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DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano

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Part of the DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments) project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested using new and existing geoscientific exploration datasets at Newberry Volcano. This GDR submission includes images, data, and models related to the 3D favorability and uncertainty models and the 2D favorability and uncertainty maps.

The DEEPEN PFA Methodology, detailed in the journal article below, is based on the method proposed by Poux & O'brien (2020), which uses the Leapfrog Geothermal software with the Edge extension to conduct PFA in 3D. This method uses all available data to build a 3D geodata model which can be broken down into smaller blocks and analyzed with advanced geostatistical methods. Each data set is imported into a 3D model in Leapfrog and divided into smaller blocks. Conditional queries can then be used to assign each block an index value which conditionally ranks each block's favorability, from 0-5 with 5 being most favorable, for each model (e.g., lithologic, seismic, magnetic, structural). The values between 0-5 assigned to each block are referred to as index values. The final step of the process is to combine all the index models to create a favorability index. This involves multiplying each index model by a given weight and then summing the resulting values.

The DEEPEN PFA Methodology follows this approach, but split up by the specific geologic components of each play type. These components are defined as follows for each magmatic play type:
1. Conventional hydrothermal plays in magmatic environments: Heat, fluid, and permeability
2. Superhot EGS plays: Heat, thermal insulation, and producibility (the ability to create and sustain fractures suitable for and EGS reservoir)
3. Supercritical plays: Heat, supercritical fluid, pressure seal, and producibility (the proper permeability and pressure conditions to allow production of supercritical fluid)

More information on these components and their development can be found in Kolker et al., (2022).

For the purposes of subsurface imaging, it is easier to detect a permeable fluid-filled reservoir than it is to detect separate fluid and permeability components. Therefore, in this analysis, we combine fluid and permeability for conventional hydrothermal plays, and supercritical fluid and producibility for supercritical plays. We also project the 3D favorability volumes onto 2D surfaces for simplified joint interpretation, and we incorporate an uncertainty component.

Uncertainty was modeled using the best approach for the dataset in question, for the datasets where we had enough information to do so. Identifying which subsurface parameters are the least resolved can help qualify current PFA results and focus future efforts in data collection. Where possible, the resulting uncertainty models/indices were weighted using the same weights applied to the respective datasets, and summed, following the PFA methodology above, but for uncertainty.

Citation Formats

TY - DATA AB - Part of the DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments) project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested using new and existing geoscientific exploration datasets at Newberry Volcano. This GDR submission includes images, data, and models related to the 3D favorability and uncertainty models and the 2D favorability and uncertainty maps. The DEEPEN PFA Methodology, detailed in the journal article below, is based on the method proposed by Poux & O'brien (2020), which uses the Leapfrog Geothermal software with the Edge extension to conduct PFA in 3D. This method uses all available data to build a 3D geodata model which can be broken down into smaller blocks and analyzed with advanced geostatistical methods. Each data set is imported into a 3D model in Leapfrog and divided into smaller blocks. Conditional queries can then be used to assign each block an index value which conditionally ranks each block's favorability, from 0-5 with 5 being most favorable, for each model (e.g., lithologic, seismic, magnetic, structural). The values between 0-5 assigned to each block are referred to as index values. The final step of the process is to combine all the index models to create a favorability index. This involves multiplying each index model by a given weight and then summing the resulting values. The DEEPEN PFA Methodology follows this approach, but split up by the specific geologic components of each play type. These components are defined as follows for each magmatic play type: 1. Conventional hydrothermal plays in magmatic environments: Heat, fluid, and permeability 2. Superhot EGS plays: Heat, thermal insulation, and producibility (the ability to create and sustain fractures suitable for and EGS reservoir) 3. Supercritical plays: Heat, supercritical fluid, pressure seal, and producibility (the proper permeability and pressure conditions to allow production of supercritical fluid) More information on these components and their development can be found in Kolker et al., (2022). For the purposes of subsurface imaging, it is easier to detect a permeable fluid-filled reservoir than it is to detect separate fluid and permeability components. Therefore, in this analysis, we combine fluid and permeability for conventional hydrothermal plays, and supercritical fluid and producibility for supercritical plays. We also project the 3D favorability volumes onto 2D surfaces for simplified joint interpretation, and we incorporate an uncertainty component. Uncertainty was modeled using the best approach for the dataset in question, for the datasets where we had enough information to do so. Identifying which subsurface parameters are the least resolved can help qualify current PFA results and focus future efforts in data collection. Where possible, the resulting uncertainty models/indices were weighted using the same weights applied to the respective datasets, and summed, following the PFA methodology above, but for uncertainty. AU - Taverna, Nicole A2 - Pauling, Hannah A3 - Kolker, Amanda A4 - Trainor-Guitton, Whitney DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/2283328 KW - geothermal KW - energy KW - DEEPEN KW - superhot KW - supercritical KW - superhot EGS KW - EGS KW - Newberry KW - magmatic KW - hydrothermal KW - pfa KW - modeling KW - modelling KW - exploration KW - 3D KW - 2D KW - favorability KW - OMF KW - Leapfrog KW - model KW - geodata KW - uncertainty KW - component KW - volcano KW - geodata model KW - processed data KW - geophysics KW - characterization LA - English DA - 2024/01/24 PY - 2024 PB - National Renewable Energy Laboratory T1 - DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano UR - https://doi.org/10.15121/2283328 ER -
Export Citation to RIS
Taverna, Nicole, et al. DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano. National Renewable Energy Laboratory, 24 January, 2024, GDR. https://doi.org/10.15121/2283328.
Taverna, N., Pauling, H., Kolker, A., & Trainor-Guitton, W. (2024). DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano. [Data set]. GDR. National Renewable Energy Laboratory. https://doi.org/10.15121/2283328
Taverna, Nicole, Hannah Pauling, Amanda Kolker, and Whitney Trainor-Guitton. DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano. National Renewable Energy Laboratory, January, 24, 2024. Distributed by GDR. https://doi.org/10.15121/2283328
@misc{OEDI_Dataset_7658, title = {DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano}, author = {Taverna, Nicole and Pauling, Hannah and Kolker, Amanda and Trainor-Guitton, Whitney}, abstractNote = {Part of the DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments) project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested using new and existing geoscientific exploration datasets at Newberry Volcano. This GDR submission includes images, data, and models related to the 3D favorability and uncertainty models and the 2D favorability and uncertainty maps.

The DEEPEN PFA Methodology, detailed in the journal article below, is based on the method proposed by Poux \& O'brien (2020), which uses the Leapfrog Geothermal software with the Edge extension to conduct PFA in 3D. This method uses all available data to build a 3D geodata model which can be broken down into smaller blocks and analyzed with advanced geostatistical methods. Each data set is imported into a 3D model in Leapfrog and divided into smaller blocks. Conditional queries can then be used to assign each block an index value which conditionally ranks each block's favorability, from 0-5 with 5 being most favorable, for each model (e.g., lithologic, seismic, magnetic, structural). The values between 0-5 assigned to each block are referred to as index values. The final step of the process is to combine all the index models to create a favorability index. This involves multiplying each index model by a given weight and then summing the resulting values.

The DEEPEN PFA Methodology follows this approach, but split up by the specific geologic components of each play type. These components are defined as follows for each magmatic play type:
1. Conventional hydrothermal plays in magmatic environments: Heat, fluid, and permeability
2. Superhot EGS plays: Heat, thermal insulation, and producibility (the ability to create and sustain fractures suitable for and EGS reservoir)
3. Supercritical plays: Heat, supercritical fluid, pressure seal, and producibility (the proper permeability and pressure conditions to allow production of supercritical fluid)

More information on these components and their development can be found in Kolker et al., (2022).

For the purposes of subsurface imaging, it is easier to detect a permeable fluid-filled reservoir than it is to detect separate fluid and permeability components. Therefore, in this analysis, we combine fluid and permeability for conventional hydrothermal plays, and supercritical fluid and producibility for supercritical plays. We also project the 3D favorability volumes onto 2D surfaces for simplified joint interpretation, and we incorporate an uncertainty component.

Uncertainty was modeled using the best approach for the dataset in question, for the datasets where we had enough information to do so. Identifying which subsurface parameters are the least resolved can help qualify current PFA results and focus future efforts in data collection. Where possible, the resulting uncertainty models/indices were weighted using the same weights applied to the respective datasets, and summed, following the PFA methodology above, but for uncertainty.}, url = {https://gdr.openei.org/submissions/1577}, year = {2024}, howpublished = {GDR, National Renewable Energy Laboratory, https://doi.org/10.15121/2283328}, note = {Accessed: 2025-05-04}, doi = {10.15121/2283328} }
https://dx.doi.org/10.15121/2283328

Details

Data from Jan 24, 2024

Last updated Jan 31, 2024

Submitted Jan 24, 2024

Organization

National Renewable Energy Laboratory

Contact

Nicole Taverna

Authors

Nicole Taverna

National Renewable Energy Laboratory

Hannah Pauling

National Renewable Energy Laboratory

Amanda Kolker

National Renewable Energy Laboratory

Whitney Trainor-Guitton

National Renewable Energy Laboratory

Research Areas

DOE Project Details

Project Name DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN)

Project Lead Lauren Boyd

Project Number 37178

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