Abstract Geothermal energy has a high potential to contribute to a more sustainable energy system if the associated economic risks can be overcome in the design process. The development planning of deep geothermal reservoirs (over 1000 m depth) relies on computer models to forecast and then optimize system design. Optimization is easy where all the objective's (e.g. NPV) optimization parameters and, most importantly, the geology are considered as known, but this is almost always not the case. Where the complex engineering design (e.g. well placement) meets significant geological uncertainty every development option should be tested using an expensive simulation against the range of geological possibilities. The impracticality of simulating so many models results in a limited exploration of geological uncertainties and development options. Consequently, the risk of improper system design cannot be properly assessed. This paper presents an approach to understand the trade-offs in maximizing heat extraction while minimizing energy usage in re-injection for a new geothermal reservoir development while considering the uncertainty from 18 different geological models. Our approach is computationally feasible because we apply multi-objective particle swarm optimization (MOPSO), to an ensemble of response surface models, built using Gaussian process regression (GPR), for each and every geological scenario. MOPSO explores the trade-off surface for the competing objectives using the mean reservoir responses (covering the geological uncertainty). Our results highlight the impact of geological uncertainty on the optimal well placement and show the need to consider geological uncertainties adequately in optimization. The work demonstrates the shortcomings of using only one geological model of a geothermal reservoir and/or a single objective in optimization. We additionally demonstrate the practicalities of using response surface models in this way for geothermal systems. We anticipate that our work raises awareness for the scope of optimization of geothermal reservoir design under geological uncertainty.

中文翻译：

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使用基于实验设计的代理模型在地热储层不确定性下进行多目标优化

摘要 如果在设计过程中能够克服相关的经济风险，地热能具有为更可持续的能源系统做出贡献的巨大潜力。深部地热储层（1000 m 以上）的开发规划依赖于计算机模型进行预测，然后优化系统设计。当所有目标的（例如 NPV）优化参数和最重要的地质被认为是已知的时，优化很容易，但情况几乎总是如此。在复杂的工程设计（例如井位）遇到重大地质不确定性的情况下，每个开发选项都应该使用针对地质可能性范围的昂贵模拟进行测试。模拟这么多模型的不切实际导致对地质不确定性和开发选项的探索有限。因此，无法正确评估系统设计不当的风险。本文提出了一种方法来理解在考虑 18 种不同地质模型的不确定性的同时，在最大限度地提取热量的同时最大限度地减少重新注入新地热储层开发中的能源使用的权衡。我们的方法在计算上是可行的，因为我们将多目标粒子群优化 (MOPSO) 应用于使用高斯过程回归 (GPR) 构建的响应面模型集合，适用于每个地质场景。MOPSO 使用平均储层响应（涵盖地质不确定性）探索竞争目标的权衡面。我们的结果突出了地质不确定性对最佳井位布置的影响，并表明在优化中需要充分考虑地质不确定性。这项工作证明了在优化中仅使用一个地热储层地质模型和/或单个目标的缺点。我们还展示了以这种方式将响应面模型用于地热系统的实用性。我们预计我们的工作将提高人们对地质不确定性下地热储层设计优化范围的认识。