DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

Taverna, Nicole; Pauling, Hannah; Kolker, Amanda

doi:10.15121/1995528

Title: DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

Dataset
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Abstract

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS,more »« less

Authors:

Taverna, Nicole; Pauling, Hannah; Kolker, Amanda

National Renewable Energy Laboratory

Publication Date:: Fri Jun 30 04:00:00 UTC 2023

Other Number(s):: 1511

Research Org.:: DOE Geothermal Data Repository; National Renewable Energy Laboratory

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)

Collaborations:: National Renewable Energy Laboratory

Subject:: 15 GEOTHERMAL ENERGY; DEEPEN; Newberry; characterization; energy; exploration; favorability; geothermal; hydrothermal; index models; magmatic; pfa; supercritical; superhot; superhot EGS

OSTI Identifier:: 1995528

DOI:: https://doi.org/10.15121/1995528

Citation Formats


                    Taverna, Nicole, Pauling, Hannah, and Kolker, Amanda. DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano.  United States: N. p., 2023. 
        Web.  doi:10.15121/1995528.

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                    Taverna, Nicole, Pauling, Hannah, & Kolker, Amanda. DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano.  United States.  doi:https://doi.org/10.15121/1995528

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                    Taverna, Nicole, Pauling, Hannah, and Kolker, Amanda. 2023.  
"DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano".  United States.  doi:https://doi.org/10.15121/1995528.  https://www.osti.gov/servlets/purl/1995528. Pub date:Fri Jun 30 04:00:00 UTC 2023

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@article{osti_1995528,

  title        = {DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano},

  author       = {Taverna, Nicole and Pauling, Hannah and Kolker, Amanda},

  abstractNote = {DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model},

  doi          = {10.15121/1995528},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Jun 30 04:00:00 UTC 2023},

  month        = {Fri Jun 30 04:00:00 UTC 2023}

}

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DOI: https://doi.org/10.15121/1995528

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