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

    Metadata Updated: January 20, 2025

    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.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: Creative Commons Attribution

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    Dates

    Metadata Created Date January 11, 2025
    Metadata Updated Date January 20, 2025

    Metadata Source

    Harvested from OpenEI data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date January 11, 2025
    Metadata Updated Date January 20, 2025
    Publisher National Renewable Energy Laboratory
    Maintainer
    Doi 10.15121/2283328
    Identifier https://data.openei.org/submissions/7658
    Data First Published 2024-01-24T07:00:00Z
    Data Last Modified 2024-01-31T16:26:28Z
    Public Access Level public
    Bureau Code 019:20
    Metadata Context https://openei.org/data.json
    Metadata Catalog ID https://openei.org/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Data Quality True
    Datagov Dedupe Retained 20250120155001
    Harvest Object Id 7b3f85b8-5beb-4500-b801-86030560d209
    Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
    Harvest Source Title OpenEI data.json
    Homepage URL https://gdr.openei.org/submissions/1577
    License https://creativecommons.org/licenses/by/4.0/
    Old Spatial {"type":"Polygon","coordinates":-180,-83,180,-83,180,83,-180,83,-180,-83}
    Program Code 019:006
    Projectlead Lauren Boyd
    Projectnumber 37178
    Projecttitle DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN)
    Source Datajson Identifier True
    Source Hash c63a80e38443c8f38517f398cb968827aa8b33a4bd0a1adcdf4c2deb00895612
    Source Schema Version 1.1
    Spatial {"type":"Polygon","coordinates":-180,-83,180,-83,180,83,-180,83,-180,-83}

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