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

Description

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.

Resources

Name Format Description Link
0 Journal article detailing superhot EGS 3D PFA methodology and results from application to Newberry Volcano. https://doi.org/10.1016/j.geothermics.2023.102909
34 Updated 2D maps showing relative favorability for supercritical resources at Newberry Volcano, using two different weighting methodologies: one based on expert opinions and one based on principal component analysis. CRS: WGS 84 UTM zone 10 https://gdr.openei.org/files/1577/sc_2d_v2.1.2.png
34 Updated 2D maps showing relative uncertainty associated with favorability models for EGS at Newberry Volcano, using two different weighting methodologies: one based on expert opinions and one based on principal component analysis. CRS: WGS 84 UTM zone 10 https://gdr.openei.org/files/1577/egs_2d_uncertainty_v2.0.0.png
34 Flow chart describing the PFA methodology used to produce these results. https://gdr.openei.org/files/1577/pfa_methodology_flowchart.png
57 Updated Leapfrog 3D geodata model, uncertainty models, and PFA favorability models as an unprocessed zipped copy. https://gdr.openei.org/files/1577/Newberry%20Model%20v2.1.1.zip
34 Updated 2D maps showing relative favorability for EGS at Newberry Volcano, using two different weighting methodologies: one based on expert opinions and one based on principal component analysis. CRS: WGS 84 UTM zone 10 https://gdr.openei.org/files/1577/egs_2d_v2.1.2.png
34 Updated 2D map showing relative uncertainty associated with favorability models for conventional hydrothermal systems at Newberry Volcano, using the average of the normalized weights used in the magmatic hydrothermal DOE-funded PFA projects. CRS: WGS 84 UTM zone 10 https://gdr.openei.org/files/1577/hydrothermal_2d_v2.1.2.png
34 Updated 2D map showing relative uncertainty associated with favorability models for conventional hydrothermal systems at Newberry Volcano, using the average of the normalized weights used in the magmatic hydrothermal DOE-funded PFA projects. CRS: WGS 84 UTM zone 10 https://gdr.openei.org/files/1577/hydrothermal_2d_uncertainty_v2.0.0.png
34 Updated 2D maps showing relative uncertainty associated with favorability models for supercritical resources at Newberry Volcano, using two different weighting methodologies: one based on expert opinions and one based on principal component analysis. CRS: WGS 84 UTM zone 10 https://gdr.openei.org/files/1577/sc_2d_uncertainty_v2.0.0.png
57 This zipped directory contains updated CSV files representing 2D and 3D favorability block models for supercritical EGS, EGS, and hydrothermal. The 2D favorability block models are the data plotted to produce the 2D favorability map images uploaded as separate resources in this submission. Data for each blocks include expert and statistical favorability for various features of EGS, Supercritical EGS, and hydrothermal; including heat source, reservoir permeability, insulation, and overall favorability metrics. Methodology for obtaining these values is provided in the attached supporting literature. All coordinates are in UTM (m). https://gdr.openei.org/files/1577/2D%20and%203D%20Favorability%20Models_v2.1.0.zip
0 This paper from Kolker et al. details methodologies used in the DEEPEN project. It compares geological components, risks, and exploration techniques, utilizing Play Fairway Analysis (PFA) with data from international training sites. The study involves assigning weights to evidence layers through expert opinions and statistical learning, aiming to create 3D geothermal favorability models. https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034597
57 This zipped directory contains updated CSV files representing 2D confidence maps and 3D confidence models for fault distance, resistivity, density, and the temperature model. The 2D confidence maps are the data plotted to produce the 2D confidence map images uploaded as separate resources in this submission. These maps are for joint interpretation of the 2D component confidence models. The 3D confidence models include joint confidence models, along with individual component confidence models, produced using the same weights used in the favorability models applied to confidence/resolution matrices. The confidence index is normalized (on a scale of 0-1), meaning that it is relative. All coordinates are in UTM (m). ERR = Error WOV = Without value BLK = Blank OUT = Outside of model boundary https://gdr.openei.org/files/1577/2D%20and%203D%20Uncertainty%20Models_v2.0.0.zip
0 Updated Leapfrog 3D geodata model, uncertainty models, and PFA favorability models in Open Mining Format (OMF). https://gdr.openei.org/files/1577/Newberry%20Model_v2.1.1.omf
33 This paper from Poux & O'brien introduces a 3-Dimensional approach to 'Play Fairway' analysis, traditionally used in hydrocarbon exploration, for application in geothermal energy exploration and development. It details a methodology that combines various types of geological and geophysical data, including advanced subsurface surveys and drilling information, to create a dynamic 3D model assessing geothermal resource favorability. This model, which updates with new data, helps in minimizing exploration risks and identifying optimal sites for drilling geothermal wells. https://gdr.openei.org/files/1577/PouxandOBrien_2020.pdf

Tags

  • leapfrog
  • supercritical
  • superhot-egs
  • geophysics
  • processed-data
  • magmatic
  • modeling
  • egs
  • volcano
  • deepen
  • hydrothermal
  • favorability
  • component
  • geodata-model
  • 2d
  • superhot
  • omf
  • pfa
  • 3d
  • model
  • characterization
  • modelling
  • uncertainty
  • exploration
  • geodata
  • newberry
  • geothermal
  • energy

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