The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs.Considering that the construction of ge...The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs.Considering that the construction of geological models with traditional numerical simulation software is complicated,the computational efficiency of the simulation calculation is often low,and the numerical simulation tools need to be repeated iteratively in the process of model optimization,machine learning methods have been used for fast reservoir simulation.However,traditional artificial neural network(ANN)has large degrees of freedom,slow convergence speed,and complex network model.This paper aims to predict the production performance of water flooding reservoirs based on a deep convolutional generative adversarial network(DC-GAN)model,and establish a dynamic mapping relationship between well location deployment and output oil saturation.The network structure is based on an improved U-Net framework.Through a deep convolutional network and deconvolution network,the features of input well deployment images are extracted,and the stability of the adversarial model is strengthened.The training speed and accuracy of the proxy model are improved,and the oil saturation of water flooding reservoirs is dynamically predicted.The results show that the trained DC-GAN has significant advantages in predicting oil saturation by the well-location employment map.The cosine similarity between the oil saturation map given by the trained DC-GAN and the oil saturation map generated by the numerical simulator is compared.In above,DC-GAN is an effective method to conduct a proxy model to quickly predict the production performance of water flooding reservoirs.展开更多
基于0.25μm GaN HEMT设计了一种工作于C波段、结构简单、宽带高效的E类功率放大器。针对单片微波集成电路(MMIC)功率放大器设计中射频扼流圈所占面积较大且难以实现的问题,采用有限元直流馈电电感替代扼流圈电感,抑制晶体管寄生参数C_(...基于0.25μm GaN HEMT设计了一种工作于C波段、结构简单、宽带高效的E类功率放大器。针对单片微波集成电路(MMIC)功率放大器设计中射频扼流圈所占面积较大且难以实现的问题,采用有限元直流馈电电感替代扼流圈电感,抑制晶体管寄生参数C_(ds)对最高工作频率的影响,并采用低Q值混合参数匹配网络,将功率放大器电路输入输出的最佳阻抗匹配到标准阻抗50Ω。版图后仿真结果表明,在4.1~4.9 GHz工作频段内,功率附加效率为51.309%~58.050%,平均增益大于11 dB,输出功率大于41 dBm。版图尺寸为2.7 mm×1.4 mm。展开更多
In spite of their extraordinary performance, AlGaN/GaN high electron mobility transistors （HEMTs） still lack solid reliability. Devices under accelerated DC stress tests （off-state, VDS = 0 state, and on-state step...In spite of their extraordinary performance, AlGaN/GaN high electron mobility transistors （HEMTs） still lack solid reliability. Devices under accelerated DC stress tests （off-state, VDS = 0 state, and on-state step-stress） are investigated to help us identify the degradation mechanisms of the AlGaN/GaN HEMTs. All our findings are consistent with the degradation mechanism based on crystallographic-defect formation due to the inverse piezoelectric effects in Ref. [1] （Joh J and del Alamo J A 2006 IEEE IDEM Tech. Digest p. 415）. However, under the on-state condition, the devices are suffering from both inverse piezoelectric effects and hot electron effects, and so to improve the reliability of the devices both effects should be taken into consideration.展开更多
基金supports from the National Natural Science Foundation of China(No.52104017)the Open Foundation of Cooperative Innovation Center of Unconventional Oil and Gas(Ministry of Education&Hubei Province)(No.UOG2022-14)the open fund of the State Center for Research and Development of Oil Shale Exploitation(33550000-21-ZC0611-0008).
文摘The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs.Considering that the construction of geological models with traditional numerical simulation software is complicated,the computational efficiency of the simulation calculation is often low,and the numerical simulation tools need to be repeated iteratively in the process of model optimization,machine learning methods have been used for fast reservoir simulation.However,traditional artificial neural network(ANN)has large degrees of freedom,slow convergence speed,and complex network model.This paper aims to predict the production performance of water flooding reservoirs based on a deep convolutional generative adversarial network(DC-GAN)model,and establish a dynamic mapping relationship between well location deployment and output oil saturation.The network structure is based on an improved U-Net framework.Through a deep convolutional network and deconvolution network,the features of input well deployment images are extracted,and the stability of the adversarial model is strengthened.The training speed and accuracy of the proxy model are improved,and the oil saturation of water flooding reservoirs is dynamically predicted.The results show that the trained DC-GAN has significant advantages in predicting oil saturation by the well-location employment map.The cosine similarity between the oil saturation map given by the trained DC-GAN and the oil saturation map generated by the numerical simulator is compared.In above,DC-GAN is an effective method to conduct a proxy model to quickly predict the production performance of water flooding reservoirs.
基金Project supported by the National Basic Research Program of China (Grant No. 2011CBA00600)the National Natural Science Foundation of China (Grant No. 61106106)the Fundamental Research Funds for the Central Universities (Grant No. K50510250006)
文摘In spite of their extraordinary performance, AlGaN/GaN high electron mobility transistors （HEMTs） still lack solid reliability. Devices under accelerated DC stress tests （off-state, VDS = 0 state, and on-state step-stress） are investigated to help us identify the degradation mechanisms of the AlGaN/GaN HEMTs. All our findings are consistent with the degradation mechanism based on crystallographic-defect formation due to the inverse piezoelectric effects in Ref. [1] （Joh J and del Alamo J A 2006 IEEE IDEM Tech. Digest p. 415）. However, under the on-state condition, the devices are suffering from both inverse piezoelectric effects and hot electron effects, and so to improve the reliability of the devices both effects should be taken into consideration.
