The so-called“Hang's Mudding-off”technique is critical to keeping the dynamic pressure balance and guaranteeing the sufficient safe operation time in wellbore.However,for lack of dynamic mathematical model analy...The so-called“Hang's Mudding-off”technique is critical to keeping the dynamic pressure balance and guaranteeing the sufficient safe operation time in wellbore.However,for lack of dynamic mathematical model analysis methods for reflecting the changes of annulus liquid level in the borehole during the“Hang's Mudding-off”operation,the actual operation is basically conducted blindly without reasonable engineering basis.According to the actual conditions,a mathematical model for the safe time of the“Hang's Mudding-off”was,for the first time,built up by using the dynamic borehole leakage analysis method.Then,the integral results of leak-off rates were calculated.Finally,the calculation results were verified by field cases.It is shown that the calculation results are highly accordant with the actual data,indicating the reliability of the mathematical model.The safe operation time can be increased by increasing mud amount or reducing mud density appropriately.With this model,the safe time of“Hang's Mudding-off”operation can be calculated accurately.This research result is of great significance to avoiding well control risk of absorption wells,optimizing the“Hang's Mudding-off”technique and reducing project cost.展开更多
针对大数据环境下DCNN(deep convolutional neural network)算法中存在网络冗余参数过多、参数寻优能力不佳和并行效率低的问题,提出了大数据环境下基于特征图和并行计算熵的深度卷积神经网络算法MR-FPDCNN(deep convolutional neural n...针对大数据环境下DCNN(deep convolutional neural network)算法中存在网络冗余参数过多、参数寻优能力不佳和并行效率低的问题,提出了大数据环境下基于特征图和并行计算熵的深度卷积神经网络算法MR-FPDCNN(deep convolutional neural network algorithm based on feature graph and parallel computing entropy using MapReduce)。该算法设计了基于泰勒损失的特征图剪枝策略FMPTL(feature map pruning based on Taylor loss),预训练网络,获得压缩后的DCNN,有效减少了冗余参数,降低了DCNN训练的计算代价。提出了基于信息共享搜索策略ISS(information sharing strategy)的萤火虫优化算法IFAS(improved firefly algorithm based on ISS),根据“IFAS”算法初始化DCNN参数,实现DCNN的并行化训练,提高网络的寻优能力。在Reduce阶段提出了基于并行计算熵的动态负载均衡策略DLBPCE(dynamic load balancing strategy based on parallel computing entropy),获取全局训练结果,实现了数据的快速均匀分组,从而提高了集群的并行效率。实验结果表明,该算法不仅降低了DCNN在大数据环境下训练的计算代价,而且提高了并行系统的并行化性能。展开更多
文摘The so-called“Hang's Mudding-off”technique is critical to keeping the dynamic pressure balance and guaranteeing the sufficient safe operation time in wellbore.However,for lack of dynamic mathematical model analysis methods for reflecting the changes of annulus liquid level in the borehole during the“Hang's Mudding-off”operation,the actual operation is basically conducted blindly without reasonable engineering basis.According to the actual conditions,a mathematical model for the safe time of the“Hang's Mudding-off”was,for the first time,built up by using the dynamic borehole leakage analysis method.Then,the integral results of leak-off rates were calculated.Finally,the calculation results were verified by field cases.It is shown that the calculation results are highly accordant with the actual data,indicating the reliability of the mathematical model.The safe operation time can be increased by increasing mud amount or reducing mud density appropriately.With this model,the safe time of“Hang's Mudding-off”operation can be calculated accurately.This research result is of great significance to avoiding well control risk of absorption wells,optimizing the“Hang's Mudding-off”technique and reducing project cost.
文摘针对大数据环境下DCNN(deep convolutional neural network)算法中存在网络冗余参数过多、参数寻优能力不佳和并行效率低的问题,提出了大数据环境下基于特征图和并行计算熵的深度卷积神经网络算法MR-FPDCNN(deep convolutional neural network algorithm based on feature graph and parallel computing entropy using MapReduce)。该算法设计了基于泰勒损失的特征图剪枝策略FMPTL(feature map pruning based on Taylor loss),预训练网络,获得压缩后的DCNN,有效减少了冗余参数,降低了DCNN训练的计算代价。提出了基于信息共享搜索策略ISS(information sharing strategy)的萤火虫优化算法IFAS(improved firefly algorithm based on ISS),根据“IFAS”算法初始化DCNN参数,实现DCNN的并行化训练,提高网络的寻优能力。在Reduce阶段提出了基于并行计算熵的动态负载均衡策略DLBPCE(dynamic load balancing strategy based on parallel computing entropy),获取全局训练结果,实现了数据的快速均匀分组,从而提高了集群的并行效率。实验结果表明,该算法不仅降低了DCNN在大数据环境下训练的计算代价,而且提高了并行系统的并行化性能。
