Abstract: The present disclosure provides a reinforcement learning-based decision optimization method of an oilfield production system, including: collecting dynamic production data of an oilfield production site to establish a data cube for reservoir production optimization; training a preset machine learning model based on the data cube to obtain a reinforcement learning-based reservoir injection-production system surrogate model configured to predict oil production according to the dynamic production data available on site; constructing an evaluation function for production optimization of a gas injection reservoir; establishing, during a process of production optimization, an enforced constraint model based on input parameters and a boundary constraint condition; and with the constraint model and the boundary constraint condition as constraints, the reservoir injection-production system surrogate model as a basis, and the evaluation function as an optimization direction, searching reservoir production optimization

Abstract: The present disclosure provides a method and device for predicting remaining oil distribution based on historical and predictive reservoir knowledge. The method includes: establishing complete data sets according to relevant historical dynamic and static monitor data of oil wells in a target reservoir; training and testing a long and short-term memory (LSTM) model using the complete data sets; predicting production of a single well at a preset moment by using the trained LSTM model; acquiring three-phase saturations of the single well at the preset moment based on the production predicting results and experience of reservoir experts; and according to the three-phase saturations of the single well, setting boundary conditions and physical constraints for the reservoir, and deriving an oil-gas-water three-phase saturation field at the preset moment using Kriging interpolation to obtain a prediction result of remaining oil distribution in the target reservoir.

Abstract: A technique of operating a wireless communication device includes selecting, from a primary sequence group that includes respective primary sequences, one of the respective primary sequences as a first portion of a cell identification (ID). In this case, the respective primary sequences are each associated with respective secondary sequence subgroups included in a secondary sequence group. Each of the respective secondary sequence subgroups include secondary sequences. One of the secondary sequences is selected (from one of the respective secondary sequence subgroups that is associated with the selected one of the respective primary sequences) for a second portion of the cell ID. At least some of the secondary sequences are only included in one of the respective secondary sequence subgroups. The first portion of the cell ID is encoded on a first downlink waveform that is to be transmitted and the second portion of the cell ID is encoded on a second downlink waveform that is to be transmitted.

Abstract: A technique of operating a wireless communication device includes selecting, from a primary sequence group that includes respective primary sequences, one of the respective primary sequences as a first portion of a cell identification (ID). In this case, the respective primary sequences are each associated with respective secondary sequence subgroups included in a secondary sequence group. Each of the respective secondary sequence subgroups include secondary sequences. One of the secondary sequences is selected (from one of the respective secondary sequence subgroups that is associated with the selected one of the respective primary sequences) for a second portion of the cell ID. At least some of the secondary sequences are only included in one of the respective secondary sequence subgroups. The first portion of the cell ID is encoded on a first downlink waveform that is to be transmitted and the second portion of the cell ID is encoded on a second downlink waveform that is to be transmitted.