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...

DEEPEN项目全称为岩浆环境地热储层勘探风险降低（DE-risking Exploration of geothermal Plays in magmatic ENvironments）。DEEPEN项目的部分工作为开发并测试一套适用于多种地热储层类型的三维储层有利区分析（3D play fairway analysis, PFA）方法，涵盖常规热液型、超高温增强型地热系统（Enhanced Geothermal System, EGS）以及超临界型储层。该方法已于纽贝里火山（Newberry Volcano）区域的新增及现有地球科学勘探数据集上开展测试验证。本次提交的GDR数据集包含与三维有利性及不确定性模型、二维有利性与不确定性图件相关的图像、数据及模型文件。
DEEPEN的PFA方法体系基于Poux等人2020年提出的方法，该方法依托搭载Edge扩展模块的Leapfrog Geothermal软件开展三维PFA分析。该方法利用全部可用数据构建三维地球数据模型，随后将模型拆分为若干小型区块，并通过先进的地质统计方法开展分析。将各数据集导入Leapfrog软件的三维模型后，拆分为更小的分析区块。随后可通过条件查询为每个区块分配索引值，基于各维度模型（如岩性、地震、磁法、构造模型）对区块的有利性进行条件排序，分值区间为0至5，其中5代表最优有利性。分配给各区块的0至5分值即为索引值。该流程的最终步骤为整合所有索引模型以生成有利性指数：具体方式为将每个索引模型乘以预设权重，再对加权后的结果进行求和。
DEEPEN的PFA方法体系遵循该思路，但针对不同储层类型的具体地质组分进行了拆分。针对各类岩浆型储层，其核心组分定义如下：1. 岩浆环境下的常规热液型储层：热源、流体与渗透率；2. 超高温EGS储层：热源、隔热层与可采性（即构建并维持适用于EGS储层的裂缝的能力）；3. 超临界型储层：热源、超临界流体、压力封盖层与可采性（即满足超临界流体开采所需的适宜渗透率与压力条件）。有关上述组分及其构建细节的更多内容可参考Kolker等人2022年的研究成果。
相较于单独识别流体与渗透率组分，在地下成像中更易于探测到充满流体的渗透性储层。因此，本次分析中，我们将常规热液型储层的流体与渗透率组分进行合并，并将超临界型储层的超临界流体与可采性组分进行整合。有关该处理流程的更多细节将在后续章节中详述。
我们还将三维有利性体投影至二维表面，以简化联合解译工作，并纳入不确定性组分。对于具备足够信息的数据集，我们采用适配该数据集的最优方法对不确定性进行建模。明确地下参数中分辨率最低的部分，有助于评估当前PFA分析结果的可靠性，并为未来的数据采集工作明确重点方向。在条件允许的情况下，我们参考前述PFA方法体系，对生成的不确定性模型/指数采用与对应数据集相同的权重进行加权，并对加权结果求和，以得到不确定性指数。
本次研究的Leapfrog模型及配套有利性模型共有三个版本：
- v1.0：2023年6月发布的首个正式版本；
- v2.1：第二次发布版本，针对地震目录（新增识别到的地震事件、移除重复事件）、温度模型（修正了深部BHT数据）及索引模型（更新了超临界与EGS储层的地震-热源索引模型，以及三类储层的电阻率-隔热层索引模型）进行了优化；同时将原有的岩浆色图（magma color map）替换为彩虹色图（jet color map），以提升解译可读性；
- v2.1.1：更新内容为纳入v2.0版本的不确定性分析结果（不确定性模型版本详见下文）。
配套的不确定性模型共有两个版本：
- v1.0：2023年6月发布的首个正式版本；
- v2.0：该