Prediction of geothermal temperature field by multi-attribute neural network

IF 2.9 2区 地球科学 Q3 ENERGY & FUELS
Wanli Gao, Jingtao Zhao
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引用次数: 0

Abstract

Hot dry rock (HDR) resources are gaining increasing attention as a significant renewable resource due to their low carbon footprint and stable nature. When assessing the potential of a conventional geothermal resource, a temperature field distribution is a crucial factor. However, the available geostatistical and numerical simulations methods are often influenced by data coverage and human factors. In this study, the Convolution Block Attention Module (CBAM) and Bottleneck Architecture were integrated into UNet (CBAM-B-UNet) for simulating the geothermal temperature field. The proposed CBAM-B-UNet takes in a geological model containing parameters such as density, thermal conductivity, and specific heat capacity as input, and it simulates the temperature field by dynamically blending these multiple parameters through the neural network. The bottleneck architectures and CBAM can reduce the computational cost while ensuring accuracy in the simulation. The CBAM-B-UNet was trained using thousands of geological models with various real structures and their corresponding temperature fields. The method’s applicability was verified by employing a complex geological model of hot dry rock. In the final analysis, the simulated temperature field results are compared with the theoretical steady-state crustal ground temperature model of Gonghe Basin. The results indicated a small error between them, further validating the method's superiority. During the temperature field simulation, the thermal evolution law of a symmetrical cooling front formed by low thermal conductivity and high specific heat capacity in the center of the fault zone and on both sides of granite was revealed. The temperature gradually decreases from the center towards the edges.

利用多属性神经网络预测地热温度场
干热岩(HDR)资源作为一种重要的可再生资源,因其低碳足迹和稳定的性质而日益受到关注。在评估常规地热资源的潜力时,温度场分布是一个关键因素。然而,现有的地质统计和数值模拟方法往往受到数据覆盖范围和人为因素的影响。本研究将卷积块注意力模块(CBAM)和瓶颈结构集成到 UNet(CBAM-B-UNet)中,用于模拟地热温度场。拟议的 CBAM-B-UNet 将包含密度、热导率和比热容等参数的地质模型作为输入,并通过神经网络动态混合这些多参数来模拟温度场。瓶颈架构和 CBAM 既能降低计算成本,又能确保模拟的准确性。CBAM-B-UNet 利用数千个具有各种真实结构的地质模型及其相应的温度场进行了训练。通过使用复杂的干热岩地质模型,验证了该方法的适用性。在最后的分析中,模拟温度场结果与共和盆地理论稳态地壳地温模型进行了比较。结果表明二者误差很小,进一步验证了该方法的优越性。在温度场模拟过程中,揭示了断层带中心及两侧花岗岩由低导热系数和高比热容形成的对称冷却锋的热演化规律。温度由中心向边缘逐渐降低。
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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.
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