面向地热井阵列的大型语言模型决策支持与建模
Decision-Support and Modeling with Large Language Models for Geothermal Well Arrays
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中文总结 AI 辅助
本研究评估ChatGPT等前沿LLMs在地热领域的应用潜力，提出用NotebookLM加速生成地热基准，分析地热井阵列等技术的机遇，还展示了LLMs助力地热数值模型自动并行化的案例。
中文摘要 AI 辅助
将多口地热井组织成精心规划的几何配置的地热井阵列，为提升产能与增强容错能力提供了可能。大型语言模型（LLMs）和高级高性能语言的最新进展，可加速这类新兴地热技术的开发与应用。基于LLMs的应用面临的一个挑战是生成输出的可靠性，因为它们容易出现主观偏差和幻觉。本研究评估了前沿LLMs（如ChatGPT、Gemini、Claude、Grok，以及AskGDR等领域特定模型）作为专家助手的潜力，这类助手可对复杂地热数据进行有见地的解读，还能提升地热模型和数值软件的功能。我们开发了一种新方法，利用谷歌最新推出的AI助手NotebookLM来加速生成未公开的定量地热基准。这些评估工具的快速生成，对于评估新兴语言模型技术的快速发展能力至关重要。我们尤其利用这些基准和基于LLMs的访谈，分析地热井阵列与闭环同轴井这两种有前景技术的机遇与局限。此外，我们呈现了一个案例研究，展示LLMs如何促进地热数值模型的自动并行化。我们的分析强调了其在数字孪生中的应用，并凸显了高级高性能代码生成的重要性。该研究方向有望通过支持下一代决策支持应用，整合数据分析、知情建议和更动态的数值建模工作流，在地热领域发挥变革性作用。
英文摘要
Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide opportunities to enhance energy production capacity and increase fault tolerance. The development and adoption of these emerging geothermal technologies could be accelerated through the recent advances in large language models (LLMs) and high-level high-performance languages. A challenge in LLM-based applications is the reliability of the generated outputs, as they can be prone to subjective biases and hallucinations. This study assesses the potential of cutting-edge LLMs - such as ChatGPT, Gemini, Claude, Grok, and domain-specific models like AskGDR - as expert assistants that can synthesize insightful interpretations of complex geothermal data, as well as improve feature capabilities of geothermal models and numerical software. We developed a novel approach, leveraging Google's recently introduced AI assistant, NotebookLM, to accelerate the generation of unpublished quantitative geothermal benchmarks. The rapid generation of these evaluation instruments is essential for assessing the swiftly evolving capabilities of emerging language model technologies. In particular, we use these benchmarks and LLM-based interviews to analyze opportunities and limitations of two promising technologies: geothermal well arrays and closed-loop coaxial wells. Furthermore, we present a case study illustrating how LLMs can facilitate auto-parallelization of geothermal numerical models. Our analysis emphasizes their application in digital twins and underscores the importance of high-level, high-performance code generation. This line of research could play a transformative role in the geothermal sector by enabling the next-generation of decision-support applications, integrating data analysis, informed recommendations, and more dynamic numerical modeling workflows.
