Utah FORGE: Powder X-ray Diffraction Data from Well 16A(78)-32 Core

Kroll, Kayla; Davila, Gabriela; Kottkamp, Wayne; Smith, Megan

doi:10.15121/1996421

Title: Utah FORGE: Powder X-ray Diffraction Data from Well 16A(78)-32 Core

Dataset
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Abstract

This dataset from Lawrence Livermore National Laboratory (LLNL) consists of four raw X-ray diffraction (XRD) scans and preliminary results of quantitative XRD analysis. The scanned samples were prepared from four subcores, which came from various depths of the FORGE well 16A(78)-32 core. Desired core lengths were selected from available core photos (on GDR), provided by FORGE personnel, and subcored at LLNL. The XRD scans were collected in May 2023 at LLNL as pre-experimental characterization data for these subcores, which will be used in core-flooding experiments at LLNL and in triaxial direct shear experiments at Los Alamos National Laboratory as part of DOE Project 5-2428. XRD scans are in RAW file format (e.g., FORGE-5477-full.raw) and are suitable for viewing and analysis using open-source quantitative XRD software (e.g., Profex; www.profex-xrd.org ) and/or other proprietary instrument software.

Authors:

Kroll, Kayla; Davila, Gabriela; Kottkamp, Wayne; Smith, Megan

Lawrence Livermore National Laboratory

Publication Date:: Thu Jul 27 00:00:00 EDT 2023

Other Number(s):: 1521

DOE Contract Number:: EE0007080

Research Org.:: DOE Geothermal Data Repository; Lawrence Livermore National Laboratory

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

Collaborations:: Lawrence Livermore National Laboratory

Subject:: 15 GEOTHERMAL ENERGY; 16A78-32; 16A78-32 XRD; EGS; FORGE; LLNL; Lawrence Livermore National Laboratory; Utah FORGE; Utah geothermal; X-Ray diffraction; XRD; core; core X-ray diffraction; core XRD; energy; geochemistry; geology; geothermal; raw data; well 16A78-32; well core studies

OSTI Identifier:: 1996421

DOI:: https://doi.org/10.15121/1996421

Citation Formats


                    Kroll, Kayla, Davila, Gabriela, Kottkamp, Wayne, and Smith, Megan. Utah FORGE: Powder X-ray Diffraction Data from Well 16A(78)-32 Core.  United States: N. p., 2023. 
        Web.  doi:10.15121/1996421.

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                    Kroll, Kayla, Davila, Gabriela, Kottkamp, Wayne, & Smith, Megan. Utah FORGE: Powder X-ray Diffraction Data from Well 16A(78)-32 Core.  United States.  doi:https://doi.org/10.15121/1996421

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                    Kroll, Kayla, Davila, Gabriela, Kottkamp, Wayne, and Smith, Megan. 2023.  
"Utah FORGE: Powder X-ray Diffraction Data from Well 16A(78)-32 Core".  United States.  doi:https://doi.org/10.15121/1996421.  https://www.osti.gov/servlets/purl/1996421. Pub date:Thu Jul 27 00:00:00 EDT 2023

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@article{osti_1996421,

  title        = {Utah FORGE: Powder X-ray Diffraction Data from Well 16A(78)-32 Core},

  author       = {Kroll, Kayla and Davila, Gabriela and Kottkamp, Wayne and Smith, Megan},

  abstractNote = {This dataset from Lawrence Livermore National Laboratory (LLNL) consists of four raw X-ray diffraction (XRD) scans and preliminary results of quantitative XRD analysis. The scanned samples were prepared from four subcores, which came from various depths of the FORGE well 16A(78)-32 core. Desired core lengths were selected from available core photos (on GDR), provided by FORGE personnel, and subcored at LLNL. The XRD scans were collected in May 2023 at LLNL as pre-experimental characterization data for these subcores, which will be used in core-flooding experiments at LLNL and in triaxial direct shear experiments at Los Alamos National Laboratory as part of DOE Project 5-2428. XRD scans are in RAW file format (e.g., FORGE-5477-full.raw) and are suitable for viewing and analysis using open-source quantitative XRD software (e.g., Profex; www.profex-xrd.org ) and/or other proprietary instrument software.},

  doi          = {10.15121/1996421},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Jul 27 00:00:00 EDT 2023},

  month        = {Thu Jul 27 00:00:00 EDT 2023}

}

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DOI: https://doi.org/10.15121/1996421

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