system for subsurface simulation (2014) — V.L. Freedman et al. — Environmental Modelling & Software
Related presentations
- Science-informed machine learning for reservoir characterization (2026) — V.V. Vesselinov, T.L. Kliphuis — Computational Methods in Water Resources (CMWR), Bologna, Italy
- BEYOND PROSPECTIVITY MAPS: Multimodal Geospatial AI for Critical Mineral Systems and Geothermal Prospectivity Exploration (2026) — V.V. Vesselinov, T.L. Kliphuis — ML4SEG (Machine Learning for Solid Earth Geosciences and Earthquake Physics), Sicily, Italy
- Imaging Subsurface Reservoirs for Hydrogen, Geothermal, and Critical Minerals: Multi-Physics Imaging and AI-Assisted Interpretation (2026) — V.V. Vesselinov, T.L. Kliphuis — AI for Geological Mapping and Modelling Conference (AIGMM), Utrecht, Netherlands
- Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin (2026) — V.V. Vesselinov, T.L. Kliphuis — Stanford Geothermal Workshop
- Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin (2026) — V.V. Vesselinov, T. Kliphuis — Stanford Geothermal Workshop
- GAIA: Cloud Framework for Geospatial Artificial Intelligence Analyses (2025) — T. Kliphuis, V.V. Vesselinov — HydroML 2025, University of California, Irvine
- GeoDAWN & GeoFLIGHT to GeoTGo: From complex data to defensible decisions related to geothermal prospectivity (2025) — V.V. Vesselinov et al. — Stanford Geothermal Workshop
- UrbanAI Transforming Urban Energy Planning Using Artificial Intelligence (2025) — R. Yang et al. — Machine Learning in Solid Earth Geoscience
- When Earth Meets AI (2025) — V.V. Vesselinov, T. Kliphuis — RealmIQ El Sailon
- Laying the groundwork for a greener future: ML for characterizing and managing geologic reservoirs (2024) — V.V. Vesselinov, H. Jasperson, T. Kliphuis
- Mapping geothermal resources using AI/ML (2024) — T. Kliphuis, H. Jasperson, V.V. Vesselinov — New Mexico Geological Society Annual Spring Meeting
- GeoTGO: Equitable and Inclusive Tool for Community-Based Geothermal (2023) — T. Kliphuis, M. Bluehouse, V.V. Vesselinov — Stanford Geothermal Workshop
- ChemML: Understanding groundwater flow and contaminant transport using machine learning (2022) — V.V. Vesselinov, T. Kliphuis — American Geophysical Union, San Juan, Puerto Rico
- Machine learning and a process model to better characterize hidden geothermal resources (2022) — M. Ahmmed, V.V. Vesselinov — Geothermal Rising Conference (GRC)
- Novel machine learning methods and tools for geothermal and geochemical problems (2022) — V.V. Vesselinov, T. Kliphuis — AGU Fall Meeting
- Physics-Informed Machine Learning of Geothermal, Geomechanical, Geochemical Process (2022) — V.V. Vesselinov, T. Kliphuis — AGU Fall Meeting
- GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources (2021) — V.V. Vesselinov, et al. — Department of Energy, Geothermal Office
- GeoThermalCloud: Fusion of Big Data and Multi-Physics Models (2021) — V.V. Vesselinov, et al. — JuliaCon, Boston, MA
- Hidden geothermal signatures of the southwest New Mexico (2021) — V.V. Vesselinov, et al. — World Geothermal Congress, Reykjavik, Iceland
- Machine Learning to Characterize the State of Stress and its Influence on Geothermal Production (2021) — V.V. Vesselinov, et al. — Geothermal Rising Conference
- ML4Geo: Machine Learning for Geosciences (2021) — V.V. Vesselinov, et al. — JuliaCon, Boston, MA
- SmartTensors: Unsupervised Machine Learning (2021) — V.V. Vesselinov, et al. — JuliaCon, Boston, MA
- Unsupervised and Physics-Informed Machine learning in Geosciences (2021) — V.V. Vesselinov, et al. — Baylor University, Texas
- Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning (2020) — V.V. Vesselinov, et al. — Stanford Geothermal Workshop
- Machine learning for geothermal resource analysis and exploration (2020) — V.V. Vesselinov, et al. — XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA
- Site-Scale and Regional-Scale Modeling for Geothermal Resource Analysis and Exploration (2020) — M. Mudunuru, V.V. Vesselinov, et al. — Geothermal Workshop, Stanford, CA
- Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data (2020) — V.V. Vesselinov — Bureau of Economic Geology, University of Austin, Texas
- Novel Unsupervised Machine Learning Methods for Data Analytics and Model Diagnostics (2019) — V.V. Vesselinov — Machine Learning in Solid Earth Geoscience, Santa Fe
- Physics-Informed Machine Learning Methods for Data Analytics and Model Diagnostics (2019) — V.V. Vesselinov — M3 NASA DRIVE Workshop, Los Alamos
- Unsupervised and Physics-Informed Machine Learning of Big Data (2019) — V.V. Vesselinov — Workshop: Applications of Big Data and High-Performance Computing in Earth Sciences, AGU Fall Meeting, San Francisco, CA (invited)
- Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models (2018) — V.V. Vesselinov — Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota
- Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models (2018) — V.V. Vesselinov — AGU Fall meeting
- Novel Robust Machine Learning Methods for Identification and Extraction of Unknown Features in Complex Real-world Data Sets (2018) — V.V. Vesselinov, D. O'Malley, B. Alexandrov — Society for Industrial and Applied Mathematics (SIAM) Uncertainty Quantification, Garden Grove, CA (invited)
- Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Field Data and Simulation Outputs (2018) — V.V. Vesselinov et al. — Computational Methods in Water Resources (CMWR), Saint-Malo, France
- Unsupervised Machine Learning based on Nonnegative Matrix/Tensor Factorization (2018) — V.V. Vesselinov et al. — World Congress on Computational Mechanics (WCCM) (invited)
- Unsupervised Machine Learning Based on Tensor Factorization (2018) — V.V. Vesselinov, D. O'Malley, B. Alexandrov — International Society for Porous Media (INTERPORE)
Carbon sequestration
- DPFEHM: a differentiable subsurface physics simulator (2023) — D. O'Malley et al. — Journal of Open Source Software
- Beam Me Up SCO2TPRO: A Comparison to the FE/NETL CO2 Saline Storage Cost Model and Updates on Tool Development (2022) — J. Bennett et al. — SSRN Electronic Journal
- Machine Learning to Discover Mineral Trapping Signatures due to CO2 Injection (2021) — B. Ahmmed et al. — International Journal of Greenhouse Gas Control
- Subsurface energy: Flow and reactive-transport in porous and fractured media (2020) — M.K. Mudunuru et al. — Handbook of Porous Materials (invited), World Scientific Publishers
- Decision Analysis for Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory (2016) — M. Grasinger et al. — International Journal of Greenhouse Gas Control
- Bayesian-Information-Gap Decision Theory (BIG-DT) with an application to CO2 sequestration (2015) — D. O’Malley, V.V. Vesselinov — Water Resources Research