Abstract:  Many of China’s main development blocks in oilfields have gradually entered a high water cut period， with complex underground oil reservoirs and progressively increasing water content， leading to a decline in oil production. Improving the accurate understanding of the current stage of oilfield development and production patterns is of significant importance for studying the dynamic changes in oilfield production and formulating oilfield development strategies. In view of the dynamic change law of oilfield production， this paper proposes a dynamic analysis model for reservoirs based on improved time-series capsule prediction method. Firstly， a bidirectional gated recurrent unit is applied to capture the timing features in the oilfield data to enhance the fitting ability of the model to timing information. Secondly， the primary temporal feature information is captured with a multi-headed attentional deep convolutional layer to efficiently extract long-range dependencies and complex feature representations of sequences. Finally， in the dynamic routing algorithm， attention mechanism is introduced to allow the higher-level capsules to better focus on important features， so as to improve the efficiency and accuracy of information transmission. To verify the validity of the model， the time-series data of the oil field is used as input to predict the daily oil production by improving the output of the capsule network model. The improved capsule network is compared with nine models such as ResNet， LeNet. The experimental results show that the improved capsule network has higher prediction accuracy， it can reach 94.5%.

Key words: capsule networks, bidirectional gated recurrent unit, dynamic routing algorithms, multi-headed attention, time-series forecasting, reservoir analysis model