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科技导报
| 专题：深部地热储层增产技 2022, 40(20): 93-100
基于机器学习的地热采灌方案优化方法
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王佳铖1 , 陈进帆1 , 赵志宏1 , 谭现锋2
作者信息
1. 清华大学土木工程系, 北京 100084;
通讯作者:
赵志宏(通信作者),副教授,研究方向为岩石力学与地下工程,电子信箱:zhzhao@mail.tsinghua.edu.cn
Optimizing development parameters of geothermal energy using machine learning technique
Affiliations
出版时间: 2022-10-28
doi: 10.3981/j.issn.1000-7857.2022.20.011
文章导航
为了解决基于数值模拟的优化方法，通常需要大量模拟计算的问题，在比较不同机器学习方法的预测性能后，建立了基于多层感知机的代理模型，以降低计算成本，然后将其与遗传算法相结合，提出了非均质地热储层中地热对井系统采灌方案优化方法。通过地热田对井系统的案例研究，证明了所开发的采灌方案优化方法的合理性和有效性。结果表明，基于代理模型的采灌方案优化方法能以更低的计算成本，准确地找到给定开采井位置时最优的回灌井位置、采灌量和尾水温度。
地热对井系统
/
机器学习
/
采灌方案优化
/
代理模型
/
遗传算法
In order to solve the problem that the simulation-based optimization method usually requires a large number of simulations, in this paper, after comparing the prediction performance of different machine learning methods, a surrogate model based on MLP was developed to reduce the computational cost, which was then combined with genetic algorithm to develop an optimization method of development parameters for geothermal doublets in heterogeneous geothermal reservoirs. Through the case study of a doublet system, the reasonability and efficiency of the developed optimization method of development parameters were demonstrated. The results show that when given a certain position of production well, the surrogate model-based optimization method of development parameters can accurately find the optimal placement of injection well, the rate of production and injection, and the temperature of recharge water with lower computational cost.
geothermal doublet system
/
machine learning
/
optimization of development parameters
/
surrogate models
/
genetic algorithm
王佳铖, 陈进帆, 赵志宏, 谭现锋.
基于机器学习的地热采灌方案优化方法.
科技导报,
2022
, 40
(20)
: 93
-100
.
DOI: 10.3981/j.issn.1000-7857.2022.20.011
WANG Jiacheng, CHEN Jinfan, ZHAO Zhihong, TAN Xianfeng.
Optimizing development parameters of geothermal energy using machine learning technique[J].
Science & Technology Review ,
2022
, 40
(20)
: 93
-100
.
DOI: 10.3981/j.issn.1000-7857.2022.20.011
2022年第40卷第20期
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文章信息
doi: 10.3981/j.issn.1000-7857.2022.20.011
接收时间：2022-08-31
首发时间：2022-11-15
出版时间：2022-10-28
收稿日期：2022-08-31
修回日期：2022-09-30
通讯作者:
赵志宏(通信作者),副教授,研究方向为岩石力学与地下工程,电子信箱:zhzhao@mail.tsinghua.edu.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2022.20.011
引用本文
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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