English(EN) Efficient Geothermal Well-Control Optimization via Diffusion-Surrogate Reinforcement Learning

扩散模型通过强化学习加速地热井控优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一个新的框架，利用扩散模型-代理强化学习来优化地热井控制。该方法采用条件扩散模型创建代理环境，预测储层演化，从而显著减少了对计算成本高昂的高保真模拟的需求。该扩散代理与近端策略优化（PPO）相结合，在控制增强型地热系统（EGS）和降低运营风险方面表现出有竞争力的性能。 AI

影响这项研究展示了扩散模型如何为强化学习创建高效的代理环境，从而可能加速地热能源等复杂物理系统中的优化任务。

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arXiv cs.AI TIER_1 English(EN) · Ruimin Dai, Guodong Chen, Randy Harsuko, Kunpeng Liu, Nori Nakata · 2026-09-01 04:00

基于扩散代理强化学习的高效地热井控优化

arXiv:2608.28791v1 Announce Type: new Abstract: Real-time decision-making for enhanced geothermal systems (EGS) is challenging because long-term production periods involve high-dimensional control spaces and a large number of time-consuming high-fidelity hydrothermal simulations.…