Modeling unobserved geothermal structures using a physics-informed neural network with transfer learning of prior knowledge

IF 2.9 2区 地球科学 Q3 ENERGY & FUELS Geothermal Energy Pub Date : 2024-10-08 DOI:10.1186/s40517-024-00312-7
Akihiro Shima, Kazuya Ishitsuka, Weiren Lin, Elvar K. Bjarkason, Anna Suzuki
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Abstract

Deep learning has gained attention as a potentially powerful technique for modeling natural-state geothermal systems; however, its physical validity and prediction inaccuracy at extrapolation ranges are limiting. This study proposes the use of transfer learning in physics-informed neural networks to leverage prior expert knowledge at the target site and satisfy conservation laws for predicting natural-state quantities such as temperature, pressure, and permeability. A neural network pre-trained with multiple numerical datasets of natural-state geothermal systems was generated using numerical reservoir simulations based on uncertainties of the permeabilities, sizes, and locations of geological units. Observed well logs were then used for tuning by transfer learning of the network. Two synthetic datasets were examined using the proposed framework. Our results demonstrate that the use of transfer learning significantly improves the prediction accuracy in extrapolation regions with no observed wells.

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利用具有先验知识迁移学习功能的物理信息神经网络为未观测到的地热结构建模
深度学习作为一种对自然态地热系统进行建模的潜在强大技术,受到了广泛关注;然而,其物理有效性和外推范围的预测不准确性却受到了限制。本研究提出在物理信息神经网络中使用迁移学习,以利用目标地点的先验专家知识,并满足预测温度、压力和渗透率等自然状态量的守恒定律。根据地质单元的渗透率、大小和位置的不确定性,利用数值储层模拟生成了一个神经网络,该网络使用多个自然状态地热系统的数值数据集进行预训练。然后,通过网络的迁移学习,利用观测到的测井记录进行调整。使用所提出的框架对两个合成数据集进行了检验。结果表明,在没有观测井的外推法区域,使用迁移学习可以显著提高预测精度。
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来源期刊
Geothermal Energy
Geothermal Energy Earth and Planetary Sciences-Geotechnical Engineering and Engineering Geology
CiteScore
5.90
自引率
7.10%
发文量
25
审稿时长
8 weeks
期刊介绍: Geothermal Energy is a peer-reviewed fully open access journal published under the SpringerOpen brand. It focuses on fundamental and applied research needed to deploy technologies for developing and integrating geothermal energy as one key element in the future energy portfolio. Contributions include geological, geophysical, and geochemical studies; exploration of geothermal fields; reservoir characterization and modeling; development of productivity-enhancing methods; and approaches to achieve robust and economic plant operation. Geothermal Energy serves to examine the interaction of individual system components while taking the whole process into account, from the development of the reservoir to the economic provision of geothermal energy.
期刊最新文献
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