Reservoir production performance prediction model based on multi-parameter time series and particle swarm optimization algorithm

The research on reservoir performance prediction method is of great significance for the dynamic adjustment of reservoir development plan. The production prediction based on machine learning suffers from low accuracy and low time effectiveness, due to the absence of the parameter adjustment and optimization technology featuring the time series model and the dynamic update technology of prediction model by introducing new data, making it insufficient for practical applications. Through investigating the multi-parameter time series prediction method based on the long short-term memory (LSTM) neural network model and the particle swarm optimization (PSO) algorithm, the reservoir production performance prediction model that is dynamically updated with time was constructed. Application of the presented model to several reservoirs of the Changqing oilfield shows that the model exhibits a high accuracy and enables real-time training and automatic updating. The presented model is of high significant for practical application.