DEEPEN 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano
DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. Part of the DEEPEN 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 is based on the method proposed by Poux et al. (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. More information on this process is described in the following sections. 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. There are two different versions of the Leapfrog model and associated favorability models: - v1.0: The first release in June 2023 - v2.1: The second release, with improvements made to the earthquake catalog (included additional identified events, removed duplicate events), to the temperature model (fixed a deep BHT), and to the index models (updated the seismicity-heat source index models for supercritical and EGS, and the resistivity-insulation index models for all three play types). Also uses the jet color map rather than the magma color map for improved interpretability. - v2.1.1: Updated to include v2.0 uncertainty results (see below for uncertainty model versions) There are two different versions of the associated uncertainty models: - v1.0: The first release in June 2023 - v2.0: The se...