Stanford Thermal Earth Model for the Conterminous United States

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Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below.

A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..

Citation Formats

TY - DATA AB - Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below. A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively.. AU - Aljubran, Mohammad A2 - Horne, Roland DB - Geothermal Data Repository DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/2324793 KW - Thermal Earth Model KW - Temperature KW - geothermal KW - energy KW - Stanford KW - temperature-at-depth KW - heat flow KW - rock thermal conductivity KW - InterPIGNN KW - physics-informed KW - graph neural networks KW - machine learning KW - model KW - temperature model KW - ArcGIS KW - API KW - model inputs KW - model outputs KW - data-driven KW - spatial interpolation KW - algorithm KW - heat conduction KW - bottomhole temperature LA - English DA - 2024/03/14 PY - 2024 PB - Stanford University T1 - Stanford Thermal Earth Model for the Conterminous United States UR - https://doi.org/10.15121/2324793 ER -
Export Citation to RIS
Aljubran, Mohammad, and Roland Horne. Stanford Thermal Earth Model for the Conterminous United States. Stanford University, 14 March, 2024, Geothermal Data Repository. https://doi.org/10.15121/2324793.
Aljubran, M., & Horne, R. (2024). Stanford Thermal Earth Model for the Conterminous United States. [Data set]. Geothermal Data Repository. Stanford University. https://doi.org/10.15121/2324793
Aljubran, Mohammad and Roland Horne. Stanford Thermal Earth Model for the Conterminous United States. Stanford University, March, 14, 2024. Distributed by Geothermal Data Repository. https://doi.org/10.15121/2324793
@misc{GDR_Dataset_1592, title = {Stanford Thermal Earth Model for the Conterminous United States}, author = {Aljubran, Mohammad and Horne, Roland}, abstractNote = {Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below.

A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..
}, url = {https://gdr.openei.org/submissions/1592}, year = {2024}, howpublished = {Geothermal Data Repository, Stanford University, https://doi.org/10.15121/2324793}, note = {Accessed: 2025-04-24}, doi = {10.15121/2324793} }
https://dx.doi.org/10.15121/2324793

Details

Data from Mar 14, 2024

Last updated Jul 16, 2024

Submitted Mar 14, 2024

Organization

Stanford University

Contact

Mohammad Aljubran

Authors

Mohammad Aljubran

Stanford University

Roland Horne

Stanford University

DOE Project Details

Project Name Wellbore Fracture Imaging Using Inflow Detection Measurements

Project Lead Lauren Boyd

Project Number EE0007080

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