Financial risk due to geological uncertainty is a major barrier for geothermal development. Production from a geothermal well depends on the unknown location of subsurface geological structures, such as faults that contain hydrothermal fluids. Traditionally, geoscientists collect many different datasets, interpret the datasets manually, and create a single model estimating faults' locations. This method, however, does not provide information about the uncertainty regarding the location of faults and often does not fully respect all observed datasets. Previous researchers investigated the use of stochastic inversion schemes for addressing geological uncertainty, but often at the expense of geologic realism. In this paper, we present algorithms and open-source code to stochastically invert five typical datasets for creating geologically realistic structural models. Using a case study with real data from the Patua Geothermal Field, we show that these inversion algorithms are successful in finding an ensemble of structural models that are geologically realistic and match the observed data sufficiently. Geoscientists can use this ensemble of models to optimize reservoir management decisions given structural uncertainty.

地质不确定性带来的财务风险是地热开发的主要障碍。地热井的产量取决于地下地质结构的未知位置，例如含有热液的断层。传统上，地球科学家收集许多不同的数据集，手动解释数据集，并创建一个估计断层位置的单一模型。但是，该方法无法提供有关故障位置不确定性的信息，并且通常不能完全尊重所有观察到的数据集。以前的研究人员研究了使用随机反演方案来解决地质不确定性的问题，但通常是以牺牲地质现实主义为代价的。在本文中，我们提供了算法和开放源代码，以随机地反转五个典型数据集，以创建地质上逼真的结构模型。通过使用来自Patua地热田真实数据的案例研究，我们证明了这些反演算法成功地找到了在地质上切合实际并与观测数据充分匹配的结构模型集合。在存在结构不确定性的情况下，地球科学家可以使用这种模型集合来优化储层管理决策。