Reservoir production prediction with optimized artificial neural network and time series approaches

Numerical simulation of oil reservoirs is one of the most commonly used methods for reservoir production prediction, but its accuracy is based on accurate geological modeling and high-quality history matching. Therefore, numerical simulation is time-consuming and costly and requires extensive information. Traditional back-propagation neural networks and their improved algorithms are widely used for production prediction, but they are not suitable for time-series prediction problems. Based on variations in oil production, this study proposes a reservoir production prediction model based on a combined convolutional neural network (CNN) and a long short-term memory (LSTM) neural network model optimized by the particle swarm optimization (PSO) algorithm. First, the model extracts important temporal data features through the upper CNN, which is next imported to the lower LSTM network to further extract correlation features in the time dimension; then, it iteratively optimizes the key hyperparameters in the CNN-LSTM model through the PSO algorithm; finally, it uses the trained model for reservoir prediction. Compared with the training results of the LSTM neural network and CNN model, the PSO–CNN-LSTM model has higher prediction accuracy in time-series production prediction. Our proposed hybrid model is a data-driven method and is based on routinely available production data. Quick and accurate production prediction can lead to better informed operational decisions and optimization of recovery and economics. •This study proposes a production prediction model based on CNN-LSTM model optimized by PSO algorithm.•The hybrid CNN-LSTM model can better characterize spatial and temporal correlations than the single CNN or LSTM model..•The PSO algorithm can speed up the process of obtaining the optimal hyperparameters.•The PSO algorithm can optimize the architecture of any deep-learning model with challenges in the time-series problem.