Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model

Oil production forecasting is one of the most critical issues during the exploitation phase of the oilfield. The limitations of traditional approaches make time-series production prediction still challenging. With the development of artificial intelligence, high-performance algorithms make reliable production prediction possible from the data perspective. This study proposes a Long Short-Term Memory (LSTM) neural network based model to infer the production of fractured horizontal wells in a volcanic reservoir, which addresses the limitations of traditional method and shows accurate predictions. The LSTM neural network enables to capture the dependencies of the oil rate time sequence data and incorporate the production constraints. Particle Swarm Optimization algorithm (PSO) is employed to optimize the essential configuration of the LSTM model. For evaluation purpose, two case studies are carried out with the production dynamics from synthetic model and from the Xinjiang oilfield, China. Towards a fair evaluation, the performance of the proposed approach is compared with traditional neural networks, time-series forecasting approaches, and conventional decline curves. The experiment results show that the proposed LSTM model outperforms other approaches. •The comprehensive data mining process is developed to forecast time-series well performance.•The PSO is utilized to optimize the structure of the LSTM neural network.•The deep learning algorithms is employed to predict daily oil rate.•The complex production variation of wells can be captured by the proposed model.