矿业科学学报2024，Vol.9Issue(4)：538-548,11.
基于PSO-LSTM模型的地热储层温度预测研究
Predicting geothermal reservoir temperature based on the PSO-LSTM model
摘要
Abstract
The temperature prediction of geothermal reservoirs at different depths is to determine the key parameters such as thermal energy storage,heat output capacity,and the sustainable utilization period of geothermal reservoirs.Taking the geothermal wells in the Qiabuqia area of Gonghe Basin as an example,this study proposes a temperature prediction model for heat reservoirs under different constraints based on particle swarm optimization(PSO)and long short-term memory network(LSTM).The prediction effect of this model is verified by comparing with those of the BP model and LSTM model.The results show that the RMSE value,MAPE value and MAD value in the prediction results of the model are the smallest compared with those in BP and LSTM models,and the minimum RMSE value is only 1.192.The determination coefficient of the model is 0.929,showing a good prediction effect.This indicates that this model could realize the prediction of reservoir temperature in geothermal system,which provides references for the efficient and long-term development of geothermal system.关键词
地热系统/粒子群优化算法/长短时记忆网络模型/温度预测Key words
geothermal system/PSO/LSTM/temperature prediction分类
能源科技引用本文复制引用
杨艺,赵惊涛,付国强..基于PSO-LSTM模型的地热储层温度预测研究[J].矿业科学学报,2024,9(4):538-548,11.基金项目
江苏省科技厅碳达峰碳中和科技创新专项(2022) （2022）
