Predicting the thermal performance of an enhanced geothermal system (EGS) requires a comprehensive characterization of the underlying fracture flow patterns from practically available data such as tracer data. However, due to the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical data, interpreting tracer data for fracture flow characterization and thermal prediction remains a challenging task. The present study aims to tackle the challenge by leveraging a data assimilation method to maximize the utilization of information inherently contained in tracer data, and meanwhile maintain the flexibility to handle various uncertainties. A tracer data interpretation framework was proposed with the following three components integrated: (a) We use principal component analysis (PCA) to reduce the dimensionality of model parameter space. (b) We use ES-MDA (ensemble smoother with multiple data assimilation) to invert for fracture aperture/flow fields and obtain posterior model ensembles for uncertainty quantification. Various data types are assimilated jointly to improve the predictive ability of the posterior ensemble. (c) The inverted fracture aperture fields are then incorporated into reservoir models to predict thermal performance. We developed a field-scale EGS model to verify the ability of the framework to characterize highly heterogeneous fracture aperture/flow fields and predicting thermal performance. We also applied the framework to a mesoscale field experiment to demonstrate its potential application in real-world geothermal reservoirs. The results indicate that the proposed framework can effectively retrieve fracture flow information from tracer data for thermal prediction and uncertainty quantification, and thus provide informative guidance for EGS optimization and risk management.

中文翻译：

---

从数据同化框架中的示踪剂测试预测增强型地热系统的热性能

预测增强型地热系统 (EGS) 的热性能需要从实际可用的数据（如示踪数据）中全面表征潜在的裂缝流动模式。然而，由于地下裂缝固有的复杂性和地质/地球物理数据普遍不足，解释用于裂缝流动特征和热预测的示踪数据仍然是一项具有挑战性的任务。本研究旨在通过利用数据同化方法来最大程度地利用示踪数据中固有的信息来应对挑战，同时保持处理各种不确定性的灵活性。提出了一个示踪数据解释框架，其中集成了以下三个组件：(a) 我们使用主成分分析 (PCA) 来降低模型参数空间的维数。(b) 我们使用 ES-MDA（多数据同化的集成平滑器）来反演裂缝孔径/流场，并获得用于不确定性量化的后验模型集成。联合吸收各种数据类型以提高后验集成的预测能力。(c) 然后将反向裂缝孔径场结合到储层模型中以预测热性能。我们开发了一个现场规模的 EGS 模型来验证框架表征高度异质裂缝孔径/流场和预测热性能的能力。我们还将该框架应用于中尺度现场实验，以证明其在现实世界地热储层中的潜在应用。